添加技能模块

kb-bilibili-content/SKILL.md

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+# KB Bilibili Content Skill
+
+**kb-bilibili-content** - Download Bilibili videos and extract content.
+
+## Features
+
+- Video download using **yt-dlp** (recommended)
+- Subtitle extraction
+- Article content extraction
+- Multiple quality options (360P/480P/720P/1080P)
+- Supports `BILIBILI_COOKIE` environment variable
+
+## Environment Variables
+
+| Variable | Required | Description |
+|----------|----------|-------------|
+| `BILIBILI_COOKIE` | No | B站登录 Cookie (for member-only videos) |
+
+## Commands
+
+```bash
+# Download video (720P default)
+python bilibili_content.py --id "BV1xx411c7mD" --type video --output "/path/to/downloads/"
+
+# Download video (1080P)
+python bilibili_content.py --id "BV1xx411c7mD" --type video --quality 80 --output "/path/to/downloads/"
+
+# Download video (480P)
+python bilibili_content.py --id "BV1xx411c7mD" --type video --quality 32 --output "/path/to/downloads/"
+
+# Extract subtitle
+python bilibili_content.py --id "BV1xx411c7mD" --type subtitle --output "/path/to/subtitles/"
+
+# Get article content
+python bilibili_content.py --id "cv43780186" --type article --output "/path/to/article.md"
+```
+
+## Output
+
+```json
+{
+  "success": true,
+  "type": "video",
+  "filename": "video_title.mp4",
+  "filepath": "/path/to/downloads/video_title.mp4",
+  "message": "下载完成: video_title.mp4"
+}
+```
+
+## Cookie Setup
+
+### Get Cookie from Browser
+1. Login to bilibili.com
+2. Press F12 → Network → Refresh page
+3. Copy Cookie from request headers
+
+### Set Environment Variable
+```bash
+# Add to ~/.bashrc for persistence
+export BILIBILI_COOKIE="SESSDATA=xxx; bili_jct=xxx; DedeUserID=xxx; DedeUserID__ckMd5=xxx"
+
+# Or set temporarily
+export BILIBILI_COOKIE="your_cookie_here"
+```
+
+## Installation
+
+```bash
+cp -r kb-bilibili-content/ ~/skills/
+pip install aiohttp playwright
+playwright install chromium
+```
+
+## How It Works
+
+Video download uses **yt-dlp** with Netscape-format cookie file:
+1. Parses `BILIBILI_COOKIE` environment variable
+2. Creates temporary Netscape-format cookie file
+3. Runs `yt-dlp --cookies <file> -f <format> -o <output>`
+4. Cleans up temporary file
+
+## Quality Options
+
+| Quality | ID | Resolution | Notes |
+|---------|-----|------------|-------|
+| 16 | 360P | 640x360 | Fastest |
+| 32 | 480P | 852x480 | Default for low bandwidth |
+| 64 | 720P | 1280x720 | **Recommended** |
+| 80 | 1080P | 1920x1080 | Best quality |
+
+Default quality: **64 (720P)**

kb-bilibili-content/bilibili_content.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Bilibili Content Downloader - B站内容获取工具
+
+功能:
+- download_video(bvid, quality=480) - 下载视频（使用 yt-dlp）
+- extract_subtitle(bvid) - 提取字幕
+- extract_article(cvid) - 获取专栏全文（使用Playwright）
+- 命令行参数支持
+- JSON 输出
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import re
+import subprocess
+import sys
+import tempfile
+from datetime import datetime
+from pathlib import Path
+from typing import Optional, Dict, Any, List
+
+
+class BilibiliContent:
+    """B站内容获取器"""
+
+    def __init__(self, output_dir: str = "./output", cookie: str = None):
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        self.cookie = cookie
+
+    def _create_cookie_file(self) -> str:
+        """创建 Netscape 格式的 Cookie 文件"""
+        if not self.cookie:
+            return None
+
+        # 解析 Cookie 字符串
+        cookies = {}
+        for item in self.cookie.split(';'):
+            item = item.strip()
+            if '=' in item:
+                key, value = item.split('=', 1)
+                cookies[key.strip()] = value.strip()
+
+        # 创建 Netscape 格式的 Cookie 文件
+        cookie_content = """# Netscape HTTP Cookie File
+# Generated by bilibili_content.py
+
+"""
+
+        for name, value in cookies.items():
+            cookie_content += f".bilibili.com\tTRUE\t/\tFALSE\t0\t{name}\t{value}\n"
+
+        # 写入临时文件
+        fd, path = tempfile.mkstemp(suffix='.txt')
+        with os.fdopen(fd, 'w') as f:
+            f.write(cookie_content)
+
+        return path
+
+    async def download_video(self, bvid: str, quality: int = 64) -> Dict[str, Any]:
+        """下载B站视频（使用 yt-dlp）"""
+        result = {"success": False, "bvid": bvid, "message": "", "filepath": None, "metadata": {}}
+
+        try:
+            # 先获取视频信息
+            print(f"获取视频信息: {bvid}")
+
+            cookie_file = self._create_cookie_file()
+            url = f"https://www.bilibili.com/video/{bvid}"
+
+            try:
+                # 获取视频标题（用于文件名）
+                if cookie_file:
+                    cmd = ["yt-dlp", "--cookies", cookie_file, "--get-title", url]
+                else:
+                    cmd = ["yt-dlp", "--get-title", url]
+
+                proc = await asyncio.create_subprocess_exec(
+                    *cmd,
+                    stdout=asyncio.subprocess.PIPE,
+                    stderr=asyncio.subprocess.PIPE
+                )
+                stdout, stderr = await proc.communicate()
+
+                title = stdout.decode('utf-8').strip()
+                if not title or 'error' in title.lower():
+                    # 尝试备用方法
+                    title = f"video_{bvid}"
+            except Exception as e:
+                title = f"video_{bvid}"
+
+            # 构建 yt-dlp 命令
+            safe_title = "".join(c for c in title if c.isalnum() or c in (' ', '-', '_', '丨')).strip()[:50]
+
+            # 选择视频格式
+            if quality == 80:
+                # 1080P
+                format_str = "30112+30280"
+            elif quality == 64:
+                # 720P
+                format_str = "30064+30280"
+            elif quality == 32:
+                # 480P
+                format_str = "30032+30280"
+            else:
+                # 360P
+                format_str = "30016+30280"
+
+            print(f"下载视频: {title}")
+            print(f"格式: {format_str}")
+
+            output_template = str(self.output_dir / f"{safe_title}.%(ext)s")
+
+            cmd = [
+                "yt-dlp",
+                "-f", format_str,
+                "-o", output_template,
+                "--no-download-archive",
+                "-q",
+                url
+            ]
+
+            if cookie_file:
+                cmd.extend(["--cookies", cookie_file])
+
+            print(f"执行命令: {' '.join(cmd[:4])}...")
+
+            proc = await asyncio.create_subprocess_exec(
+                *cmd,
+                stdout=asyncio.subprocess.PIPE,
+                stderr=asyncio.subprocess.PIPE
+            )
+            stdout, stderr = await proc.communicate()
+
+            # 查找下载的文件
+            downloaded_files = list(self.output_dir.glob(f"{safe_title}.*"))
+            if downloaded_files:
+                video_file = downloaded_files[0]
+                result["success"] = True
+                result["filepath"] = str(video_file)
+                result["message"] = f"下载完成: {video_file.name}"
+                result["metadata"] = {"title": title, "bvid": bvid}
+                print(f"✅ {result['message']}")
+            else:
+                result["message"] = "未找到下载的文件"
+                print(f"❌ {result['message']}")
+
+            # 清理临时 Cookie 文件
+            if cookie_file and os.path.exists(cookie_file):
+                os.remove(cookie_file)
+
+        except Exception as e:
+            result["message"] = f"错误: {str(e)}"
+            print(f"❌ {result['message']}")
+
+        return result
+
+    async def extract_subtitle(self, bvid: str) -> Dict[str, Any]:
+        """
+        提取字幕
+        
+        注意：没有找到官方字幕是正常的！很多视频没有自动生成的字幕。
+        返回 success=True 即使没有字幕，让工作流继续执行后续步骤（如 Whisper）。
+        """
+        result = {"success": False, "bvid": bvid, "subtitles": [], "has_subtitle": False, "message": ""}
+
+        try:
+            import aiohttp
+
+            api_url = f"https://api.bilibili.com/x/player/pagelist?bvid={bvid}"
+
+            async with aiohttp.ClientSession() as session:
+                async with session.get(api_url) as resp:
+                    data = await resp.json()
+
+            pages = data.get('data', [])
+            if not pages:
+                result["message"] = "未找到分P（这是正常的）"
+                # 仍然返回 success=True，让工作流继续
+                result["success"] = True
+                return result
+
+            cid = pages[0].get('cid', '')
+
+            async with aiohttp.ClientSession() as session:
+                async with session.get(f"https://api.bilibili.com/x/player/v2?bvid={bvid}&cid={cid}") as resp:
+                    sub_data = await resp.json()
+
+            subtitles = sub_data.get('data', {}).get('subtitle', {}).get('list', [])
+
+            if not subtitles:
+                result["message"] = "没有官方字幕（这是正常的），将使用 Whisper 进行语音识别"
+                # 仍然返回 success=True，让工作流继续
+                result["success"] = True
+                return result
+
+            result["success"] = True
+            result["has_subtitle"] = True
+            result["subtitles"] = [{"lan": s.get('lan'), "title": s.get('lan_doc')} for s in subtitles]
+            result["message"] = f"✅ 找到 {len(subtitles)} 个字幕，将使用官方字幕进行深度分析"
+            print(result["message"])
+
+        except Exception as e:
+            result["message"] = f"获取字幕出错（这是正常的），将使用 Whisper: {str(e)}"
+            # 出错时也返回 success=True，让工作流继续
+            result["success"] = True
+
+        return result
+
+    async def download_subtitle(self, bvid: str) -> Dict[str, Any]:
+        """
+        下载字幕并转换为 SRT 格式
+        
+        如果没有官方字幕，返回 success=True 但 has_subtitle=False，
+        让工作流继续使用 Whisper 进行语音识别。
+        """
+        result = {"success": False, "bvid": bvid, "filepath": None, "has_subtitle": False, "message": ""}
+
+        try:
+            import aiohttp
+
+            # 获取 CID
+            api_url = f"https://api.bilibili.com/x/player/pagelist?bvid={bvid}"
+
+            async with aiohttp.ClientSession() as session:
+                async with session.get(api_url) as resp:
+                    data = await resp.json()
+
+            pages = data.get('data', [])
+            if not pages:
+                result["message"] = "未找到分P，将使用 Whisper 进行语音识别"
+                result["success"] = True
+                return result
+
+            cid = pages[0].get('cid', '')
+
+            # 获取字幕列表
+            async with aiohttp.ClientSession() as session:
+                async with session.get(f"https://api.bilibili.com/x/player/v2?bvid={bvid}&cid={cid}") as resp:
+                    sub_data = await resp.json()
+
+            subtitles = sub_data.get('data', {}).get('subtitle', {}).get('list', [])
+
+            if not subtitles:
+                result["message"] = "没有官方字幕，将使用 Whisper 进行语音识别"
+                result["success"] = True
+                return result
+
+            # 选择中文字幕，如果没有则选第一个
+            zh_sub = None
+            for s in subtitles:
+                if s.get('lan') in ('ai-zh', 'zh-CN', 'zh'):
+                    zh_sub = s
+                    break
+            if not zh_sub:
+                zh_sub = subtitles[0]
+
+            # 下载字幕文件
+            sub_url = zh_sub.get('subtitle_url', '')
+            if not sub_url:
+                result["message"] = "字幕URL为空，将使用 Whisper"
+                result["success"] = True
+                return result
+
+            if sub_url.startswith('//'):
+                sub_url = 'https:' + sub_url
+
+            async with aiohttp.ClientSession() as session:
+                async with session.get(sub_url) as resp:
+                    sub_content = await resp.json()
+
+            # 转换为 SRT 格式
+            body = sub_content.get('body', [])
+            if not body:
+                result["message"] = "字幕内容为空，将使用 Whisper"
+                result["success"] = True
+                return result
+
+            def format_srt_time(t: float) -> str:
+                """将秒数转换为 SRT 时间格式"""
+                h = int(t // 3600)
+                m = int((t % 3600) // 60)
+                s = int(t % 60)
+                ms = int((t - int(t)) * 1000)
+                return f"{h:02d}:{m:02d}:{s:02d},{ms:03d}"
+
+            srt_lines = []
+            for i, line in enumerate(body, 1):
+                start = line.get('from', 0)
+                end = line.get('to', 0)
+                content = line.get('content', '')
+                srt_lines.extend([str(i), f"{format_srt_time(start)} --> {format_srt_time(end)}", content, ""])
+
+            srt_content = "\n".join(srt_lines)
+
+            # 保存 SRT 文件
+            output_path = self.output_dir / f"{bvid}.srt"
+            with open(output_path, 'w', encoding='utf-8') as f:
+                f.write(srt_content)
+
+            result["success"] = True
+            result["has_subtitle"] = True
+            result["filepath"] = str(output_path)
+            result["message"] = f"✅ 官方字幕已下载: {output_path.name} ({len(body)}条)"
+            print(result["message"])
+
+        except Exception as e:
+            result["message"] = f"下载字幕出错: {str(e)}，将使用 Whisper"
+            result["success"] = True  # 仍然返回成功，让工作流继续
+
+        return result
+
+    async def extract_article(self, cvid: str) -> Dict[str, Any]:
+        """获取专栏全文（使用Playwright异步API）"""
+        result = {"success": False, "cvid": cvid, "title": "", "content": "", "message": ""}
+
+        try:
+            from playwright.async_api import async_playwright
+
+            cvid_clean = cvid.replace('cv', '')
+            url = f"https://www.bilibili.com/read/cv{cvid_clean}"
+            print(f"获取专栏: {url}")
+
+            async with async_playwright() as p:
+                browser = await p.chromium.launch(headless=True)
+                page = await browser.new_page()
+
+                await page.goto(url, wait_until="networkidle")
+                await page.wait_for_timeout(3000)
+
+                # 提取标题
+                title_elem = await page.query_selector('h1')
+                if title_elem:
+                    result["title"] = (await title_elem.inner_text()).strip()
+                    print(f"标题: {result['title'][:50]}...")
+
+                # 提取正文内容
+                content_elem = await page.query_selector('.read-article-holder')
+                if content_elem:
+                    html_content = await content_elem.inner_html()
+                    text_content = await content_elem.inner_text()
+                    result["content"] = text_content.strip()
+                    result["html_content"] = html_content.strip()
+                    result["success"] = bool(result["title"] and result["content"])
+                    print(f"内容长度: {len(result['content'])} 字符")
+                else:
+                    result["message"] = "未找到专栏内容"
+
+                await browser.close()
+
+            if result["success"]:
+                result["message"] = f"✅ 获取成功: {result['title'][:30]}..."
+            else:
+                result["message"] = f"获取失败: {result.get('message', '未知错误')}"
+
+        except Exception as e:
+            result["message"] = f"错误: {str(e)}"
+            print(f"错误: {e}")
+
+        return result
+
+
+async def main():
+    parser = argparse.ArgumentParser(description='Bilibili Content Downloader')
+    parser.add_argument('--id', '-i', required=True, help='B站内容ID')
+    parser.add_argument('--type', '-t', required=True, choices=['video', 'subtitle', 'subtitle-download', 'article'], help='内容类型')
+    parser.add_argument('--output', '-o', default='./output', help='输出目录')
+    parser.add_argument('--quality', '-q', type=int, default=64, help='视频质量: 16=360P, 32=480P, 64=720P, 80=1080P')
+
+    args = parser.parse_args()
+
+    cookie = os.environ.get('BILIBILI_COOKIE')
+    downloader = BilibiliContent(output_dir=args.output, cookie=cookie)
+
+    if args.type == 'video':
+        result = await downloader.download_video(args.id, quality=args.quality)
+    elif args.type == 'subtitle':
+        result = await downloader.extract_subtitle(args.id)
+    elif args.type == 'subtitle-download':
+        # 下载并转换字幕为 SRT 格式
+        result = await downloader.download_subtitle(args.id)
+    else:
+        result = await downloader.extract_article(args.id.replace('cv', ''))
+
+    # 统一输出格式
+    if result.get("success"):
+        output = {
+            "success": True,
+            "type": args.type,
+            "has_subtitle": result.get("has_subtitle", False),
+            "filename": Path(result.get('filepath', '')).name if result.get('filepath') else None,
+            "filepath": str(Path(result.get('filepath', '')).resolve()) if result.get('filepath') else None,
+            "message": result.get('message', '处理成功')
+        }
+    else:
+        output = {
+            "success": False,
+            "error": result.get('message', '处理失败'),
+            "detail": f"ID: {args.id}, 类型: {args.type}"
+        }
+
+    print(json.dumps(output, ensure_ascii=False, indent=2))
+    return result
+
+
+if __name__ == '__main__':
+    try:
+        result = asyncio.run(main())
+        sys.exit(0 if result.get("success") else 1)
+    except KeyboardInterrupt:
+        print(json.dumps({"success": False, "error": "用户取消"}, ensure_ascii=False, indent=2))
+        sys.exit(1)
+    except Exception as e:
+        print(json.dumps({"success": False, "error": str(e)}, ensure_ascii=False, indent=2))
+        sys.exit(1)

kb-bilibili-content/bilibili_extractor.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+B站视频提取脚本
+提取视频信息、统计数据、字幕并生成Markdown笔记
+"""
+
+import sys
+import os
+import json
+import re
+from datetime import datetime
+
+# 添加skills路径
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'skills', 'obsidian-kb'))
+
+from src.extractors.bilibili_extractor import BilibiliExtractor
+from src.config import get_config
+
+
+def format_number(num):
+    """格式化数字"""
+    if num >= 100000000:
+        return f"{num / 100000000:.1f}亿"
+    elif num >= 10000:
+        return f"{num / 10000:.1f}万"
+    else:
+        return f"{num:,}"
+
+
+def generate_markdown(result, bvid):
+    """生成Markdown笔记"""
+    metadata = result['metadata']
+    stats = result['stats']
+    description = result['description']
+    subtitle = result['subtitle']
+
+    # 获取当前时间
+    now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+
+    md = f"""# {metadata['title']}
+
+> **来源**: [B站](https://www.bilibili.com/video/{bvid})
+> **作者**: {metadata['author']}
+> **发布时间**: {metadata['pubtime']}
+> **提取时间**: {now}
+> **时长**: {metadata['duration']}
+
+## 📊 统计数据
+
+| 指标 | 数量 |
+|------|------|
+| 👀 播放 | {format_number(stats['view'])} |
+| 💬 弹幕 | {format_number(stats['danmaku'])} |
+| 📝 评论 | {format_number(stats['reply'])} |
+| ⭐ 收藏 | {format_number(stats['favorite'])} |
+| 🪙 投币 | {format_number(stats['coin'])} |
+| 🔗 分享 | {format_number(stats['share'])} |
+| 👍 点赞 | {format_number(stats['like'])} |
+
+## 📝 视频简介
+
+{description if description else '(暂无简介)'}
+
+## 📌 标签
+
+{metadata['tags'] if metadata['tags'] else '(无标签)'}
+
+## 🎬 字幕
+
+{subtitle if subtitle else '(无字幕)'}
+
+---
+
+**元数据**:
+- BV号: `{metadata['bvid']}`
+- AV号: `{metadata['aid']}`
+- UP主ID: `{metadata['mid']}`
+"""
+
+    return md
+
+
+def main():
+    """主函数"""
+    # BV号
+    bvid = "BV1N5foBjE5K"  # 从 https://b23.tv/btMGoOi 解析得到
+
+    print(f"开始提取B站视频: {bvid}")
+    print("=" * 60)
+
+    # 获取配置
+    try:
+        config = get_config()
+    except Exception as e:
+        print(f"⚠️  获取配置失败，使用默认配置: {e}")
+        config = None
+
+    # 创建提取器
+    extractor = BilibiliExtractor(config=config)
+
+    # 提取视频信息
+    try:
+        result = extractor.extract(bvid)
+
+        print("\n✅ 提取成功！\n")
+        print("=" * 60)
+        print("基本信息:")
+        print("=" * 60)
+        print(f"标题: {result['metadata']['title']}")
+        print(f"作者: {result['metadata']['author']}")
+        print(f"时长: {result['metadata']['duration']}")
+        print(f"发布: {result['metadata']['pubtime']}")
+
+        print("\n" + "=" * 60)
+        print("统计数据:")
+        print("=" * 60)
+        print(f"播放: {format_number(result['stats']['view'])}")
+        print(f"弹幕: {format_number(result['stats']['danmaku'])}")
+        print(f"评论: {format_number(result['stats']['reply'])}")
+        print(f"收藏: {format_number(result['stats']['favorite'])}")
+        print(f"投币: {format_number(result['stats']['coin'])}")
+        print(f"点赞: {format_number(result['stats']['like'])}")
+
+        # 生成 Markdown 笔记
+        md_content = generate_markdown(result, bvid)
+
+        # 确保 notes 目录存在
+        notes_dir = "/home/admin/.openclaw/workspace/notes"
+        os.makedirs(notes_dir, exist_ok=True)
+
+        # 生成文件名（使用BV号作为文件名）
+        filename = f"{bvid}.md"
+        filepath = os.path.join(notes_dir, filename)
+
+        # 写入文件
+        with open(filepath, 'w', encoding='utf-8') as f:
+            f.write(md_content)
+
+        print("\n" + "=" * 60)
+        print(f"✅ Markdown笔记已生成: {filepath}")
+        print("=" * 60)
+
+        # 返回笔记内容
+        return md_content
+
+    except Exception as e:
+        print(f"\n❌ 提取失败: {e}")
+        import traceback
+        traceback.print_exc()
+        return None
+
+
+if __name__ == '__main__':
+    main()

kb-bilibili-content/video_downloader.py

@@ -0,0 +1,175 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+使用旧版 API 获取并下载视频流
+"""
+import asyncio
+import json
+import os
+from playwright.async_api import async_playwright
+
+async def download_video_stream(bvid, output_dir="/tmp/bilibili_downloads"):
+    """下载视频流"""
+    url = f"https://www.bilibili.com/video/{bvid}"
+
+    # 创建输出目录
+    os.makedirs(output_dir, exist_ok=True)
+
+    async with async_playwright() as playwright:
+        browser = await playwright.chromium.launch(headless=True)
+        context = await browser.new_context()
+        page = await context.new_page()
+
+        print(f"正在下载视频: {url}")
+        print("=" * 80)
+
+        # 首先获取 aid 和 cid
+        print("\n1. 获取视频信息...")
+        try:
+            api_url = "https://api.bilibili.com/x/web-interface/view"
+            response = await page.request.get(api_url, params={'bvid': bvid})
+            data = await response.json()
+
+            if data.get('code') != 0:
+                print(f"   ❌ 失败: {data.get('message')}")
+                await browser.close()
+                return None
+
+            video_data = data.get('data', {})
+            title = video_data.get('title')
+            aid = video_data.get('aid')
+            cid = video_data.get('cid')
+            duration = video_data.get('duration')
+            print(f"   标题: {title}")
+            print(f"   时长: {duration}秒")
+        except Exception as e:
+            print(f"   ❌ 异常: {e}")
+            await browser.close()
+            return None
+
+        # 获取视频流 URL
+        print("\n2. 获取视频流 URL...")
+        try:
+            playurl_api = "https://api.bilibili.com/x/player/playurl"
+            params = {
+                'bvid': bvid,
+                'cid': cid,
+                'qn': 80,  # 1080P
+                'otype': 'json',
+                'fnver': 0,
+                'fnval': 0,  # FLV 格式
+            }
+
+            playurl_response = await page.request.get(playurl_api, params=params)
+
+            if not playurl_response.ok:
+                print(f"   ❌ HTTP 错误: {playurl_response.status}")
+                await browser.close()
+                return None
+
+            playurl_data = await playurl_response.json()
+
+            if playurl_data.get('code') != 0:
+                print(f"   ❌ API 错误: {playurl_data.get('message')}")
+                await browser.close()
+                return None
+
+            play_data = playurl_data.get('data', {})
+
+            # 检查支持的清晰度
+            print(f"\n   支持的清晰度:")
+            for i, (q, desc) in enumerate(zip(play_data.get('accept_quality', []), play_data.get('accept_description', []))):
+                current = " (当前)" if q == play_data.get('quality') else ""
+                print(f"      {desc} ({q}){current}")
+
+            # 获取视频 URL
+            durl = play_data.get('durl', [])
+            if not durl:
+                print(f"   ❌ 没有找到视频流")
+                await browser.close()
+                return None
+
+            print(f"\n   找到 {len(durl)} 个视频分段")
+            video_urls = []
+            for i, segment in enumerate(durl):
+                segment_url = segment.get('url')
+                segment_size = segment.get('size', 0)
+                video_urls.append(segment_url)
+                print(f"      分段 {i+1}: {segment_size / 1024 / 1024:.2f}MB")
+                print(f"         {segment_url[:100]}...")
+
+        except Exception as e:
+            print(f"   ❌ 异常: {e}")
+            import traceback
+            traceback.print_exc()
+            await browser.close()
+            return None
+
+        # 下载视频
+        print(f"\n3. 开始下载视频...")
+        all_segments = []
+        total_size = 0
+
+        for i, video_url in enumerate(video_urls, 1):
+            print(f"\n   下载分段 {i}/{len(video_urls)}...")
+
+            try:
+                # 添加必要的请求头
+                headers = {
+                    'Referer': 'https://www.bilibili.com/',
+                    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
+                }
+
+                # 下载分段
+                segment_response = await page.request.get(
+                    video_url,
+                    headers=headers,
+                    timeout=60000
+                )
+
+                if not segment_response.ok:
+                    print(f"      ❌ 下载失败: {segment_response.status}")
+                    continue
+
+                segment_data = await segment_response.body()
+                segment_size = len(segment_data)
+                total_size += segment_size
+                all_segments.append(segment_data)
+
+                print(f"      ✅ 下载成功: {segment_size / 1024 / 1024:.2f}MB")
+
+            except Exception as e:
+                print(f"      ❌ 下载异常: {e}")
+                continue
+
+        if not all_segments:
+            print(f"\n   ❌ 没有成功下载任何分段")
+            await browser.close()
+            return None
+
+        # 合并分段
+        print(f"\n4. 合并视频分段...")
+        video_data = b''.join(all_segments)
+
+        # 保存文件
+        safe_title = "".join(c for c in title if c.isalnum() or c in (' ', '-', '_')).strip()[:50]
+        output_path = os.path.join(output_dir, f"{safe_title}.flv")
+
+        with open(output_path, 'wb') as f:
+            f.write(video_data)
+
+        print(f"   ✅ 视频已保存: {output_path}")
+        print(f"   文件大小: {total_size / 1024 / 1024:.2f}MB")
+
+        await browser.close()
+
+        return output_path
+
+if __name__ == '__main__':
+    bvid = "BV1KtFzzqEUG"
+    result = asyncio.run(download_video_stream(bvid))
+    if result:
+        print(f"\n✅ 下载完成！")
+        print(f"文件路径: {result}")
+    else:
+        print(f"\n❌ 下载失败")

kb-bilibili-metadata/SKILL.md

@@ -0,0 +1,46 @@
+# KB Bilibili Metadata Skill
+
+**kb-bilibili-metadata** - Extract Bilibili video/article/up主 metadata.
+
+## Features
+
+- Video metadata extraction
+- Article (专栏) metadata extraction
+- UP主 information extraction
+- JSON output format
+- Playwright + HTTP API fallback
+
+## Commands
+
+```bash
+# Video metadata
+python bilibili_metadata.py --id "BV1xx411c7mD" --type video
+
+# Article metadata
+python bilibili_metadata.py --id "cv43780186" --type article
+
+# UP主 info
+python bilibili_metadata.py --id "7770724" --type up
+```
+
+## Output
+
+```json
+{
+  "success": true,
+  "data": {
+    "platform": "bilibili",
+    "type": "video",
+    "id": "BV1xx411c7mD",
+    "title": "视频标题",
+    "author": "UP主名字",
+    "stats": {"view": 100000, "like": 5000}
+  }
+}
+```
+
+## Installation
+
+```bash
+cp -r kb-bilibili-metadata/ ~/skills/
+```

kb-bilibili-metadata/__init__.py

@@ -0,0 +1,8 @@
+"""
+bilibili-metadata 技能包
+
+B站元数据提取工具
+"""
+
+__version__ = "1.0.0"
+__author__ = "OpenClaw"

kb-bilibili-metadata/bilibili_extractor.py

@@ -0,0 +1,666 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+B站元数据提取器
+
+独立版本，不依赖 obsidian-kb 结构
+支持视频、专栏、UP主信息提取
+"""
+
+import asyncio
+import json
+import logging
+import re
+from datetime import datetime
+from typing import Dict, Any, Optional
+from urllib.parse import urlparse
+
+logger = logging.getLogger(__name__)
+
+# 默认请求头
+DEFAULT_HEADERS = {
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
+    'Referer': 'https://www.bilibili.com/',
+    'Accept': 'application/json, text/plain, */*',
+    'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
+}
+
+try:
+    from playwright.async_api import async_playwright
+    PLAYWRIGHT_AVAILABLE = True
+except ImportError:
+    PLAYWRIGHT_AVAILABLE = False
+
+try:
+    from undetected_playwright import stealth_async
+    STEALTH_AVAILABLE = True
+except ImportError:
+    STEALTH_AVAILABLE = False
+
+try:
+    import httpx
+    HTTPX_AVAILABLE = True
+except ImportError:
+    HTTPX_AVAILABLE = False
+
+
+class BilibiliExtractor:
+    """
+    B站元数据提取器
+    
+    支持：
+    - 视频信息提取（标题、作者、统计数据、字幕等）
+    - 专栏文章提取
+    - UP主信息提取
+    """
+
+    # 正则模式
+    BV_PATTERN = re.compile(r'BV[a-zA-Z0-9]{10}')
+    CV_PATTERN = re.compile(r'cv(\d+)')
+    URL_PATTERN = re.compile(r'https?://www\.bilibili\.com/video/(BV[a-zA-Z0-9]{10})')
+    ARTICLE_URL_PATTERN = re.compile(r'https?://www\.bilibili\.com/read/(cv\d+)')
+    UP_URL_PATTERN = re.compile(r'https?://space\.bilibili\.com/(\d+)')
+
+    def __init__(self, headless: bool = True, timeout: int = 30):
+        """
+        初始化提取器
+        
+        Args:
+            headless: 是否使用无头模式（浏览器）
+            timeout: 超时时间（秒）
+        """
+        self.headless = headless
+        self.timeout = timeout * 1000  # 转换为毫秒
+
+    def extract_video_metadata(self, bvid: str) -> Dict[str, Any]:
+        """
+        提取视频元数据
+        
+        Args:
+            bvid: B站视频BV号（如 BV1xx411c7mD）
+            
+        Returns:
+            Dict containing metadata, stats, description, subtitle
+        """
+        # 验证并解析BV号
+        bvid = self._resolve_bvid(bvid)
+        if not bvid:
+            raise ValueError(f"无效的BV号: {bvid}")
+
+        # 使用异步方法提取
+        return asyncio.run(self._extract_video_async(bvid))
+
+    def extract_article_metadata(self, cvid: str) -> Dict[str, Any]:
+        """
+        提取专栏元数据
+        
+        Args:
+            cvid: B站专栏CV号（如 cv43780186）
+            
+        Returns:
+            Dict containing article metadata
+        """
+        # 解析CV号
+        cvid = self._resolve_cvid(cvid)
+        if not cvid:
+            raise ValueError(f"无效的CV号: {cvid}")
+
+        return asyncio.run(self._extract_article_async(cvid))
+
+    def extract_up_metadata(self, mid: str) -> Dict[str, Any]:
+        """
+        提取UP主信息
+        
+        Args:
+            mid: B站用户ID（如 7770724）
+            
+        Returns:
+            Dict containing UP主信息
+        """
+        # 解析MID
+        mid = self._resolve_mid(mid)
+        if not mid:
+            raise ValueError(f"无效的MID: {mid}")
+
+        return asyncio.run(self._extract_up_async(mid))
+
+    def _resolve_bvid(self, source_id: str) -> Optional[str]:
+        """解析并返回BV号"""
+        source = source_id.strip()
+        
+        # 直接BV号
+        if self.BV_PATTERN.fullmatch(source):
+            return source
+        
+        # 完整URL
+        match = self.URL_PATTERN.match(source)
+        if match:
+            return match.group(1)
+        
+        return None
+
+    def _resolve_cvid(self, source_id: str) -> Optional[str]:
+        """解析并返回CV号"""
+        source = source_id.strip()
+        
+        # 直接CV号（带cv前缀）
+        if source.startswith('cv'):
+            match = self.CV_PATTERN.match(source)
+            if match:
+                return source
+        
+        # 纯数字
+        if source.isdigit():
+            return f"cv{source}"
+        
+        # 完整URL
+        match = self.ARTICLE_URL_PATTERN.match(source)
+        if match:
+            return match.group(1)
+        
+        return None
+
+    def _resolve_mid(self, source_id: str) -> Optional[str]:
+        """解析并返回MID"""
+        source = source_id.strip()
+        
+        # 纯数字
+        if source.isdigit():
+            return source
+        
+        # 完整URL
+        match = self.UP_URL_PATTERN.match(source)
+        if match:
+            return match.group(1)
+        
+        return None
+
+    async def _extract_video_async(self, bvid: str) -> Dict[str, Any]:
+        """异步提取视频信息"""
+        url = f"https://www.bilibili.com/video/{bvid}"
+        result = {
+            'metadata': {},
+            'stats': {},
+            'subtitle': '',
+            'description': ''
+        }
+
+        if not PLAYWRIGHT_AVAILABLE:
+            # 使用 HTTP API
+            return await self._extract_video_by_api(bvid)
+
+        async with async_playwright() as playwright:
+            try:
+                browser = await playwright.chromium.launch(
+                    headless=self.headless,
+                    args=[
+                        '--no-sandbox',
+                        '--disable-setuid-sandbox',
+                        '--disable-blink-features=AutomationControlled',
+                        '--disable-dev-shm-usage',
+                    ]
+                )
+
+                context = await browser.new_context(
+                    viewport={'width': 1920, 'height': 1080},
+                    user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
+                    locale='zh-CN',
+                )
+
+                if STEALTH_AVAILABLE:
+                    await stealth_async(context)
+
+                page = await context.new_page()
+                await page.goto(url, wait_until='domcontentloaded', timeout=self.timeout)
+                await asyncio.sleep(2)
+
+                content = await page.content()
+                video_data = await self._try_extract_video_methods(page, content, bvid)
+
+                if video_data:
+                    result['metadata'] = {
+                        'title': video_data.get('title', ''),
+                        'author': video_data.get('author', ''),
+                        'bvid': video_data.get('bvid', ''),
+                        'aid': video_data.get('aid', ''),
+                        'mid': video_data.get('mid', ''),
+                        'pubtime': video_data.get('pubtime', ''),
+                        'duration': video_data.get('duration', ''),
+                        'url': url,
+                        'tags': video_data.get('tags', ''),
+                    }
+                    result['stats'] = {
+                        'view': video_data.get('view', 0),
+                        'danmaku': video_data.get('danmaku', 0),
+                        'reply': video_data.get('reply', 0),
+                        'favorite': video_data.get('favorite', 0),
+                        'coin': video_data.get('coin', 0),
+                        'share': video_data.get('share', 0),
+                        'like': video_data.get('like', 0),
+                    }
+                    result['description'] = video_data.get('desc', '')
+
+                await browser.close()
+
+            except Exception as e:
+                logger.error(f"视频提取错误: {e}")
+                # 回退到 API
+                return await self._extract_video_by_api(bvid)
+
+        return result
+
+    async def _extract_video_by_api(self, bvid: str) -> Dict[str, Any]:
+        """使用B站API提取视频信息"""
+        result = {
+            'metadata': {},
+            'stats': {},
+            'subtitle': '',
+            'description': ''
+        }
+
+        try:
+            async with httpx.AsyncClient(timeout=30.0, headers=DEFAULT_HEADERS) as client:
+                # 获取视频信息
+                response = await client.get('https://api.bilibili.com/x/web-interface/view', params={'bvid': bvid})
+                if response.status_code == 200:
+                    data = response.json()
+                    if data.get('code') == 0:
+                        video_data = data.get('data', {})
+                        
+                        result['metadata'] = {
+                            'title': video_data.get('title', ''),
+                            'author': video_data.get('owner', {}).get('name', ''),
+                            'bvid': video_data.get('bvid', ''),
+                            'aid': video_data.get('aid', ''),
+                            'mid': video_data.get('owner', {}).get('mid', ''),
+                            'pubtime': datetime.fromtimestamp(video_data.get('pubdate', 0)).strftime('%Y-%m-%d %H:%M:%S') if video_data.get('pubdate') else '',
+                            'duration': self._format_duration(video_data.get('duration', 0)),
+                            'url': f"https://www.bilibili.com/video/{bvid}",
+                            'tags': ', '.join([t.get('tag_name', '') for t in video_data.get('tag', [])]),
+                        }
+                        
+                        stat = video_data.get('stat', {})
+                        result['stats'] = {
+                            'view': stat.get('view', 0),
+                            'danmaku': stat.get('danmaku', 0),
+                            'reply': stat.get('reply', 0),
+                            'favorite': stat.get('favorite', 0),
+                            'coin': stat.get('coin', 0),
+                            'share': stat.get('share', 0),
+                            'like': stat.get('like', 0),
+                        }
+                        result['description'] = video_data.get('desc', '')
+
+        except Exception as e:
+            logger.error(f"API提取错误: {e}")
+
+        return result
+
+    async def _try_extract_video_methods(self, page, content: str, bvid: str) -> Dict[str, Any]:
+        """尝试多种方法提取视频信息"""
+        video_data = {}
+
+        # 方法1: 从 __INITIAL_STATE__
+        try:
+            initial_state = await page.evaluate('() => typeof window.__INITIAL_STATE__ !== "undefined" ? window.__INITIAL_STATE__ : null')
+            if initial_state:
+                video_data = self._parse_video_state(initial_state)
+                if video_data and video_data.get('title'):
+                    return video_data
+        except:
+            pass
+
+        # 方法2: 使用 API（更可靠）
+        return await self._extract_video_by_api(bvid)
+
+    def _parse_video_state(self, data: Dict) -> Dict[str, Any]:
+        """解析 __INITIAL_STATE__ 数据"""
+        video_data = {}
+        try:
+            video = data.get('videoData', {})
+            video_data['title'] = video.get('title', '')
+            video_data['bvid'] = video.get('bvid', '')
+            video_data['aid'] = video.get('aid', '')
+            owner = video.get('owner', {})
+            video_data['author'] = owner.get('name', '')
+            video_data['mid'] = owner.get('mid', '')
+            stat = video.get('stat', {})
+            video_data['view'] = stat.get('view', 0)
+            video_data['danmaku'] = stat.get('danmaku', 0)
+            video_data['reply'] = stat.get('reply', 0)
+            video_data['favorite'] = stat.get('favorite', 0)
+            video_data['coin'] = stat.get('coin', 0)
+            video_data['share'] = stat.get('share', 0)
+            video_data['like'] = stat.get('like', 0)
+            pubdate = video.get('pubdate', 0)
+            video_data['pubtime'] = datetime.fromtimestamp(pubdate).strftime('%Y-%m-%d %H:%M:%S') if pubdate else ''
+            video_data['duration'] = self._format_duration(video.get('duration', 0))
+            video_data['desc'] = video.get('desc', '')
+            video_data['tags'] = ', '.join([t.get('tag_name', '') for t in video.get('tag', [])])
+        except:
+            pass
+        return video_data
+
+    async def _extract_article_async(self, cvid: str) -> Dict[str, Any]:
+        """异步提取专栏信息"""
+        result = {}
+
+        if not HTTPX_AVAILABLE:
+            return {'error': 'httpx 未安装'}
+
+        try:
+            async with httpx.AsyncClient(timeout=30.0, headers=DEFAULT_HEADERS) as client:
+                # 获取专栏信息
+                response = await client.get('https://api.bilibili.com/x/article/viewinfo', params={'id': cvid[2:]})
+                if response.status_code == 200:
+                    data = response.json()
+                    if data.get('code') == 0:
+                        article = data.get('data', {})
+                        result = {
+                            'cvid': cvid,
+                            'title': article.get('title', ''),
+                            'author': article.get('author_name', ''),
+                            'mid': article.get('author_mid', ''),
+                            'publish_time': datetime.fromtimestamp(article.get('publish_time', 0)).strftime('%Y-%m-%d %H:%M:%S') if article.get('publish_time') else '',
+                            'view_count': article.get('stats', {}).get('view', 0),
+                            'like_count': article.get('stats', {}).get('like', 0),
+                            'coin_count': article.get('stats', {}).get('coin', 0),
+                            'share_count': article.get('stats', {}).get('share', 0),
+                            'reply_count': article.get('stats', {}).get('reply', 0),
+                            'url': f"https://www.bilibili.com/read/{cvid}",
+                            'summary': article.get('summary', ''),
+                            'word_count': article.get('words', 0),
+                        }
+        except Exception as e:
+            logger.error(f"专栏提取错误: {e}")
+            result = {'error': str(e)}
+
+        return result
+
+    async def _extract_up_async(self, mid: str) -> Dict[str, Any]:
+        """
+        异步提取UP主信息（支持订阅跟踪）
+        
+        Args:
+            mid: UP主ID
+            
+        Returns:
+            Dict containing UP主完整信息
+        """
+        result = {
+            'type': 'up',
+            'mid': mid,
+            'subscribed': False,
+            'basic_info': {},
+            'subscription_info': {}
+        }
+
+        if not HTTPX_AVAILABLE:
+            return {'error': 'httpx 未安装'}
+
+        try:
+            async with httpx.AsyncClient(timeout=30.0, headers=DEFAULT_HEADERS) as client:
+                # 获取UP主基本信息
+                response = await client.get('https://api.bilibili.com/x/space/acc/info', params={'mid': mid})
+                if response.status_code == 200:
+                    data = response.json()
+                    if data.get('code') == 0:
+                        up = data.get('data', {})
+                        result['basic_info'] = {
+                            'name': up.get('name', ''),
+                            'sex': up.get('sex', ''),
+                            'face': up.get('face', ''),
+                            'sign': up.get('sign', ''),
+                            'level': up.get('level', 0),
+                            'vip_type': up.get('vip', {}).get('type', 0),
+                            'vip_status': up.get('vip', {}).get('status', 0),
+                            'following_count': up.get('following', 0),
+                            'follower_count': up.get('follower', 0),
+                            'url': f"https://space.bilibili.com/{mid}",
+                        }
+                        
+                # 获取UP主视频统计
+                stats_response = await client.get('https://api.bilibili.com/x/space/navnum', params={'mid': mid})
+                if stats_response.status_code == 200:
+                    stats_data = stats_response.json()
+                    if stats_data.get('code') == 0:
+                        result['basic_info']['video_count'] = stats_data.get('data', {}).get('video', 0)
+                
+                # 获取UP主专栏数量
+                article_response = await client.get('https://api.bilibili.com/x/space/article', params={'mid': mid, 'pn': 1, 'ps': 1})
+                if article_response.status_code == 200:
+                    article_data = article_response.json()
+                    if article_data.get('code') == 0:
+                        result['basic_info']['article_count'] = article_data.get('data', {}).get('count', 0)
+                        
+                # 获取UP主最近视频（用于跟踪更新）
+                video_response = await client.get('https://api.bilibili.com/x/space/arc/search', 
+                    params={'mid': mid, 'pn': 1, 'ps': 1, 'order': 'pubtime'})
+                if video_response.status_code == 200:
+                    video_data = video_response.json()
+                    if video_data.get('code') == 0:
+                        videos = video_data.get('data', {}).get('list', {}).get('vlist', [])
+                        if videos:
+                            latest_video = videos[0]
+                            result['subscription_info'] = {
+                                'last_video_id': latest_video.get('bvid', ''),
+                                'last_video_title': latest_video.get('title', ''),
+                                'last_video_time': datetime.fromtimestamp(latest_video.get('created', 0)).strftime('%Y-%m-%d %H:%M:%S') if latest_video.get('created') else '',
+                            }
+                
+                # 获取UP主最近专栏（用于跟踪更新）
+                article_response = await client.get('https://api.bilibili.com/x/space/article', 
+                    params={'mid': mid, 'pn': 1, 'ps': 1, 'sort': 'publish_time'})
+                if article_response.status_code == 200:
+                    article_data = article_response.json()
+                    if article_data.get('code') == 0:
+                        articles = article_data.get('data', {}).get('articles', [])
+                        if articles:
+                            latest_article = articles[0]
+                            if 'last_article' not in result['subscription_info']:
+                                result['subscription_info']['last_article'] = {}
+                            result['subscription_info']['last_article'] = {
+                                'id': latest_article.get('id', 0),
+                                'title': latest_article.get('title', ''),
+                                'time': datetime.fromtimestamp(latest_article.get('publish_time', 0)).strftime('%Y-%m-%d %H:%M:%S') if latest_article.get('publish_time') else '',
+                            }
+                        
+        except Exception as e:
+            logger.error(f"UP主提取错误: {e}")
+            result = {'error': str(e)}
+
+        return result
+
+    def _format_duration(self, seconds: int) -> str:
+        """格式化时长（秒 -> HH:MM:SS）"""
+        if not seconds:
+            return ''
+        hours = seconds // 3600
+        minutes = (seconds % 3600) // 60
+        secs = seconds % 60
+        if hours > 0:
+            return f"{hours:02d}:{minutes:02d}:{secs:02d}"
+        else:
+            return f"{minutes:02d}:{secs:02d}"
+
+    async def _extract_series_async(self, mid: str, series_id: str) -> Dict[str, Any]:
+        """
+        异步提取系列合集信息（不包含列表内容）
+        
+        Args:
+            mid: UP主ID
+            series_id: 系列ID
+            
+        Returns:
+            Dict containing series basic info
+        """
+        result = {
+            'type': 'series',
+            'series_id': series_id,
+            'mid': mid,
+            'subscribed': False,
+            'basic_info': {}
+        }
+
+        if not HTTPX_AVAILABLE:
+            return {'error': 'httpx 未安装'}
+
+        try:
+            async with httpx.AsyncClient(timeout=30.0, headers=DEFAULT_HEADERS) as client:
+                # 获取系列信息
+                response = await client.get('https://api.bilibili.com/x/space/series', 
+                    params={'mid': mid, 'series_id': series_id, 'pn': 1, 'ps': 1})
+                if response.status_code == 200:
+                    data = response.json()
+                    if data.get('code') == 0:
+                        series_info = data.get('data', {}).get('list', {})
+                        result['basic_info'] = {
+                            'title': series_info.get('title', ''),
+                            'description': series_info.get('desc', ''),
+                            'cover': series_info.get('cover', ''),
+                            'mid': mid,
+                            'creator_name': '',
+                            'total_count': series_info.get('cnt', 0),
+                            'last_updated': '',
+                            'update_count': 0
+                        }
+                        
+                        # 获取创作者信息
+                        up_info = await client.get('https://api.bilibili.com/x/space/acc/info', 
+                            params={'mid': mid})
+                        if up_info.status_code == 200:
+                            up_data = up_info.json()
+                            if up_data.get('code') == 0:
+                                result['basic_info']['creator_name'] = up_data.get('data', {}).get('name', '')
+
+        except Exception as e:
+            logger.error(f"系列合集提取错误: {e}")
+            result = {'error': str(e)}
+
+        return result
+
+    async def _extract_readlist_async(self, rl_id: str) -> Dict[str, Any]:
+        """
+        异步提取专栏合集信息（不包含列表内容）
+        
+        Args:
+            rl_id: 专栏合集ID (rl开头)
+            
+        Returns:
+            Dict containing readlist basic info
+        """
+        result = {
+            'type': 'readlist',
+            'readlist_id': rl_id,
+            'subscribed': False,
+            'basic_info': {}
+        }
+
+        if not HTTPX_AVAILABLE:
+            return {'error': 'httpx 未安装'}
+
+        try:
+            async with httpx.AsyncClient(timeout=30.0, headers=DEFAULT_HEADERS) as client:
+                # 获取专栏合集信息
+                response = await client.get('https://api.bilibili.com/x/article/readlist', 
+                    params={'id': rl_id.replace('rl', '')})
+                if response.status_code == 200:
+                    data = response.json()
+                    if data.get('code') == 0:
+                        list_info = data.get('data', {})
+                        result['basic_info'] = {
+                            'title': list_info.get('name', ''),
+                            'description': list_info.get('description', ''),
+                            'cover': list_info.get('cover', ''),
+                            'mid': list_info.get('mid', ''),
+                            'creator_name': '',
+                            'total_count': list_info.get('articles_count', 0),
+                            'last_updated': '',
+                            'is_public': list_info.get('public', True)
+                        }
+                        
+                        # 获取创作者信息
+                        mid = list_info.get('mid', '')
+                        if mid:
+                            up_info = await client.get('https://api.bilibili.com/x/space/acc/info', 
+                                params={'mid': mid})
+                            if up_info.status_code == 200:
+                                up_data = up_info.json()
+                                if up_data.get('code') == 0:
+                                    result['basic_info']['creator_name'] = up_data.get('data', {}).get('name', '')
+
+        except Exception as e:
+            logger.error(f"专栏合集提取错误: {e}")
+            result = {'error': str(e)}
+
+        return result
+
+    async def _extract_favlist_async(self, fid: str) -> Dict[str, Any]:
+        """
+        异步提取收藏夹信息（不包含列表内容）
+        
+        Args:
+            fid: 收藏夹ID
+            
+        Returns:
+            Dict containing favlist basic info
+        """
+        result = {
+            'type': 'favlist',
+            'favlist_id': fid,
+            'subscribed': False,
+            'basic_info': {}
+        }
+
+        if not HTTPX_AVAILABLE:
+            return {'error': 'httpx 未安装'}
+
+        try:
+            async with httpx.AsyncClient(timeout=30.0, headers=DEFAULT_HEADERS) as client:
+                # 获取收藏夹信息
+                response = await client.get('https://api.bilibili.com/x/space/favlist', 
+                    params={'fid': fid, 'pn': 1, 'ps': 1})
+                if response.status_code == 200:
+                    data = response.json()
+                    if data.get('code') == 0:
+                        fav_info = data.get('data', {})
+                        result['basic_info'] = {
+                            'title': fav_info.get('title', ''),
+                            'description': fav_info.get('intro', ''),
+                            'cover': fav_info.get('cover', ''),
+                            'mid': fav_info.get('mid', ''),
+                            'creator_name': '',
+                            'total_count': fav_info.get('media_count', 0),
+                            'is_public': fav_info.get('public', True),
+                            'like_count': fav_info.get('like', 0)
+                        }
+                        
+                        # 获取创作者信息
+                        mid = fav_info.get('mid', '')
+                        if mid:
+                            up_info = await client.get('https://api.bilibili.com/x/space/acc/info', 
+                                params={'mid': mid})
+                            if up_info.status_code == 200:
+                                up_data = up_info.json()
+                                if up_data.get('code') == 0:
+                                    result['basic_info']['creator_name'] = up_data.get('data', {}).get('name', '')
+
+        except Exception as e:
+            logger.error(f"收藏夹提取错误: {e}")
+            result = {'error': str(e)}
+
+        return result
+
+
+# 测试代码
+if __name__ == '__main__':
+    extractor = BilibiliExtractor()
+    
+    print("=" * 60)
+    print("B站元数据提取器测试")
+    print("=" * 60)
+    
+    # 测试视频
+    print("\n测试视频提取: BV1MnFNz5EDL")
+    result = extractor.extract_video_metadata('BV1MnFNz5EDL')
+    print(json.dumps(result, ensure_ascii=False, indent=2))

kb-bilibili-metadata/bilibili_metadata.py

@@ -0,0 +1,228 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+B站元数据提取 CLI 工具
+
+支持命令行调用，输出 JSON 格式数据
+"""
+
+import argparse
+import asyncio
+import json
+import os
+import sys
+from pathlib import Path
+from typing import Dict, Any
+
+from bilibili_extractor import BilibiliExtractor
+
+
+def format_number(num: int) -> str:
+    """格式化数字"""
+    if num >= 100000000:
+        return f"{num / 100000000:.1f}亿"
+    elif num >= 10000:
+        return f"{num / 10000:.1f}万"
+    else:
+        return f"{num:,}"
+
+
+def extract_video(extractor: BilibiliExtractor, bvid: str, output_file: str = None) -> Dict[str, Any]:
+    """提取视频元数据"""
+    result = extractor.extract_video_metadata(bvid)
+    
+    # 如果需要输出到文件
+    if output_file:
+        with open(output_file, 'w', encoding='utf-8') as f:
+            json.dump(result, f, ensure_ascii=False, indent=2)
+    
+    return result
+
+
+def extract_article(extractor: BilibiliExtractor, cvid: str, output_file: str = None) -> Dict[str, Any]:
+    """提取专栏元数据"""
+    result = extractor.extract_article_metadata(cvid)
+    
+    if output_file:
+        with open(output_file, 'w', encoding='utf-8') as f:
+            json.dump(result, f, ensure_ascii=False, indent=2)
+    
+    return result
+
+
+def extract_up(extractor: BilibiliExtractor, mid: str, output_file: str = None) -> Dict[str, Any]:
+    """提取UP主信息（包含订阅跟踪所需信息）"""
+    result = asyncio.run(extractor._extract_up_async(mid))
+    
+    if output_file:
+        with open(output_file, 'w', encoding='utf-8') as f:
+            json.dump(result, f, ensure_ascii=False, indent=2)
+    
+    return result
+
+
+def extract_series(extractor: BilibiliExtractor, mid: str, series_id: str, output_file: str = None) -> Dict[str, Any]:
+    """提取系列合集元数据"""
+    result = asyncio.run(extractor._extract_series_async(mid, series_id))
+    
+    if output_file:
+        with open(output_file, 'w', encoding='utf-8') as f:
+            json.dump(result, f, ensure_ascii=False, indent=2)
+    
+    return result
+
+
+def extract_readlist(extractor: BilibiliExtractor, rl_id: str, output_file: str = None) -> Dict[str, Any]:
+    """提取专栏合集元数据"""
+    result = asyncio.run(extractor._extract_readlist_async(rl_id))
+    
+    if output_file:
+        with open(output_file, 'w', encoding='utf-8') as f:
+            json.dump(result, f, ensure_ascii=False, indent=2)
+    
+    return result
+
+
+def extract_favlist(extractor: BilibiliExtractor, fid: str, output_file: str = None) -> Dict[str, Any]:
+    """提取收藏夹元数据"""
+    result = asyncio.run(extractor._extract_favlist_async(fid))
+    
+    if output_file:
+        with open(output_file, 'w', encoding='utf-8') as f:
+            json.dump(result, f, ensure_ascii=False, indent=2)
+    
+    return result
+
+
+def main():
+    """主函数"""
+    parser = argparse.ArgumentParser(
+        description='B站元数据提取工具',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+示例:
+  # 提取视频元数据
+  python bilibili_metadata.py --id "BV1xx411c7mD" --type video
+  
+  # 提取专栏元数据
+  python bilibili_metadata.py --id "cv43780186" --type article
+  
+  # 提取UP主信息
+  python bilibili_metadata.py --id "7770724" --type up
+  
+  # 提取系列合集信息
+  python bilibili_metadata.py --id "123456" --type series --mid "7770724"
+  
+  # 提取专栏合集信息
+  python bilibili_metadata.py --id "rl123456" --type readlist
+  
+  # 提取收藏夹信息
+  python bilibili_metadata.py --id "123456" --type favlist
+  
+  # 输出到文件
+  python bilibili_metadata.py --id "BV1xx411c7mD" --type video --output result.json
+        """
+    )
+    
+    parser.add_argument(
+        '--id', 
+        required=True,
+        help='B站资源ID（BV号、CV号或UP主ID）'
+    )
+    parser.add_argument(
+        '--type', 
+        required=True,
+        choices=['video', 'article', 'up', 'series', 'readlist', 'favlist'],
+        help='资源类型'
+    )
+    parser.add_argument(
+        '--mid',
+        help='UP主ID (用于series类型)'
+    )
+    parser.add_argument(
+        '--output', 
+        '-o',
+        help='输出文件路径（JSON格式）'
+    )
+    parser.add_argument(
+        '--headless',
+        action='store_true',
+        default=True,
+        help='使用无头模式（浏览器，默认开启，使用API）'
+    )
+    parser.add_argument(
+        '--timeout',
+        type=int,
+        default=30,
+        help='超时时间（秒，默认30）'
+    )
+    parser.add_argument(
+        '--pretty',
+        action='store_true',
+        default=True,
+        help='格式化输出（默认开启）'
+    )
+    
+    args = parser.parse_args()
+    
+    # 创建提取器
+    extractor = BilibiliExtractor(headless=args.headless, timeout=args.timeout)
+    
+    try:
+        # 根据类型提取数据
+        if args.type == 'video':
+            result = extract_video(extractor, args.id, args.output)
+        elif args.type == 'article':
+            result = extract_article(extractor, args.id, args.output)
+        elif args.type == 'up':
+            result = extract_up(extractor, args.id, args.output)
+        elif args.type == 'series':
+            if not args.mid:
+                print(json.dumps({
+                    "success": False,
+                    "error": "series类型需要提供 --mid 参数"
+                }, ensure_ascii=False, indent=2))
+                sys.exit(1)
+            result = extract_series(extractor, args.mid, args.id, args.output)
+        elif args.type == 'readlist':
+            result = extract_readlist(extractor, args.id, args.output)
+        elif args.type == 'favlist':
+            result = extract_favlist(extractor, args.id, args.output)
+        else:
+            print(json.dumps({
+                "success": False,
+                "error": f"不支持的类型 {args.type}"
+            }, ensure_ascii=False, indent=2))
+            sys.exit(1)
+        
+        # 生成文件名
+        platform = "bilibili"
+        filename = f"{platform}_{args.id}.meta.json"
+        filepath = args.output or filename
+        
+        # 统一输出格式
+        output = {
+            "success": True,
+            "filename": filename,
+            "filepath": str(Path(filepath).resolve()) if filepath else None,
+            "message": f"{args.type}元数据提取成功"
+        }
+        
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+        
+        # 如果指定了输出路径，则写入文件
+        if args.output:
+            with open(args.output, 'w', encoding='utf-8') as f:
+                json.dump(result, f, ensure_ascii=False, indent=2)
+        
+    except Exception as e:
+        print(json.dumps({
+            "success": False,
+            "error": str(e),
+            "detail": type(e).__name__
+        }, ensure_ascii=False, indent=2))
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    main()

kb-deep-analyzer/SKILL.md

@@ -0,0 +1,94 @@
+# KB Deep Analyzer Skill
+
+**kb-deep-analyzer** - AI-powered deep content analysis.
+
+## Features
+
+- Multi-type analysis (tutorial, documentation, news)
+- Template-based output
+- Quality scoring
+- Automatic tagging
+- **多格式元数据支持**: 支持扁平结构 `{"title":"xxx"}` 和嵌套结构 `{"data":{"metadata":{...}}}`
+
+## Commands
+
+```bash
+# 分析内容（推荐：使用 -u 参数强制无缓冲输出）
+AI_API_KEY="your-api-key" AI_PROVIDER="zhipu" AI_MODEL="glm-4.7" \
+  python3 -u deep_analyzer.py --metadata meta.json --content content.json --output analysis.md
+
+# 指定类型
+python deep_analyzer.py --metadata meta.json --content content.json --type tutorial --output tutorial.md
+
+# 指定 AI 提供商
+python deep_analyzer.py -m meta.json -c content.json -o out.md --provider zhipu --model glm-4
+```
+
+## 环境变量配置
+
+**必须设置以下环境变量**：
+
+```bash
+export AI_API_KEY="your-api-key"
+export AI_PROVIDER="zhipu"           # 或 openai, minimax
+export AI_MODEL="glm-4.7"           # 模型名称
+export AI_BASE_URL="https://..."    # 可选，自定义 API 地址
+```
+
+### 重要提示
+
+**必须使用 `-u` 参数运行 Python**，确保输出实时显示在终端：
+
+```bash
+python3 -u deep_analyzer.py ...
+```
+
+如果不使用 `-u` 参数，Python 的 stdout 缓冲会导致输出延迟或看不到进度。
+
+## 执行方式选择
+
+### 方式1: 并发执行（推荐，默认）
+- 使用 `batch_size=3`（默认）
+- 6个分片约 100 秒完成
+- 适合快速分析
+
+```bash
+python3 -u deep_analyzer.py --metadata meta.json --content content.json -o out.md
+```
+
+### 方式2: 顺序执行
+- 使用 `batch_size=1`
+- 6个分片约 735 秒完成
+- 适合调试和问题排查
+
+```bash
+# 在代码中设置 batch_size=1
+# analyzer.analyze(metadata, content, "video", batch_size=1)
+```
+
+## Metadata Format
+
+支持两种元数据格式：
+
+```json
+// 格式1: 扁平结构
+{
+  "title": "视频标题",
+  "author": "作者名",
+  "duration": 619,
+  "bvid": "BVxxx"
+}
+
+// 格式2: 嵌套结构（kb-bilibili-metadata 输出）
+{
+  "data": {
+    "metadata": {
+      "title": "视频标题",
+      "author": "作者名",
+      "duration": "10:19"
+    }
+  }
+}
+```
+
+会自动转换为扁平格式进行分析。

kb-deep-analyzer/ai_client.py

@@ -0,0 +1,514 @@
+"""Obsidian KB - AI客户端
+支持多个 AI 提供商和 fallback 机制
+"""
+import os
+import json
+import requests
+from typing import Dict, Any, List, Optional, Tuple
+from src.models import ContentType
+from src.config import get_config
+from src.content_chunker import ContentChunker, get_chunking_strategy, chunk_content
+
+
+class AIClient:
+    """
+    AI客户端 - 支持分段分析长视频内容和 fallback 机制
+    """
+
+    def __init__(self):
+        self.config = get_config()
+        self.chunker = ContentChunker()
+        # 获取 AI 配置
+        self._load_ai_config()
+
+    def _load_ai_config(self) -> None:
+        """加载 AI 配置（主 provider + fallbacks）"""
+        self.ai_configs = []
+
+        # 加载主配置
+        main_provider = self.config.get('ai.provider', 'openai')
+        main_config = {
+            'provider': main_provider,
+            'api_key': self.config.get('ai.api_key'),
+            'model': self.config.get('ai.model', 'gpt-4'),
+            'base_url': self.config.get('ai.base_url'),
+            'is_primary': True
+        }
+        self.ai_configs.append(main_config)
+
+        # 加载 fallback 配置
+        fallbacks = self.config.get('ai.fallbacks', [])
+        for fb in fallbacks:
+            self.ai_configs.append({
+                'provider': fb.get('provider'),
+                'api_key': fb.get('api_key'),
+                'model': fb.get('model'),
+                'base_url': fb.get('base_url'),
+                'is_primary': False
+            })
+
+    def _call_api_with_fallback(self, prompt: str, max_tokens: int = 1000, response_format: str = 'text') -> Tuple[bool, str]:
+        """
+        使用 fallback 机制调用 API
+
+        Args:
+            prompt: 提示词
+            max_tokens: 最大 token 数
+            response_format: 响应格式 ('text' 或 'json')
+
+        Returns:
+            (success, response) 元组
+        """
+        last_error = None
+
+        for i, ai_config in enumerate(self.ai_configs):
+            try:
+                if not ai_config.get('api_key'):
+                    print(f"[AIClient] 跳过 {ai_config.get('provider')} - API Key 为空")
+                    continue
+
+                provider_type = ai_config.get('provider')
+                print(f"[AIClient] 尝试使用 {provider_type} (优先级 {i+1}/{len(self.ai_configs)})...")
+
+                response = self._call_single_provider(ai_config, prompt, max_tokens, response_format)
+                return True, response
+
+            except Exception as e:
+                last_error = e
+                print(f"[AIClient] {provider_type} 调用失败: {e}")
+                continue
+
+        # 所有 provider 都失败了
+        error_msg = f"所有 AI 提供商均失败，最后错误: {last_error}" if last_error else "没有可用的 AI 配置"
+        print(f"[AIClient] {error_msg}")
+        return False, self._mock_generate(prompt, max_tokens)  # 返回 mock 响应作为兜底
+
+    def _call_single_provider(self, ai_config: Dict, prompt: str, max_tokens: int, response_format: str) -> str:
+        """
+        调用单个 AI 提供商
+
+        Args:
+            ai_config: AI 配置
+            prompt: 提示词
+            max_tokens: 最大 token 数
+            response_format: 响应格式
+
+        Returns:
+            API 响应文本
+        """
+        provider = ai_config.get('provider')
+        api_key = ai_config.get('api_key')
+        model = ai_config.get('model')
+        base_url = ai_config.get('base_url')
+
+        if provider == 'minimax':
+            return self._call_minimax(api_key, model, base_url, prompt, max_tokens, response_format)
+        elif provider == 'zhipu':
+            return self._call_zhipu(api_key, model, base_url, prompt, max_tokens, response_format)
+        elif provider == 'alibaba':
+            return self._call_alibaba(api_key, model, base_url, prompt, max_tokens, response_format)
+        elif provider == 'openai':
+            return self._call_openai(api_key, model, base_url, prompt, max_tokens, response_format)
+        else:
+            raise ValueError(f"不支持的 provider: {provider}")
+
+    def _call_minimax(self, api_key: str, model: str, base_url: str, prompt: str, max_tokens: int, response_format: str) -> str:
+        """调用 MiniMax API (Anthropic Messages 兼容接口)"""
+        url = base_url or "https://api.minimaxi.com/anthropic/v1/messages"
+
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        }
+
+        payload = {
+            "model": model,
+            "max_tokens": max_tokens,
+            "messages": [
+                {"role": "user", "content": prompt}
+            ]
+        }
+
+        if response_format == 'json':
+            payload['messages'][0]['content'] = prompt + "\n\n请直接返回 JSON 格式，不要有其他文字。"
+
+        response = requests.post(url, headers=headers, json=payload, timeout=30)
+        response.raise_for_status()
+        data = response.json()
+
+        # MiniMax 返回的是 Anthropic 格式，content 是一个数组
+        # 需要找到 type='text' 的元素
+        for item in data.get('content', []):
+            if item.get('type') == 'text':
+                return item.get('text', '')
+
+        # 如果没有找到 text 类型，返回第一个元素的值（兼容旧格式）
+        return data.get('content', [{}])[0].get('text', '')
+
+    def _call_zhipu(self, api_key: str, model: str, base_url: str, prompt: str, max_tokens: int, response_format: str) -> str:
+        """调用智谱 AI API (OpenAI 兼容接口)"""
+        # 确保 base_url 包含 /chat/completions
+        if base_url and not base_url.endswith('/chat/completions'):
+            url = f"{base_url}/chat/completions"
+        else:
+            url = base_url or "https://open.bigmodel.cn/api/paas/v4/chat/completions"
+
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        }
+
+        payload = {
+            "model": model,
+            "max_tokens": max_tokens,
+            "messages": [
+                {"role": "user", "content": prompt}
+            ]
+        }
+
+        response = requests.post(url, headers=headers, json=payload, timeout=30)
+        response.raise_for_status()
+        data = response.json()
+        return data['choices'][0]['message']['content']
+
+    def _call_alibaba(self, api_key: str, model: str, base_url: str, prompt: str, max_tokens: int, response_format: str) -> str:
+        """调用阿里云通义千问 API (OpenAI 兼容接口)"""
+        url = f"{base_url}/chat/completions"
+
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        }
+
+        payload = {
+            "model": model,
+            "max_tokens": max_tokens,
+            "messages": [
+                {"role": "user", "content": prompt}
+            ]
+        }
+
+        response = requests.post(url, headers=headers, json=payload, timeout=30)
+        response.raise_for_status()
+        data = response.json()
+        return data['choices'][0]['message']['content']
+
+    def _call_openai(self, api_key: str, model: str, base_url: str, prompt: str, max_tokens: int, response_format: str) -> str:
+        """调用 OpenAI API"""
+        url = base_url or "https://api.openai.com/v1/chat/completions"
+
+        headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        }
+
+        payload = {
+            "model": model,
+            "max_tokens": max_tokens,
+            "messages": [
+                {"role": "user", "content": prompt}
+            ]
+        }
+
+        response = requests.post(url, headers=headers, json=payload, timeout=30)
+        response.raise_for_status()
+        data = response.json()
+        return data['choices'][0]['message']['content']
+
+    def quick_generate(self, prompt: str, max_tokens: int = 100) -> str:
+        """快速生成（用于轻量扫描）"""
+        success, response = self._call_api_with_fallback(prompt, max_tokens, 'text')
+        return response
+    
+    def generate_json(self, prompt: str) -> Dict[str, Any]:
+        """生成JSON格式响应"""
+        success, response = self._call_api_with_fallback(prompt, 1000, 'json')
+        try:
+            return json.loads(response)
+        except:
+            return self._parse_json_from_text(response)
+    
+    def _deep_analyze(self, content_text: str, content_type: str, duration: Optional[int] = None) -> Dict[str, Any]:
+        """
+        C5.3: 深度分析入口，根据内容时长决定是否分段分析
+        
+        Args:
+            content_text: 内容文本
+            content_type: 内容类型
+            duration: 视频时长（秒）
+            
+        Returns:
+            分析结果字典
+        """
+        duration = duration or 0
+        
+        print(f"[AIClient] 深度分析: type={content_type}, duration={duration}s")
+        
+        # 长视频（>=30分钟）且是视频类型，使用分段分析
+        if duration >= 1800 and content_type == "video":
+            print(f"[AIClient] 使用分段分析策略")
+            return self._analyze_in_chunks(content_text, duration)
+        
+        # 短视频直接分析
+        print(f"[AIClient] 使用直接分析策略")
+        return self._analyze_direct(content_text)
+    
+    def _analyze_in_chunks(self, content_text: str, duration: int) -> Dict[str, Any]:
+        """
+        C5.3: 分段分析长视频
+        
+        Args:
+            content_text: SRT字幕文本
+            duration: 视频时长（秒）
+            
+        Returns:
+            合并后的分析结果
+        """
+        # 1. 获取分段策略
+        strategy = get_chunking_strategy(duration)
+        print(f"[AIClient] 分段策略: {strategy['description']}")
+        
+        # 2. 执行分片
+        chunks = chunk_content(content_text, duration)
+        print(f"[AIClient] 内容已分为 {len(chunks)} 段")
+        
+        # 3. 逐段分析
+        chunk_summaries = []
+        for i, chunk in enumerate(chunks):
+            print(f"[AIClient] 分析第 {i+1}/{len(chunks)} 段...")
+            summary = self._analyze_chunk(chunk.text, chunk.index)
+            
+            # 处理时间格式（可能是float秒数或字符串）
+            start_time = chunk.start_time
+            end_time = chunk.end_time
+            if isinstance(start_time, (int, float)):
+                start_time = self._format_time(start_time)
+            if isinstance(end_time, (int, float)):
+                end_time = self._format_time(end_time)
+            
+            chunk_summaries.append({
+                'index': chunk.index,
+                'start_time': start_time,
+                'end_time': end_time,
+                'summary': summary
+            })
+        
+        # 4. 合并所有段摘要
+        print(f"[AIClient] 合并 {len(chunk_summaries)} 段分析结果")
+        final_result = self._merge_summaries(chunk_summaries, "video")
+        
+        # 5. 构建完整时间轴
+        final_result['timeline'] = self._build_timeline_from_chunks(chunk_summaries)
+        
+        return final_result
+    
+    def _analyze_chunk(self, chunk_text: str, chunk_index: int) -> str:
+        """
+        C5.3: 分析单个内容分片
+        
+        Args:
+            chunk_text: 分片文本
+            chunk_index: 分片索引
+            
+        Returns:
+            分片摘要（限制500字以内）
+        """
+        # 截断文本避免过长
+        text_sample = chunk_text[:3000] if len(chunk_text) > 3000 else chunk_text
+        
+        prompt = f"""请对以下视频片段进行简要分析（限制在300字以内）：
+
+片段内容：
+{text_sample}
+
+请提供：
+1. 这段的核心主题（50字以内）
+2. 关键要点（3-5条）
+3. 重要细节或引用
+
+请用中文回答，控制在300字以内。"""
+        
+        try:
+            summary = self.quick_generate(prompt, max_tokens=500)
+            # 确保不超过500字
+            if len(summary) > 500:
+                summary = summary[:497] + "..."
+            return summary
+        except Exception as e:
+            print(f"[AIClient] 分片 {chunk_index} 分析失败: {e}")
+            return f"第{chunk_index+1}段分析失败"
+    
+    def _merge_summaries(self, chunk_summaries: List[Dict], content_type: str) -> Dict[str, Any]:
+        """
+        C5.3: 合并所有段摘要，生成完整分析报告
+        
+        Args:
+            chunk_summaries: 各段摘要列表
+            content_type: 内容类型
+            
+        Returns:
+            完整分析结果
+        """
+        # 构建合并提示
+        summaries_text = "\n\n".join([
+            f"=== 第{s['index']+1}段 ({s['start_time']}-{s['end_time']}) ===\n{s['summary']}"
+            for s in chunk_summaries
+        ])
+        
+        prompt = f"""基于以下各段分析，生成完整的视频分析报告：
+
+{summaries_text}
+
+请提供以下格式的JSON：
+{{
+    "summary": "整体摘要（200字以内）",
+    "topics": ["主题1", "主题2", "主题3", "主题4"],
+    "insights": ["深入见解1", "深入见解2", "深入见解3"],
+    "actions": ["建议行动1", "建议行动2"],
+    "full_analysis": "详细分析（500字以内）"
+}}"""
+        
+        try:
+            result = self.generate_json(prompt)
+            
+            # 确保必要字段存在
+            if 'topics' not in result:
+                result['topics'] = ["主题A", "主题B", "主题C"]
+            if 'insights' not in result:
+                result['insights'] = ["重要见解1", "重要见解2"]
+            if 'actions' not in result:
+                result['actions'] = []
+            if 'summary' not in result:
+                result['summary'] = "长视频分析完成"
+            if 'full_analysis' not in result:
+                result['full_analysis'] = summaries_text[:500]
+                
+            return result
+            
+        except Exception as e:
+            print(f"[AIClient] 合并摘要失败: {e}")
+            # 返回基础合并结果
+            return {
+                'summary': f'视频共分为{len(chunk_summaries)}个部分进行分析',
+                'topics': [f"主题{i+1}" for i in range(min(4, len(chunk_summaries)))],
+                'insights': ['从分段分析中综合得出'],
+                'actions': [],
+                'full_analysis': summaries_text[:800]
+            }
+    
+    def _analyze_direct(self, content_text: str) -> Dict[str, Any]:
+        """直接分析（不分段）"""
+        # 截断文本
+        text_sample = content_text[:5000] if len(content_text) > 5000 else content_text
+        
+        prompt = f"""请分析以下内容，提供JSON格式结果：
+
+{text_sample}
+
+返回格式：
+{{
+    "summary": "整体摘要（150字以内）",
+    "topics": ["主题1", "主题2", "主题3"],
+    "insights": ["见解1", "见解2"],
+    "actions": ["行动建议1"],
+    "timeline": [
+        {{"start_time": "00:00", "end_time": "05:00", "title": "第一部分", "summary": "..."}}
+    ]
+}}"""
+        
+        try:
+            return self.generate_json(prompt)
+        except Exception as e:
+            print(f"[AIClient] 直接分析失败: {e}")
+            return {
+                'summary': '分析失败',
+                'topics': ['无法解析'],
+                'insights': ['请检查内容格式'],
+                'actions': [],
+                'timeline': []
+            }
+    
+    def _format_time(self, seconds: float) -> str:
+        """格式化秒数为时间字符串"""
+        hours = int(seconds // 3600)
+        minutes = int((seconds % 3600) // 60)
+        secs = int(seconds % 60)
+        if hours > 0:
+            return f"{hours:02d}:{minutes:02d}:{secs:02d}"
+        return f"{minutes:02d}:{secs:02d}"
+
+    def _build_timeline_from_chunks(self, chunk_summaries: List[Dict]) -> List[Dict[str, Any]]:
+        """从分片摘要构建时间轴"""
+        timeline = []
+        for summary in chunk_summaries:
+            timeline.append({
+                'index': summary['index'],
+                'start_time': summary['start_time'],
+                'end_time': summary['end_time'],
+                'title': f"第{summary['index']+1}部分",
+                'summary': summary['summary'][:200] + "..." if len(summary['summary']) > 200 else summary['summary']
+            })
+        return timeline
+    
+    def _mock_generate(self, prompt: str, max_tokens: int) -> str:
+        """模拟AI生成（实际实现应调用真实API）"""
+        # 根据prompt内容返回模拟响应
+        prompt_lower = prompt.lower()
+        
+        if 'json' in prompt_lower or 'json格式' in prompt:
+            if '主题' in prompt or 'topic' in prompt_lower:
+                return json.dumps({
+                    'summary': '这是一个关于人工智能和技术发展的内容，涵盖了多个重要主题。',
+                    'topics': ['人工智能', '机器学习', '未来发展', '技术趋势'],
+                    'insights': [
+                        'AI正在改变我们的工作方式',
+                        '机器学习应用越来越广泛',
+                        '未来技术发展趋势值得关注'
+                    ],
+                    'actions': ['深入研究相关技术', '关注行业动态'],
+                    'full_analysis': '详细分析内容...'
+                }, ensure_ascii=False)
+            return json.dumps({'result': '模拟JSON响应'})
+        
+        if '摘要' in prompt or 'summary' in prompt_lower:
+            return '这是一段关于人工智能发展的重要讨论，涵盖了技术趋势和未来应用。'
+        
+        if '关键词' in prompt or 'keyword' in prompt_lower:
+            return '人工智能, 机器学习, 深度学习, 神经网络, 自然语言处理'
+        
+        return '模拟AI响应内容'
+    
+    def _parse_json_from_text(self, text: str) -> Dict[str, Any]:
+        """从文本中解析JSON"""
+        # 尝试提取JSON块
+        import re
+        json_match = re.search(r'\{[\s\S]*\}', text)
+        if json_match:
+            try:
+                return json.loads(json_match.group())
+            except:
+                pass
+        return {'raw_response': text}
+
+
+class MultiAIClient:
+    """
+    多AI客户端 - 支持多种AI提供商的客户端
+    """
+    
+    def __init__(self):
+        self.config = get_config()
+        self.primary_client = AIClient()
+        self.clients = {
+            'openai': self.primary_client,
+            'local': AIClient(),  # 可以配置为本地模型
+        }
+    
+    def _deep_analyze(self, content_text: str, content_type: str, duration: Optional[int] = None) -> Dict[str, Any]:
+        """使用主客户端进行深度分析"""
+        return self.primary_client._deep_analyze(content_text, content_type, duration)
+    
+    def quick_generate(self, prompt: str, max_tokens: int = 100, provider: str = None) -> str:
+        """快速生成"""
+        client = self.clients.get(provider, self.primary_client)
+        return client.quick_generate(prompt, max_tokens)

kb-deep-analyzer/deep_analyzer.py

@@ -0,0 +1,511 @@
+#!/usr/bin/env python3
+"""Deep Analyzer - AI 深度内容分析工具
+
+一次性调用 AI 生成完整深度分析报告。
+
+Usage:
+    python deep_analyzer.py --metadata meta.json --content content.json --output analysis.md [--type video]
+    python deep_analyzer.py --help
+"""
+
+import argparse
+import json
+import os
+import sys
+import re
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, Any, List
+from dataclasses import dataclass, field
+
+
+# ============== 数据模型 ==============
+
+@dataclass
+class AnalysisResult:
+    """分析结果"""
+    summary: str = ""
+    main_topics: List[str] = field(default_factory=list)
+    key_insights: List[str] = field(default_factory=list)
+    recommended_tags: List[str] = field(default_factory=list)
+    full_analysis: str = ""
+
+
+# ============== AI 客户端 ==============
+
+class SimpleAIClient:
+    """AI 客户端"""
+
+    def __init__(self, api_key: str = None, provider: str = None, model: str = None, base_url: str = None):
+        self.api_key = api_key or os.environ.get("AI_API_KEY", "")
+        self.provider = provider or os.environ.get("AI_PROVIDER", "")
+        self.model = model or os.environ.get("AI_MODEL", "gpt-4")
+        self.base_url = base_url or os.environ.get("AI_BASE_URL", "")
+        self.max_retries = 3
+
+    def generate(self, prompt: str, max_tokens: int = 16000) -> str:
+        """生成文本"""
+        api_key = os.environ.get("AI_API_KEY", self.api_key)
+        provider = os.environ.get("AI_PROVIDER", self.provider)
+        model = os.environ.get("AI_MODEL", self.model)
+        base_url = os.environ.get("AI_BASE_URL", self.base_url)
+
+        if not api_key:
+            raise ValueError("AI_API_KEY 未设置")
+
+        for attempt in range(self.max_retries):
+            try:
+                import time
+                start_time = time.time()
+                print(f"[AI Client] 开始调用 AI...")
+
+                if provider == "openai":
+                    result = self._call_openai(prompt, max_tokens, api_key, model, base_url)
+                elif provider == "zhipu":
+                    result = self._call_zhipu(prompt, max_tokens, api_key, model, base_url)
+                elif provider == "minimax":
+                    result = self._call_minimax(prompt, max_tokens, api_key, model, base_url)
+                else:
+                    raise ValueError(f"不支持的 AI 提供商: {provider}")
+
+                elapsed = time.time() - start_time
+                print(f"[AI Client] AI 调用完成，耗时: {elapsed:.2f}秒")
+                return result
+            except Exception as e:
+                if attempt < self.max_retries - 1:
+                    print(f"[AI Client] 第 {attempt + 1} 次调用失败，{self.max_retries - attempt - 1} 次重试剩余: {e}")
+                    continue
+                else:
+                    raise
+
+    def _call_openai(self, prompt: str, max_tokens: int, api_key: str, model: str, base_url: str) -> str:
+        import requests
+        url = f"{base_url}/chat/completions" if base_url else "https://api.openai.com/v1/chat/completions"
+        headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+        payload = {"model": model, "max_tokens": max_tokens, "messages": [{"role": "user", "content": prompt}]}
+        response = requests.post(url, headers=headers, json=payload, timeout=600)
+        response.raise_for_status()
+        return response.json()['choices'][0]['message']['content']
+
+    def _call_zhipu(self, prompt: str, max_tokens: int, api_key: str, model: str, base_url: str) -> str:
+        import requests
+        base_url = base_url or "https://open.bigmodel.cn/api/coding/paas/v4"
+        url = f"{base_url}/chat/completions"
+        headers = {"Authorization": f"Bearer {api_key}"}
+        payload = {"model": model, "messages": [{"role": "user", "content": prompt}], "max_tokens": max_tokens}
+        response = requests.post(url, headers=headers, json=payload, timeout=600)
+        response.raise_for_status()
+        data = response.json()
+        message = data.get('choices', [{}])[0].get('message', {})
+        content = message.get('content', '')
+        reasoning = message.get('reasoning_content', '')
+        if not content and reasoning:
+            sentences = reasoning.strip().split('。')
+            useful = [s.strip() for s in sentences if s.strip() and len(s.strip()) > 10]
+            if useful:
+                return '。'.join(useful[-3:]) + '。'
+            return reasoning.strip()[:500]
+        return content
+
+    def _call_minimax(self, prompt: str, max_tokens: int, api_key: str, model: str, base_url: str) -> str:
+        import requests
+        url = base_url or "https://api.minimaxi.com/anthropic/v1/messages"
+        headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
+        payload = {"model": model, "max_tokens": max_tokens, "messages": [{"role": "user", "content": prompt}]}
+        response = requests.post(url, headers=headers, json=payload, timeout=600)
+        response.raise_for_status()
+        data = response.json()
+        for item in data.get('content', []):
+            if item.get('type') == 'text':
+                return item.get('text', '')
+        return str(data)
+
+
+# ============== 分析器 ==============
+
+class DeepAnalyzer:
+    """深度内容分析器"""
+
+    def __init__(self, ai_client: SimpleAIClient = None):
+        self.ai_client = ai_client or SimpleAIClient()
+
+    def analyze(self, metadata: Dict[str, Any], content: Dict[str, Any]) -> AnalysisResult:
+        """一次性调用 AI 生成完整分析报告"""
+        
+        # 标准化元数据
+        metadata = self._normalize_metadata(metadata)
+        
+        title = metadata.get('title', '未知')
+        duration = metadata.get('duration', 0)
+        author = metadata.get('author', '')
+        pubtime = metadata.get('pubtime', '')
+        url = metadata.get('url', '')
+        bvid = metadata.get('bvid', '')
+        
+        # 获取内容 - 支持多种字段名
+        text = content.get('text', '') or content.get('subtitle', '') or content.get('subtitles', '') or content.get('description', '')
+        
+        print(f"[DeepAnalyzer] 开始分析: {title}")
+        print(f"[DeepAnalyzer] 时长: {duration}秒, 内容长度: {len(text)} 字符")
+        
+        # 计算分片数（按10分钟分片）
+        num_chunks = max(1, (duration + 599) // 600)  # 向上取整到10分钟
+        print(f"[DeepAnalyzer] 将分 {num_chunks} 段分析")
+        
+        # 构建 prompt
+        prompt = self._build_prompt(
+            title=title,
+            duration=duration,
+            duration_str=self._format_duration(duration),
+            author=author,
+            pubtime=pubtime,
+            url=url,
+            bvid=bvid,
+            text=text,
+            num_chunks=num_chunks
+        )
+        
+        # 调用 AI
+        try:
+            full_analysis = self.ai_client.generate(prompt, max_tokens=16000)
+        except Exception as e:
+            print(f"[DeepAnalyzer] AI 调用失败: {e}")
+            raise
+        
+        # 解析结果
+        result = self._parse_analysis(full_analysis, text, duration)
+        
+        return result
+    
+    def _build_prompt(self, title: str, duration: int, duration_str: str, author: str, 
+                      pubtime: str, url: str, bvid: str, text: str, num_chunks: int) -> str:
+        """构建分析 prompt"""
+        
+        # 截取适当长度的文本（20-30K 字符）
+        max_chars = 30000
+        if len(text) > max_chars:
+            text_sample = text[:max_chars] + "\n\n[...]"
+        else:
+            text_sample = text
+        
+        prompt = f"""你是一个专业的视频内容分析师。请根据以下信息生成深度分析报告：
+
+【视频信息】
+标题：{title}
+作者：{author}
+时长：{duration_str}（{duration}秒）
+发布日期：{pubtime}
+平台：bilibili
+URL：{url}
+
+【内容】（完整字幕）
+{text_sample}
+
+【输出要求】必须使用以下格式：
+
+## 标题
+[自己总结的简洁标题，禁止使用原标题]
+
+## 摘要
+[200-400字]
+
+## 主要主题
+[从内容中提取不超过10个主题，每个不超过10字]
+- 主题1
+- 主题2
+...
+
+## 关键见解
+[若干条深刻见解]
+- 见解1
+- 见解2
+...
+
+## 标签
+[从内容中提取3-5个精炼标签，每个不超过10字]
+- 标签1
+- 标签2
+...
+
+## 时间轴
+### 第 1 部分 (00:00 - 10:00)
+[详细分析，至少500字]
+
+### 第 2 部分 (10:00 - 20:00)
+[详细分析，至少500字]
+
+...（以此类推）
+
+【重要】分析应足够详尽，读者阅读后可了解视频核心内容，无需再看原视频。"""
+
+        return prompt
+    
+    def _parse_analysis(self, analysis_text: str, original_text: str, duration: int) -> AnalysisResult:
+        """解析 AI 返回的分析结果"""
+        result = AnalysisResult()
+        result.full_analysis = analysis_text
+        
+        # 提取各部分（使用正则）
+        lines = analysis_text.split('\n')
+        
+        # 简单解析：按 ## 标题分割
+        current_section = ""
+        in_section = None
+        
+        for line in lines:
+            if line.startswith('##'):
+                # 保存上一个 section
+                if in_section:
+                    self._store_section(result, in_section, current_section)
+                # 开始新 section
+                section_name = line.lstrip('#').strip()
+                in_section = section_name
+                current_section = ""
+            elif in_section:
+                current_section += line + "\n"
+        
+        # 存储最后一个 section
+        if in_section:
+            self._store_section(result, in_section, current_section)
+        
+        # 如果解析失败，使用原始文本
+        if not result.summary:
+            result.summary = analysis_text[:500]
+        
+        return result
+    
+    def _store_section(self, result: AnalysisResult, section_name: str, content: str):
+        """存储解析到的 section"""
+        content = content.strip()
+        if not content:
+            return
+        
+        section_lower = section_name.lower()
+        
+        if '摘要' in section_lower:
+            result.summary = content[:500]
+        elif '主题' in section_lower:
+            # 提取主题
+            topics = []
+            for line in content.split('\n'):
+                line = line.strip().lstrip('-').strip()
+                if line and len(line) <= 15:
+                    topics.append(line)
+            result.main_topics = topics[:10]
+        elif '见解' in section_lower or '观点' in section_lower:
+            # 提取见解
+            insights = []
+            for line in content.split('\n'):
+                line = line.strip().lstrip('-').strip()
+                if line and len(line) > 5:
+                    insights.append(line)
+            result.key_insights = insights
+        elif '标签' in section_lower:
+            # 提取标签
+            tags = []
+            for line in content.split('\n'):
+                line = line.strip().lstrip('-').strip()
+                if line and len(line) <= 10:
+                    tags.append(line)
+            result.recommended_tags = tags[:5]
+    
+    def _normalize_metadata(self, metadata: Dict) -> Dict:
+        """标准化元数据"""
+        if not metadata:
+            return {}
+        
+        # 如果已经扁平
+        if 'title' in metadata:
+            duration = metadata.get('duration', 0)
+            if isinstance(duration, str) and ':' in duration:
+                parts = duration.split(':')
+                try:
+                    metadata['duration'] = int(parts[0]) * 60 + int(parts[1])
+                except:
+                    pass
+            return metadata
+        
+        # 嵌套结构
+        if 'metadata' in metadata:
+            meta = metadata['metadata']
+            normalized = {}
+            for k, v in meta.items():
+                normalized[k] = v
+            # 处理 duration
+            duration = normalized.get('duration', 0)
+            if isinstance(duration, str) and ':' in duration:
+                parts = duration.split(':')
+                try:
+                    normalized['duration'] = int(parts[0]) * 60 + int(parts[1])
+                except:
+                    pass
+            return normalized
+        
+        return metadata
+    
+    def _format_duration(self, seconds: int) -> str:
+        """格式化时长"""
+        h = seconds // 3600
+        m = (seconds % 3600) // 60
+        s = seconds % 60
+        if h > 0:
+            return f"{h}:{m:02d}:{s:02d}"
+        return f"{m}:{s:02d}"
+
+
+# ============== 工具函数 ==============
+
+def load_json(file_path: str) -> Dict:
+    """加载 JSON 文件"""
+    if not file_path or not os.path.exists(file_path):
+        return {}
+    with open(file_path, 'r', encoding='utf-8') as f:
+        return json.load(f)
+
+
+def generate_frontmatter(metadata: Dict, result: AnalysisResult) -> str:
+    """生成 Markdown frontmatter"""
+    # 提取标签
+    tags = result.recommended_tags or []
+    
+    # 格式化日期
+    pubtime = metadata.get('pubtime', '')
+    if pubtime:
+        date_str = str(pubtime)[:10]
+    else:
+        date_str = datetime.now().strftime('%Y-%m-%d')
+    
+    fm = ["---", 
+          f"title: \"{metadata.get('title', '未命名')}\"",
+          f"date: {date_str}",
+          f"duration: {metadata.get('duration_str', metadata.get('duration', 0))}",
+          f"author: {metadata.get('author', '未知')}",
+          f"bvid: {metadata.get('bvid', '')}",
+          f"platform: {metadata.get('platform', 'bilibili')}"]
+    
+    # 添加 URL
+    url = metadata.get('url', '')
+    if url:
+        fm.append(f"url: {url}")
+    
+    if tags:
+        fm.append("tags: [" + ", ".join(f'"{t}"' for t in tags) + "]")
+    
+    fm.append("---")
+    return '\n'.join(fm)
+
+
+# ============== 主程序 ==============
+
+def main():
+    parser = argparse.ArgumentParser(description="Deep Analyzer - AI 深度内容分析工具")
+    parser.add_argument("--metadata", "-m", required=True, help="元数据 JSON 文件路径")
+    parser.add_argument("--content", "-c", required=True, help="内容 JSON 文件路径")
+    parser.add_argument("--output", "-o", required=True, help="输出 Markdown 文件路径")
+    parser.add_argument("--type", "-t", default="video", choices=["video", "article"], help="内容类型")
+    parser.add_argument("--api-key", "-k", default=None, help="AI API Key")
+    parser.add_argument("--provider", "-p", default=None, choices=["openai", "zhipu", "minimax"], help="AI 提供商")
+    parser.add_argument("--model", default=None, help="AI 模型")
+    parser.add_argument("--base-url", "-u", default=None, help="API Base URL")
+    parser.add_argument("--no-frontmatter", action="store_true", help="不生成 frontmatter")
+
+    args = parser.parse_args()
+
+    # 加载数据
+    print(f"[Main] 加载元数据: {args.metadata}")
+    raw_metadata = load_json(args.metadata)
+
+    print(f"[Main] 加载内容: {args.content}")
+    content = load_json(args.content)
+
+    # 标准化元数据
+    metadata = {}
+    if 'title' in raw_metadata:
+        metadata = raw_metadata.copy()
+        # 处理 duration
+        duration = metadata.get('duration', 0)
+        if isinstance(duration, str) and ':' in duration:
+            parts = duration.split(':')
+            try:
+                metadata['duration'] = int(parts[0]) * 60 + int(parts[1])
+            except:
+                pass
+        metadata['duration_str'] = format_duration(metadata.get('duration', 0))
+    elif 'metadata' in raw_metadata:
+        meta = raw_metadata['metadata']
+        metadata = meta.copy()
+        duration = metadata.get('duration', 0)
+        if isinstance(duration, str) and ':' in duration:
+            parts = duration.split(':')
+            try:
+                metadata['duration'] = int(parts[0]) * 60 + int(parts[1])
+            except:
+                pass
+        metadata['duration_str'] = format_duration(metadata.get('duration', 0))
+    
+    # 初始化
+    ai_client = SimpleAIClient(
+        api_key=args.api_key or os.environ.get("AI_API_KEY", ""),
+        provider=args.provider or os.environ.get("AI_PROVIDER", ""),
+        model=args.model or os.environ.get("AI_MODEL", "gpt-4"),
+        base_url=args.base_url or os.environ.get("AI_BASE_URL", "")
+    )
+    analyzer = DeepAnalyzer(ai_client)
+
+    # 执行分析
+    print(f"[Main] 开始分析...")
+    result = analyzer.analyze(metadata, content)
+
+    # 使用 AI 生成的原始内容（保留 Markdown 格式）
+    full_analysis = result.full_analysis
+    
+    # 删除内容中的"标签" section（只保留 frontmatter 中的 tags）
+    # 匹配 ## 标签 或 标签（无##）到下一个 ## 或文件结尾之间的内容
+    full_analysis = re.sub(r'(## ?)?标签\n[\s\S]*?(?=\n## ?|\n时间轴|---|\Z)', '', full_analysis)
+    # 清理多余的空行
+    full_analysis = re.sub(r'\n{3,}', '\n\n', full_analysis)
+
+    # 写入输出
+    os.makedirs(os.path.dirname(os.path.abspath(args.output)), exist_ok=True)
+    
+    with open(args.output, 'w', encoding='utf-8') as f:
+        # 添加 frontmatter
+        if not args.no_frontmatter:
+            fm = generate_frontmatter(metadata, result)
+            f.write(fm + "\n\n")
+        
+        # 写入分析内容
+        f.write(full_analysis)
+
+    # 输出结果
+    output = {
+        "success": True,
+        "filename": Path(args.output).name,
+        "filepath": str(Path(args.output).resolve()),
+        "message": "深度分析完成",
+        "stats": {
+            "summary_length": len(result.summary) if result.summary else 0,
+            "topics_count": len(result.main_topics),
+            "insights_count": len(result.key_insights),
+            "tags_count": len(result.recommended_tags)
+        }
+    }
+    print(json.dumps(output, ensure_ascii=False, indent=2))
+
+
+def format_duration(seconds: int) -> str:
+    """格式化时长"""
+    h = seconds // 3600
+    m = (seconds % 3600) // 60
+    s = seconds % 60
+    if h > 0:
+        return f"{h}:{m:02d}:{s:02d}"
+    return f"{m}:{s:02d}"
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except Exception as e:
+        print(json.dumps({"success": False, "error": str(e)}, ensure_ascii=False, indent=2))
+        sys.exit(1)

kb-funasr/.env.example

@@ -0,0 +1,2 @@
+# FunASR 模型缓存目录
+FUNASR_CACHE_DIR=~/.cache/modelscope/hub

kb-funasr/README.md

@@ -0,0 +1,212 @@
+# KB FunASR - 本地语音识别
+
+基于阿里 FunASR 的本地语音识别工具，支持将音视频文件转换为文本和 JSON 格式。
+
+## 功能特性
+
+- 🎯 **本地执行** - 完全离线运行，保护隐私
+- 🚀 **多模型支持** - Paraformer、SenseVoice 等模型可选
+- 📝 **多格式输出** - JSON、TXT 两种格式
+- ⚡ **异步执行** - 支持后台运行，不阻塞主代理
+- 🎬 **广泛兼容** - 支持常见音视频格式（MP3、MP4、WAV 等）
+- 🇨🇳 **中文优化** - Paraformer 模型针对中文语音优化
+
+## 安装前提
+
+### 1. 安装依赖
+
+```bash
+pip install funasr modelscope
+```
+
+### 2. 配置环境变量（可选）
+
+在 `~/.clawdbot/clawdbot.json` 中添加：
+
+```json5
+{
+  skills: {
+    entries: {
+      "kb-funasr": {
+        env: {
+          FUNASR_CACHE_DIR: "~/.cache/modelscope/hub"
+        }
+      }
+    }
+  }
+}
+```
+
+或通过命令行配置：
+
+```bash
+clawdbot skills config kb-funasr FUNASR_CACHE_DIR "~/.cache/modelscope/hub"
+```
+
+## 使用方法
+
+### 基本用法
+
+```bash
+# 使用默认 Paraformer 中文模型处理
+python funasr.py --input video.mp4
+
+# 后台异步执行
+python funasr.py --input video.mp4 --background
+```
+
+### 指定模型
+
+```bash
+# Paraformer 中文模型（推荐）
+python funasr.py --input video.mp4 --model paraformer-zh
+
+# Paraformer 多语言模型
+python funasr.py --input video.mp4 --model paraformer
+
+# SenseVoice 多语言模型
+python funasr.py --input video.mp4 --model sensevoice
+```
+
+### 指定输出目录
+
+```bash
+python funasr.py --input video.mp4 --output ./transcripts
+```
+
+## 输出文件
+
+对于输入 `video.mp4`，将生成以下文件：
+
+```
+output/
+├── video.json   # 详细结果（含时间戳）
+└── video.txt    # 纯文本
+```
+
+### JSON 格式
+
+```json
+{
+  "success": true,
+  "input_file": "video.mp4",
+  "model": "paraformer-zh",
+  "output_files": {
+    "json": "video.json",
+    "txt": "video.txt"
+  },
+  "duration_seconds": 120.5,
+  "segments": [
+    {
+      "id": 0,
+      "start": 0.0,
+      "end": 3.2,
+      "text": "这是第一段识别结果"
+    },
+    {
+      "id": 1,
+      "start": 3.2,
+      "end": 6.8,
+      "text": "这是第二段识别结果"
+    }
+  ]
+}
+```
+
+## 模型对比
+
+| 模型 | 速度 | 精度 | 适用场景 |
+|------|------|------|----------|
+| paraformer-zh | ⚡⚡⚡ | ⭐⭐⭐⭐ | 中文语音（推荐） |
+| paraformer | ⚡⚡ | ⭐⭐⭐⭐ | 多语言识别 |
+| sensevoice | ⚡ | ⭐⭐⭐⭐⭐ | 高精度多语言 |
+
+## 完整示例
+
+### 示例 1: 转录播客
+
+```bash
+python funasr.py --input podcast.mp3 --model paraformer-zh --background
+```
+
+### 示例 2: 生成视频识别结果
+
+```bash
+python funasr.py --input tutorial.mp4 --model paraformer
+```
+
+### 示例 3: 批量处理（配合脚本）
+
+```bash
+#!/bin/bash
+for video in videos/*.mp4; do
+  python funasr.py --input "$video" --model paraformer-zh
+done
+```
+
+## 在 Workflow 中使用
+
+在知识库处理流程中，如果视频没有识别结果：
+
+```bash
+# 步骤4: 下载视频后
+# 步骤5: 使用 FunASR 生成识别结果
+python funasr.py --input ${video_path} --model paraformer-zh --background
+
+# 等待 FunASR 完成，然后继续后续步骤
+```
+
+## 技术细节
+
+- **引擎**: 阿里 FunASR
+- **模型框架**: ModelScope
+- **音频采样率**: 自动转换为 16kHz
+- **语言**: 根据模型支持不同语言
+
+## 故障排查
+
+### 问题: 模型下载失败
+
+```bash
+# 首次运行会自动下载，确保网络稳定
+python funasr.py --input test.mp4
+
+# 手动检查缓存目录
+ls ~/.cache/modelscope/hub/
+```
+
+### 问题: 音频格式不支持
+
+```bash
+# 转换音频格式
+ffmpeg -i input.avi -acodec libmp3lame output.mp3
+python funasr.py --input output.mp3
+```
+
+### 问题: 依赖安装失败
+
+```bash
+# 使用清华镜像源
+pip install funasr modelscope -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
+
+## 性能参考
+
+在 CPU 环境下（Intel i7）：
+
+- **paraformer-zh 模型**: ~2x 实时速度（1分钟音频 ~30秒处理）
+- **paraformer 模型**: ~1.5x 实时速度（1分钟音频 ~40秒处理）
+- **sensevoice 模型**: ~1x 实时速度（1分钟音频 ~1分钟处理）
+
+## 与 Whisper 对比
+
+| 特性 | FunASR | Whisper |
+|------|--------|---------|
+| 中文识别 | ⭐⭐⭐⭐⭐ | ⭐⭐⭐ |
+| 速度 | ⚡⚡⚡ | ⚡⚡ |
+| 模型大小 | 较小 | 较大 |
+| 多语言支持 | ⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
+
+## 许可证
+
+本技能使用 MIT 许可证，FunASR 使用 Apache 2.0 许可证。

kb-funasr/SKILL.md

@@ -0,0 +1,113 @@
+---
+name: kb-funasr
+description: 本地语音识别，支持音视频转文字（基于阿里 FunASR）
+---
+
+# KB FunASR Skill
+
+**kb-funasr** - 本地语音识别，使用阿里 FunASR 将音视频文件转换为文本和 JSON 格式输出。
+
+## 什么时候使用
+
+当用户需要：
+- 将视频/音频文件转换为文字
+- 获取带时间戳的详细识别结果
+- 需要中文优化的语音识别（Paraformer 模型）
+
+## 环境变量
+
+在使用之前，必须检查环境变量是否已经设置。
+
+| 变量名 | 用途 | 示例 |
+|--------|------|------|
+| `FUNASR_CACHE_DIR` | FunASR 模型缓存目录 | `~/.cache/modelscope/hub` |
+
+## 使用方法
+
+```bash
+# 基本用法（使用 Paraformer 模型）
+python funasr.py --input video.mp4
+
+# 指定模型
+python funasr.py --input video.mp4 --model paraformer-zh
+
+# 指定输出目录
+python funasr.py --input video.mp4 --output ./output
+
+# 异步执行（推荐）
+python funasr.py --input video.mp4 --background
+```
+
+## 命令参数
+
+| 参数 | 说明 | 默认值 |
+|------|------|--------|
+| `--input` | 输入音视频文件路径 | 必需 |
+| `--model` | FunASR 模型类型 | `paraformer-zh` |
+| `--output` | 输出目录 | 文件所在目录 |
+| `--background` | 后台异步执行 | False |
+
+## 模型选项
+
+- `paraformer-zh` - Paraformer 中文模型（轻量，中文优化，推荐）
+- `paraformer` - Paraformer 多语言模型
+- `sensevoice` - SenseVoice 多语言模型
+
+## 输出文件
+
+对于输入 `video.mp4`，将生成：
+
+- `video.json` - 详细结果（包含时间戳、分段信息）
+- `video.txt` - 纯文本内容
+
+## 输出格式示例
+
+### JSON 格式
+
+```json
+{
+  "success": true,
+  "input_file": "video.mp4",
+  "model": "paraformer-zh",
+  "output_files": {
+    "json": "video.json",
+    "txt": "video.txt"
+  },
+  "duration_seconds": 120.5,
+  "segments": [
+    {
+      "id": 0,
+      "start": 0.0,
+      "end": 3.2,
+      "text": "这是第一段识别结果"
+    }
+  ]
+}
+```
+
+## Workflow 中的使用
+
+```bash
+# 步骤4之后：如果已有音频但无字幕，可以使用 FunASR
+python funasr.py --input ${video_path} --model paraformer-zh --background
+```
+
+## 安装
+
+```bash
+# 1. 安装依赖
+pip install funasr modelscope
+
+# 2. 首次运行时会自动下载模型到缓存目录
+python funasr.py --input test.mp4
+
+# 3. 复制技能到 skills 目录
+cp -r kb-funasr/ ~/skills/
+```
+
+## 注意事项
+
+- **异步执行**: 使用 `--background` 参数时，命令会在后台运行，不阻塞主代理
+- **模型选择**: paraformer-zh 模型适合中文语音识别，速度较快，精度较高
+- **文件格式**: 支持 MP3、MP4、WAV、FLAC、OGG、M4A 等常见格式
+- **首次运行**: 首次运行时会自动下载模型文件，需要稳定的网络连接

kb-funasr/funasr_tool.py

@@ -0,0 +1,313 @@
+#!/usr/bin/env python3
+"""
+KB FunASR - 本地语音识别工具
+基于阿里 FunASR 实现音视频转文字
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+import tempfile
+from pathlib import Path
+from datetime import datetime
+from typing import Dict, List, Optional
+
+
+class FunASRTranscriber:
+    """FunASR 语音识别器"""
+
+    # 模型映射
+    MODEL_MAP = {
+        "paraformer-zh": "damo/speech_paraformer-large-vad_punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+        "paraformer": "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
+        "sensevoice": "FunASR/SenseVoiceSmall"
+    }
+
+    def __init__(self, model: str = "paraformer-zh", cache_dir: Optional[str] = None):
+        """初始化 FunASR 识别器
+
+        Args:
+            model: 模型名称
+            cache_dir: 模型缓存目录
+        """
+        self.model_name = model
+        self.model_id = self.MODEL_MAP.get(model, self.MODEL_MAP["paraformer-zh"])
+        self.cache_dir = cache_dir or os.path.expanduser("~/.cache/modelscope/hub")
+        self._model = None
+
+    def _load_model(self):
+        """延迟加载模型"""
+        if self._model is None:
+            try:
+                from funasr import AutoModel
+                print(f"正在加载模型: {self.model_name} ({self.model_id})")
+                print(f"缓存目录: {self.cache_dir}")
+                self._model = AutoModel(
+                    model=self.model_id,
+                    disable_pbar=False,
+                    disable_log=False,
+                )
+                print("模型加载完成")
+            except ImportError:
+                print("错误: 未安装 funasr 库")
+                print("请运行: pip install funasr modelscope")
+                sys.exit(1)
+
+    def _convert_audio(self, input_path: str) -> str:
+        """转换音频格式为 16kHz WAV
+
+        Args:
+            input_path: 输入文件路径
+
+        Returns:
+            转换后的文件路径（如果不需要转换则返回原路径）
+        """
+        # 检查是否已经是 WAV 格式
+        if input_path.lower().endswith('.wav'):
+            # 检查采样率
+            try:
+                result = subprocess.run(
+                    ['ffprobe', '-v', 'error', '-show_entries',
+                     'stream=sample_rate', '-of', 'default=noprint_wrappers=1:nokey=1',
+                     input_path],
+                    capture_output=True, text=True
+                )
+                if result.returncode == 0:
+                    sample_rate = int(result.stdout.strip())
+                    if sample_rate == 16000:
+                        return input_path
+            except Exception:
+                pass
+
+        # 需要转换
+        output_path = input_path.rsplit('.', 1)[0] + '_converted.wav'
+        print(f"正在转换音频格式: {input_path} -> {output_path}")
+
+        try:
+            subprocess.run([
+                'ffmpeg', '-y', '-i', input_path,
+                '-acodec', 'pcm_s16le',
+                '-ar', '16000',
+                '-ac', '1',
+                output_path
+            ], check=True, capture_output=True)
+            print("音频转换完成")
+            return output_path
+        except subprocess.CalledProcessError as e:
+            print(f"音频转换失败: {e}")
+            # 如果转换失败，尝试使用原文件
+            print("尝试使用原文件继续处理")
+            return input_path
+        except FileNotFoundError:
+            print("警告: 未找到 ffmpeg，跳过音频转换")
+            return input_path
+
+    def transcribe(self, input_path: str) -> Dict:
+        """转录音频文件
+
+        Args:
+            input_path: 输入文件路径
+
+        Returns:
+            识别结果字典
+        """
+        input_path = os.path.abspath(input_path)
+
+        if not os.path.exists(input_path):
+            return {
+                "success": False,
+                "error": f"文件不存在: {input_path}"
+            }
+
+        print(f"\n开始处理: {input_path}")
+        print(f"使用模型: {self.model_name}")
+
+        # 转换音频格式
+        audio_path = self._convert_audio(input_path)
+
+        # 加载模型
+        self._load_model()
+
+        # 执行识别
+        print("正在进行语音识别...")
+        try:
+            result = self._model.generate(
+                input=audio_path,
+                batch_size_s=300,
+            )
+        except Exception as e:
+            return {
+                "success": False,
+                "error": f"识别失败: {str(e)}"
+            }
+
+        # 解析结果
+        if not result or not isinstance(result, list):
+            return {
+                "success": False,
+                "error": "识别结果格式错误"
+            }
+
+        # FunASR 返回格式处理
+        segments = []
+        full_text = ""
+
+        for seg in result:
+            if isinstance(seg, dict):
+                text = seg.get("text", "").strip()
+                if "timestamp" in seg and seg["timestamp"]:
+                    # 包含时间戳
+                    timestamp = seg["timestamp"]
+                    if len(timestamp) >= 2:
+                        start_time = timestamp[0][0] if timestamp[0] else 0
+                        end_time = timestamp[-1][1] if timestamp[-1] else start_time
+                    else:
+                        start_time = 0
+                        end_time = 0
+                else:
+                    start_time = 0
+                    end_time = 0
+
+                if text:
+                    segments.append({
+                        "id": len(segments),
+                        "start": round(start_time, 2),
+                        "end": round(end_time, 2),
+                        "text": text
+                    })
+                    full_text += text + "\n"
+
+        # 清理临时文件
+        if audio_path != input_path and "_converted.wav" in audio_path:
+            try:
+                os.remove(audio_path)
+            except Exception:
+                pass
+
+        # 计算时长
+        duration = segments[-1]["end"] if segments else 0
+
+        return {
+            "success": True,
+            "input_file": os.path.basename(input_path),
+            "model": self.model_name,
+            "output_files": {},
+            "duration_seconds": round(duration, 2),
+            "segments": segments,
+            "text": full_text.strip()
+        }
+
+    def save_outputs(self, result: Dict, output_dir: str):
+        """保存输出文件
+
+        Args:
+            result: 识别结果
+            output_dir: 输出目录
+        """
+        if not result.get("success"):
+            return
+
+        output_dir = Path(output_dir)
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+        basename = Path(result["input_file"]).stem
+
+        # 保存 JSON
+        json_path = output_dir / f"{basename}.json"
+        with open(json_path, "w", encoding="utf-8") as f:
+            json.dump(result, f, ensure_ascii=False, indent=2)
+        print(f"已保存: {json_path}")
+        result["output_files"]["json"] = str(json_path)
+
+        # 保存 TXT
+        txt_path = output_dir / f"{basename}.txt"
+        with open(txt_path, "w", encoding="utf-8") as f:
+            f.write(result.get("text", ""))
+        print(f"已保存: {txt_path}")
+        result["output_files"]["txt"] = str(txt_path)
+
+
+def run_as_background(input_path: str, model: str, output_dir: str):
+    """后台运行识别
+
+    Args:
+        input_path: 输入文件路径
+        model: 模型名称
+        output_dir: 输出目录
+    """
+    # 创建日志文件
+    log_file = os.path.join(output_dir, f"funasr_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log")
+
+    # 重新执行命令（后台模式）
+    cmd = [
+        sys.executable, __file__,
+        "--input", input_path,
+        "--model", model,
+        "--output", output_dir
+    ]
+
+    with open(log_file, "w") as f:
+        process = subprocess.Popen(
+            cmd,
+            stdout=f,
+            stderr=subprocess.STDOUT,
+            start_new_session=True
+        )
+
+    print(f"\n✓ 后台任务已启动 (PID: {process.pid})")
+    print(f"日志文件: {log_file}")
+    print(f"输出目录: {output_dir}")
+    print("\n使用以下命令查看进度:")
+    print(f"  tail -f {log_file}")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="KB FunASR - 本地语音识别工具",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+示例:
+  python funasr.py --input video.mp4
+  python funasr.py --input podcast.mp3 --model paraformer-zh
+  python funasr.py --input video.mp4 --output ./transcripts --background
+        """
+    )
+
+    parser.add_argument("--input", "-i", required=True, help="输入音视频文件路径")
+    parser.add_argument("--model", "-m", default="paraformer-zh",
+                        choices=["paraformer-zh", "paraformer", "sensevoice"],
+                        help="模型名称 (默认: paraformer-zh)")
+    parser.add_argument("--output", "-o", default=None, help="输出目录 (默认: 文件所在目录)")
+    parser.add_argument("--background", "-b", action="store_true", help="后台异步执行")
+
+    args = parser.parse_args()
+
+    # 确定输出目录
+    if args.output:
+        output_dir = args.output
+    else:
+        output_dir = os.path.dirname(args.input) or "."
+
+    # 后台模式
+    if args.background:
+        run_as_background(args.input, args.model, output_dir)
+        return
+
+    # 前台模式
+    transcriber = FunASRTranscriber(model=args.model)
+    result = transcriber.transcribe(args.input)
+
+    if result.get("success"):
+        transcriber.save_outputs(result, output_dir)
+        print(f"\n✓ 处理完成!")
+        print(f"  时长: {result['duration_seconds']} 秒")
+        print(f"  分段数: {len(result['segments'])}")
+    else:
+        print(f"\n✗ 处理失败: {result.get('error')}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

kb-link-parser/SKILL.md

@@ -0,0 +1,43 @@
+# KB Link Parser Skill
+
+**kb-link-parser** - Extract and resolve IDs from various platform URLs.
+
+## Features
+
+- Multi-platform support: Bilibili, YouTube, Zhihu
+- Short link detection: b23.tv and other short links
+- Multiple content types: video, article, up主, playlist, collection
+- Batch processing from file
+- JSON output format
+
+## Commands
+
+```bash
+# Parse single URL
+python link-parser.py --url "https://www.bilibili.com/video/BV1xx411c7mD"
+
+# Batch processing
+python link-parser.py --batch urls.txt --output results.json
+
+# Output format
+python link-parser.py --url "..." --format json
+```
+
+## Output
+
+```json
+{
+  "success": true,
+  "data": {
+    "platform": "bilibili",
+    "type": "video",
+    "id": "BV1xx411c7mD"
+  }
+}
+```
+
+## Installation
+
+```bash
+cp -r kb-link-parser/ ~/skills/
+```

kb-link-parser/link-parser.py

@@ -0,0 +1,637 @@
+#!/usr/bin/env python3
+"""
+Link Parser - Extract and resolve IDs from various platform URLs.
+
+Supports:
+- Bilibili (video, article, up主, collection, series)
+- YouTube (video, channel, playlist)
+- Zhihu (question, answer, user, collection)
+- Xiaohongshu (note, user)
+- Short links (b23.tv, xhslink.com, etc.)
+
+Usage:
+    python link-parser.py --url "https://www.bilibili.com/video/BV1xx411c7mD"
+    python link-parser.py --batch urls.txt --output results.json
+"""
+
+import argparse
+import json
+import re
+import sys
+from typing import Optional, Dict, Any
+from urllib.parse import urlparse
+
+from link_parser_shortlink import ShortLinkResolver
+
+
+class LinkParser:
+    """Main link parser class"""
+
+    # Bilibili patterns
+    BILIBILI_VIDEO_PATTERN = re.compile(r'https?://(?:www\.)?bilibili\.com/video/(BV[a-zA-Z0-9]+)')
+    BILIBILI_VIDEO_SHORT_PATTERN = re.compile(r'https?://b23\.tv/([a-zA-Z0-9]+)')
+    BILIBILI_ARTICLE_PATTERN = re.compile(r'https?://(?:www\.)?bilibili\.com/(?:article|read)/([a-zA-Z0-9]+)')
+    BILIBILI_SPACE_PATTERN = re.compile(r'https?://(?:space\.)?bilibili\.com/(\d+)')
+    BILIBILI_MOBILE_SPACE_PATTERN = re.compile(r'https?://m\.bilibili\.com/space/(\d+)')
+    BILIBILI_MOBILE_OPUS_PATTERN = re.compile(r'https?://m\.bilibili\.com/opus/(\d+)')
+    BILIBILI_FAVLIST_PATTERN = re.compile(r'https?://(?:space\.)?bilibili\.com/\d+/favlist\?fid=(\d+)')
+    BILIBILI_OPUS_PATTERN = re.compile(r'https?://(?:www\.)?bilibili\.com/opus/(\d+)')
+    BILIBILI_SERIES_PATTERN = re.compile(r'https?://(?:space\.)?bilibili\.com/\d+/lists/(\d+)\?type=series')
+    BILIBILI_READLIST_PATTERN = re.compile(r'https?://(?:www\.)?bilibili\.com/read/readlist/(rl[a-zA-Z0-9]+)')
+
+    # YouTube patterns
+    YOUTUBE_VIDEO_PATTERN = re.compile(r'(?:https?://)?(?:www\.)?(?:youtube\.com/watch\?v=|youtu\.be/)([a-zA-Z0-9_-]{11})')
+    YOUTUBE_CHANNEL_PATTERN = re.compile(r'(?:https?://)?(?:www\.)?youtube\.com/channel/([a-zA-Z0-9_-]+)')
+    YOUTUBE_USER_PATTERN = re.compile(r'(?:https?://)?(?:www\.)?youtube\.com/user/([a-zA-Z0-9_-]+)')
+    YOUTUBE_PLAYLIST_PATTERN = re.compile(r'(?:https?://)?(?:www\.)?youtube\.com/playlist\?list=([a-zA-Z0-9_-]+)')
+
+    # Xiaohongshu (小红书) patterns
+    XIAOHONGSHU_SHORT_PATTERN = re.compile(r'https?://xhslink\.com/o/([a-zA-Z0-9]+)')
+    XIAOHONGSHU_NOTE_PATTERN = re.compile(r'https?://(?:www\.)?xiaohongshu\.com/explore/([a-zA-Z0-9]+)')
+    XIAOHONGSHU_NOTE_PATTERN2 = re.compile(r'https?://(?:www\.)?xiaohongshu\.com/notes/([a-zA-Z0-9]+)')
+    XIAOHONGSHU_DISCOVERY_PATTERN = re.compile(r'https?://(?:www\.)?xiaohongshu\.com/discovery/item/([a-zA-Z0-9]+)')
+    XIAOHONGSHU_USER_PATTERN = re.compile(r'https?://(?:www\.)?xiaohongshu\.com/user/profile/(\d+)')
+
+    # Zhihu patterns
+    ZHIHAU_QUESTION_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/question/(\d+)')
+    ZHIHAU_ANSWER_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/question/\d+/answer/(\d+)')
+    ZHIHAU_USER_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/people/([a-zA-Z0-9_-]+)')
+    ZHIHAU_COLUMN_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/column/([a-zA-Z0-9_-]+)')
+    ZHIHAU_COLLECTION_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/collection/(\d+)')
+    # Additional Zhihu patterns
+    ZHIHAU_ZHUANLAN_PATTERN = re.compile(r'https?://zhuanlan\.zhihu\.com/p/(\d+)')
+    ZHIHAU_ORG_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/org/([a-zA-Z0-9_-]+)')
+    ZHIHAU_ORG_ANSWERS_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/org/[a-zA-Z0-9_-]+/answers')
+    ZHIHAU_ORG_VIDEOS_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/org/[a-zA-Z0-9_-]+/zvideos')
+    ZHIHAU_ORG_ASKS_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/org/[a-zA-Z0-9_-]+/asks')
+    ZHIHAU_ORG_POSTS_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/org/[a-zA-Z0-9_-]+/posts')
+    ZHIHAU_ORG_PINS_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/org/[a-zA-Z0-9_-]+/pins')
+    ZHIHAU_ZVIDEO_PATTERN = re.compile(r'https?://(?:www\.)?zhihu\.com/zvideo/(\d+)')
+
+    def __init__(self):
+        self.shortlink_resolver = ShortLinkResolver()
+
+    def resolve_shortlink(self, url: str) -> Optional[str]:
+        """
+        Resolve short link to get final URL.
+
+        Args:
+            url: Short URL to resolve
+
+        Returns:
+            Final URL after resolution, or None if not resolvable
+        """
+        # Check if it's a known short link pattern
+        if self.shortlink_resolver.is_bilibili_short_link(url):
+            # Try to resolve using requests (follows redirects)
+            try:
+                import requests
+                response = requests.head(url, allow_redirects=True, timeout=10)
+                return response.url
+            except Exception:
+                # Fallback: return original URL
+                return url
+
+        # Check if it's Xiaohongshu short link
+        if self.XIAOHONGSHU_SHORT_PATTERN.match(url):
+            try:
+                import requests
+                response = requests.get(url, allow_redirects=True, timeout=10)
+                return response.url
+            except Exception:
+                return url
+
+        return url
+
+    def extract_bilibili_id(self, url: str) -> Optional[Dict[str, str]]:
+        """
+        Extract Bilibili ID from URL.
+
+        Args:
+            url: Bilibili URL
+
+        Returns:
+            Dict with platform, type, and id fields, or None if not matched
+        """
+        # Try series/playlist (must check before space pattern)
+        match = self.BILIBILI_SERIES_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "bilibili",
+                "type": "playlist",
+                "id": match.group(1)
+            }
+
+        # Try readlist (专栏合集)
+        match = self.BILIBILI_READLIST_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "bilibili",
+                "type": "readlist",
+                "id": match.group(1)
+            }
+
+        # Try favlist (收藏夹)
+        match = self.BILIBILI_FAVLIST_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "bilibili",
+                "type": "favlist",
+                "id": match.group(1)
+            }
+
+        # Try opus (动态)
+        match = self.BILIBILI_OPUS_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "bilibili",
+                "type": "opus",
+                "id": match.group(1)
+            }
+
+        # Try mobile opus (移动端动态)
+        match = self.BILIBILI_MOBILE_OPUS_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "bilibili",
+                "type": "opus",
+                "id": match.group(1)
+            }
+
+        # Try video
+        match = self.BILIBILI_VIDEO_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "bilibili",
+                "type": "video",
+                "id": match.group(1)
+            }
+
+        # Try article/read
+        match = self.BILIBILI_ARTICLE_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "bilibili",
+                "type": "article",
+                "id": match.group(1)
+            }
+
+        # Try space (UP主) - must check after specific patterns
+        match = self.BILIBILI_SPACE_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "bilibili",
+                "type": "up主",
+                "id": match.group(1)
+            }
+
+        # Try mobile space (移动端UP主空间)
+        match = self.BILIBILI_MOBILE_SPACE_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "bilibili",
+                "type": "up主",
+                "id": match.group(1)
+            }
+
+        return None
+
+    def extract_youtube_id(self, url: str) -> Optional[Dict[str, str]]:
+        """
+        Extract YouTube ID from URL.
+
+        Args:
+            url: YouTube URL
+
+        Returns:
+            Dict with platform, type, and id fields, or None if not matched
+        """
+        # Try video
+        match = self.YOUTUBE_VIDEO_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "youtube",
+                "type": "video",
+                "id": match.group(1)
+            }
+
+        # Try channel
+        match = self.YOUTUBE_CHANNEL_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "youtube",
+                "type": "channel",
+                "id": match.group(1)
+            }
+
+        # Try user
+        match = self.YOUTUBE_USER_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "youtube",
+                "type": "user",
+                "id": match.group(1)
+            }
+
+        # Try playlist
+        match = self.YOUTUBE_PLAYLIST_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "youtube",
+                "type": "playlist",
+                "id": match.group(1)
+            }
+
+        return None
+
+    def extract_zhihu_id(self, url: str) -> Optional[Dict[str, str]]:
+        """
+        Extract Zhihu ID from URL.
+
+        Args:
+            url: Zhihu URL
+
+        Returns:
+            Dict with platform, type, and id fields, or None if not matched
+        """
+        # Try question
+        match = self.ZHIHAU_QUESTION_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "zhihu",
+                "type": "question",
+                "id": match.group(1)
+            }
+
+        # Try answer
+        match = self.ZHIHAU_ANSWER_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "zhihu",
+                "type": "answer",
+                "id": match.group(1)
+            }
+
+        # Try user
+        match = self.ZHIHAU_USER_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "zhihu",
+                "type": "user",
+                "id": match.group(1)
+            }
+
+        # Try column
+        match = self.ZHIHAU_COLUMN_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "zhihu",
+                "type": "column",
+                "id": match.group(1)
+            }
+
+        # Try collection
+        match = self.ZHIHAU_COLLECTION_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "zhihu",
+                "type": "collection",
+                "id": match.group(1)
+            }
+
+        # Try zhuanlan article (专栏文章)
+        match = self.ZHIHAU_ZHUANLAN_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "zhihu",
+                "type": "zhuanlan",
+                "id": match.group(1)
+            }
+
+        # Try org page (机构页面)
+        match = self.ZHIHAU_ORG_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "zhihu",
+                "type": "org",
+                "id": match.group(1)
+            }
+
+        # Try org answers (机构回答)
+        if self.ZHIHAU_ORG_ANSWERS_PATTERN.match(url):
+            match = self.ZHIHAU_ORG_PATTERN.match(url)
+            if match:
+                return {
+                    "platform": "zhihu",
+                    "type": "org_answers",
+                    "id": match.group(1)
+                }
+
+        # Try org zvideos (机构视频)
+        if self.ZHIHAU_ORG_VIDEOS_PATTERN.match(url):
+            match = self.ZHIHAU_ORG_PATTERN.match(url)
+            if match:
+                return {
+                    "platform": "zhihu",
+                    "type": "org_zvideos",
+                    "id": match.group(1)
+                }
+
+        # Try org asks (机构提问)
+        if self.ZHIHAU_ORG_ASKS_PATTERN.match(url):
+            match = self.ZHIHAU_ORG_PATTERN.match(url)
+            if match:
+                return {
+                    "platform": "zhihu",
+                    "type": "org_asks",
+                    "id": match.group(1)
+                }
+
+        # Try org posts (机构文章)
+        if self.ZHIHAU_ORG_POSTS_PATTERN.match(url):
+            match = self.ZHIHAU_ORG_PATTERN.match(url)
+            if match:
+                return {
+                    "platform": "zhihu",
+                    "type": "org_posts",
+                    "id": match.group(1)
+                }
+
+        # Try org pins (机构想法)
+        if self.ZHIHAU_ORG_PINS_PATTERN.match(url):
+            match = self.ZHIHAU_ORG_PATTERN.match(url)
+            if match:
+                return {
+                    "platform": "zhihu",
+                    "type": "org_pins",
+                    "id": match.group(1)
+                }
+
+        # Try zvideo (知乎视频)
+        match = self.ZHIHAU_ZVIDEO_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "zhihu",
+                "type": "zvideo",
+                "id": match.group(1)
+            }
+
+        return None
+
+    def extract_xiaohongshu_id(self, url: str) -> Optional[Dict[str, str]]:
+        """
+        Extract Xiaohongshu ID from URL.
+
+        Args:
+            url: Xiaohongshu URL
+
+        Returns:
+            Dict with platform, type, and id fields, or None if not matched
+        """
+        # Try short link
+        match = self.XIAOHONGSHU_SHORT_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "xiaohongshu",
+                "type": "note",
+                "id": match.group(1)
+            }
+
+        # Try explore link
+        match = self.XIAOHONGSHU_NOTE_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "xiaohongshu",
+                "type": "note",
+                "id": match.group(1)
+            }
+
+        # Try notes link
+        match = self.XIAOHONGSHU_NOTE_PATTERN2.match(url)
+        if match:
+            return {
+                "platform": "xiaohongshu",
+                "type": "note",
+                "id": match.group(1)
+            }
+
+        # Try discovery item link (resolved from short link)
+        match = self.XIAOHONGSHU_DISCOVERY_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "xiaohongshu",
+                "type": "note",
+                "id": match.group(1)
+            }
+
+        # Try user profile
+        match = self.XIAOHONGSHU_USER_PATTERN.match(url)
+        if match:
+            return {
+                "platform": "xiaohongshu",
+                "type": "user",
+                "id": match.group(1)
+            }
+
+        return None
+
+    def parse(self, url: str) -> Dict[str, Any]:
+        """
+        Parse URL and extract platform-specific ID.
+
+        Args:
+            url: URL to parse
+
+        Returns:
+            Dict with platform, type, id, and original_url fields
+        """
+        result = {
+            "platform": None,
+            "type": None,
+            "id": None,
+            "original_url": url,
+            "resolved_url": None,
+            "success": False,
+            "error": None
+        }
+
+        # First, resolve short links
+        resolved = self.resolve_shortlink(url)
+        if resolved:
+            result["resolved_url"] = resolved
+            url = resolved
+
+        # Try each platform
+        bilibili_result = self.extract_bilibili_id(url)
+        if bilibili_result:
+            result.update(bilibili_result)
+            result["success"] = True
+            return result
+
+        youtube_result = self.extract_youtube_id(url)
+        if youtube_result:
+            result.update(youtube_result)
+            result["success"] = True
+            return result
+
+        zhihu_result = self.extract_zhihu_id(url)
+        if zhihu_result:
+            result.update(zhihu_result)
+            result["success"] = True
+            return result
+
+        # Try Xiaohongshu
+        xiaohongshu_result = self.extract_xiaohongshu_id(url)
+        if xiaohongshu_result:
+            result.update(xiaohongshu_result)
+            result["success"] = True
+            return result
+
+        # Check for short links that need resolution
+        short_links = self.shortlink_resolver.detect_short_links(url)
+        if short_links:
+            result["error"] = "Short link detected. Please provide the full URL."
+            return result
+
+        result["error"] = "Unsupported URL format"
+        return result
+
+
+def format_output(data: Dict[str, Any], output_format: str = "json") -> str:
+    """Format output based on specified format."""
+    if output_format == "json":
+        return json.dumps(data, ensure_ascii=False, indent=2)
+    elif output_format == "compact":
+        return json.dumps(data, ensure_ascii=False, separators=(',', ':'))
+    elif output_format == "text":
+        lines = []
+        for key, value in data.items():
+            if value is not None:
+                lines.append(f"{key}: {value}")
+        return "\n".join(lines)
+    else:
+        return json.dumps(data, ensure_ascii=False, indent=2)
+
+
+def main():
+    """Main entry point."""
+    parser = argparse.ArgumentParser(
+        description="Parse and extract IDs from various platform URLs",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  %(prog)s --url "https://www.bilibili.com/video/BV1xx411c7mD"
+  %(prog)s --url "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
+  %(prog)s --url "https://www.zhihu.com/question/266309957"
+  %(prog)s --batch urls.txt --output results.json
+        """
+    )
+
+    parser.add_argument(
+        "--url", "-u",
+        help="Single URL to parse"
+    )
+
+    parser.add_argument(
+        "--batch", "-b",
+        help="File containing URLs to parse (one per line)"
+    )
+
+    parser.add_argument(
+        "--output", "-o",
+        help="Output file path (for batch mode)"
+    )
+
+    parser.add_argument(
+        "--format", "-f",
+        choices=["json", "compact", "text"],
+        default="json",
+        help="Output format (default: json)"
+    )
+
+    args = parser.parse_args()
+
+    # Check if we're in shortlink help mode
+    if len(sys.argv) == 2 and sys.argv[1] in ['--help', '-h']:
+        parser.print_help()
+        return 0
+
+    if not args.url and not args.batch:
+        parser.print_help()
+        print("\nError: Please provide --url or --batch")
+        return 1
+
+    link_parser = LinkParser()
+
+    if args.url:
+        # Single URL mode
+        parsed = link_parser.parse(args.url)
+
+        # 统一输出格式
+        if parsed["success"]:
+            output = {
+                "success": True,
+                "platform": parsed.get("platform"),
+                "type": parsed.get("type"),
+                "id": parsed.get("id"),
+                "filename": f"{parsed.get('platform')}_{parsed.get('id')}.link.json" if parsed.get("platform") and parsed.get("id") else None,
+                "message": "URL解析成功"
+            }
+        else:
+            output = {
+                "success": False,
+                "error": parsed.get("error", "解析失败"),
+                "detail": f"URL: {args.url}"
+            }
+
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+        return 0 if output["success"] else 1
+
+    elif args.batch:
+        try:
+            with open(args.batch, 'r', encoding='utf-8') as f:
+                urls = [line.strip() for line in f if line.strip()]
+        except Exception as e:
+            print(json.dumps({
+                "success": False,
+                "error": "读取文件失败",
+                "detail": str(e)
+            }, ensure_ascii=False, indent=2))
+            return 1
+
+        results = []
+        for url in urls:
+            parsed = link_parser.parse(url)
+            if parsed["success"]:
+                results.append({
+                    "success": True,
+                    "platform": parsed.get("platform"),
+                    "type": parsed.get("type"),
+                    "id": parsed.get("id"),
+                    "filename": f"{parsed.get('platform')}_{parsed.get('id')}.link.json" if parsed.get("platform") and parsed.get("id") else None
+                })
+            else:
+                results.append({
+                    "success": False,
+                    "error": parsed.get("error", "解析失败"),
+                    "detail": f"URL: {url}"
+                })
+
+        if args.output:
+            with open(args.output, 'w', encoding='utf-8') as f:
+                json.dump(results, f, ensure_ascii=False, indent=2)
+            print(json.dumps({
+                "success": True,
+                "message": f"结果已保存到 {args.output}",
+                "total": len(results)
+            }, ensure_ascii=False, indent=2))
+        else:
+            for result in results:
+                print(json.dumps(result, ensure_ascii=False, indent=2))
+                print("-" * 40)
+
+        return 0
+
+
+if __name__ == '__main__':
+    sys.exit(main())

kb-link-parser/link_parser_shortlink.py

@@ -0,0 +1,296 @@
+#!/usr/bin/env python3
+"""
+Short link resolver for link-parser skill.
+
+Provides utilities for detecting and resolving short links, particularly
+Bilibili short links (b23.tv) which may be blocked by anti-scraping measures.
+
+Supported short link services:
+- Bilibili: b23.tv
+- Generic URL shorteners (detection only)
+"""
+
+import re
+from typing import Optional, Dict, List, Tuple
+from urllib.parse import urlparse
+
+
+class ShortLinkResolver:
+    """Short link detection and resolution handler"""
+
+    # Pattern for Bilibili short links
+    BILIBILI_SHORT_PATTERN = re.compile(r'https?://b23\.tv/([a-zA-Z0-9]+)')
+
+    # Pattern for other short link services
+    OTHER_SHORT_PATTERNS = {
+        'bit.ly': re.compile(r'https?://bit\.ly/\w+'),
+        't.cn': re.compile(r'https?://t\.cn/\w+'),
+        'dwz.cn': re.compile(r'https?://dwz\.cn/\w+'),
+        'goo.gl': re.compile(r'https?://goo\.gl/\w+'),
+        'tinyurl.com': re.compile(r'https?://tinyurl\.com/\w+'),
+    }
+
+    @staticmethod
+    def detect_short_links(text: str) -> List[Dict[str, str]]:
+        """
+        Detect short links in text.
+
+        Args:
+            text: Input text to search
+
+        Returns:
+            List of detected short links with metadata:
+            [
+                {
+                    'url': 'https://b23.tv/xxxxx',
+                    'platform': 'bilibili',
+                    'is_short': True
+                },
+                ...
+            ]
+        """
+        detected = []
+
+        # Check for Bilibili short links
+        for match in ShortLinkResolver.BILIBILI_SHORT_PATTERN.finditer(text):
+            detected.append({
+                'url': match.group(0),
+                'code': match.group(1),
+                'platform': 'bilibili',
+                'is_short': True
+            })
+
+        # Check for other short link services
+        for platform, pattern in ShortLinkResolver.OTHER_SHORT_PATTERNS.items():
+            for match in pattern.finditer(text):
+                detected.append({
+                    'url': match.group(0),
+                    'platform': platform,
+                    'is_short': True
+                })
+
+        return detected
+
+    @staticmethod
+    def is_bilibili_short_link(url: str) -> bool:
+        """
+        Check if URL is a Bilibili short link.
+
+        Args:
+            url: URL to check
+
+        Returns:
+            True if URL is a b23.tv short link
+        """
+        return bool(ShortLinkResolver.BILIBILI_SHORT_PATTERN.match(url))
+
+    @staticmethod
+    def format_short_link_help(short_link: Optional[str] = None) -> str:
+        """
+        Format help message for short links.
+
+        Args:
+            short_link: Optional short link URL to mention
+
+        Returns:
+            Help message
+        """
+        lines = [
+            "Detection: Short link",
+            ""
+        ]
+
+        if short_link:
+            lines.append(f"Short link: {short_link}")
+            lines.append("")
+
+        lines.extend([
+            "Due to anti-scraping protection, short links cannot be automatically resolved.",
+            "Please provide the full URL or the content ID (e.g., BV号 for Bilibili).",
+            "",
+            "Full URL format: https://www.bilibili.com/video/BVxxxxx"
+        ])
+
+        return "\n".join(lines)
+
+    @staticmethod
+    def format_short_link_warning(short_links: List[Dict[str, str]]) -> str:
+        """
+        Format warning message for multiple short links.
+
+        Args:
+            short_links: List of short link metadata
+
+        Returns:
+            Warning message
+        """
+        if not short_links:
+            return ""
+
+        lines = [
+            "Short links detected:",
+            ""
+        ]
+
+        for i, link in enumerate(short_links, 1):
+            platform = link.get('platform', 'unknown')
+            url = link['url']
+
+            if platform == 'bilibili':
+                lines.append(f"  [{i}] Bilibili short link: {url}")
+                lines.append("      Full URL or BV ID required")
+            else:
+                lines.append(f"  [{i}] {platform}: {url}")
+
+            lines.append("")
+
+        return "\n".join(lines)
+
+
+class PlaywrightResolver:
+    """
+    Browser-based short link resolver using Playwright.
+
+    This is an optional implementation for environments where Playwright
+    is available. It can bypass anti-scraping measures by simulating
+    a real browser.
+
+    Requirements:
+        pip install playwright
+        playwright install chromium
+    """
+
+    def __init__(self, headless: bool = True, timeout: int = 10000):
+        """
+        Initialize Playwright resolver.
+
+        Args:
+            headless: Whether to run browser in headless mode
+            timeout: Page navigation timeout in milliseconds
+        """
+        self.headless = headless
+        self.timeout = timeout
+        self._playwright = None
+
+    def _check_playwright_available(self) -> bool:
+        """Check if Playwright is installed and available"""
+        try:
+            from playwright.sync_api import sync_playwright
+            return True
+        except ImportError:
+            return False
+
+    def resolve(self, short_url: str) -> Optional[str]:
+        """
+        Resolve short link using Playwright browser automation.
+
+        Args:
+            short_url: Short URL to resolve
+
+        Returns:
+            Final URL after redirects, or None if resolution fails
+        """
+        if not self._check_playwright_available():
+            raise ImportError(
+                "Playwright not installed. Install with: pip install playwright && playwright install chromium"
+            )
+
+        from playwright.sync_api import sync_playwright
+
+        try:
+            with sync_playwright() as p:
+                # Launch browser
+                browser = p.chromium.launch(headless=self.headless)
+                context = browser.new_context(
+                    user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36',
+                    viewport={'width': 1920, 'height': 1080}
+                )
+                page = context.new_page()
+
+                # Navigate to short URL
+                page.goto(short_url, timeout=self.timeout, wait_until='networkidle')
+
+                # Get final URL
+                final_url = page.url
+
+                # Close browser
+                browser.close()
+
+                return final_url
+
+        except Exception as e:
+            print(f"Playwright resolution failed: {e}")
+            return None
+
+    def resolve_bilibili_short(self, short_url: str) -> Optional[str]:
+        """
+        Resolve Bilibili short link and extract BV ID.
+
+        Args:
+            short_url: Bilibili short URL (e.g., https://b23.tv/xxxxx)
+
+        Returns:
+            Full Bilibili URL with BV ID, or None if resolution fails
+        """
+        final_url = self.resolve(short_url)
+        if not final_url:
+            return None
+
+        # Check if final URL contains BV ID
+        if 'bilibili.com/video/' in final_url:
+            return final_url
+
+        # Extract BV ID if present in URL
+        bv_pattern = re.compile(r'(BV[a-zA-Z0-9]{10})')
+        match = bv_pattern.search(final_url)
+        if match:
+            return f"https://www.bilibili.com/video/{match.group(1)}"
+
+        return None
+
+
+def get_resolver() -> ShortLinkResolver:
+    """
+    Get the default resolver instance.
+
+    Returns:
+        ShortLinkResolver instance
+    """
+    return ShortLinkResolver()
+
+
+def detect_short_links(text: str) -> List[Dict[str, str]]:
+    """
+    Detect short links in text.
+
+    Args:
+        text: Input text to search
+
+    Returns:
+        List of detected short links with metadata
+    """
+    resolver = ShortLinkResolver()
+    return resolver.detect_short_links(text)
+
+
+# CLI interface for testing
+def main():
+    """CLI for testing the short link resolver."""
+    import sys
+
+    if len(sys.argv) < 2:
+        print("Usage: python3 link_parser_shortlink.py 'text to test'")
+        print("\nExamples:")
+        print('  python3 link_parser_shortlink.py "Check this: https://b23.tv/oLBZZfK"')
+        print('  python3 link_parser_shortlink.py "https://bit.ly/xxxxx"')
+        sys.exit(1)
+
+    text = ' '.join(sys.argv[1:])
+    short_links = detect_short_links(text)
+
+    import json
+    print(json.dumps(short_links, ensure_ascii=False, indent=2))
+
+
+if __name__ == '__main__':
+    main()

kb-ocr-processor/SKILL.md

@@ -0,0 +1,66 @@
+# KB OCR Processor Skill
+
+**kb-ocr-processor** - Extract text from video frames using OCR.
+
+## Features
+
+- **视频截帧**: 直接从视频文件截取帧图片
+- **批量处理**: 支持处理帧目录
+- **双引擎**: EasyOCR / Tesseract
+- **置信度评分**: 每帧识别结果带置信度
+- **文本合并**: 自动合并所有帧的识别结果
+
+## Commands
+
+```bash
+# 方式1: 直接从视频截帧并OCR（推荐）
+python ocr_processor.py --video ./video.flv --output ocr.json
+
+# 方式2: 处理已截取的帧目录
+python ocr_processor.py --input ./frames/ --output ocr.json
+
+# 指定截帧帧率（默认1帧/秒）
+python ocr_processor.py --video ./video.flv --fps 2 --output ocr.json
+
+# 同时生成合并的文本文件
+python ocr_processor.py --video ./video.flv --output ocr.json --merge
+
+# 使用 Tesseract 引擎
+python ocr_processor.py --input ./frames/ --engine tesseract --output ocr.json
+```
+
+## Workflow 中的使用
+
+```bash
+# 步骤5: OCR处理（视频无字幕时）
+python ocr_processor.py --video ${CONTENT_VIDEO_PATH} --output ${CONTENT_DIR}/ocr.json --merge
+```
+
+## Output
+
+```json
+{
+  "success": true,
+  "engine": "easyocr",
+  "total_frames": 619,
+  "results": [
+    {
+      "source": "0001.jpg",
+      "text": "字幕文字",
+      "confidence": 0.95
+    }
+  ]
+}
+```
+
+## Installation
+
+```bash
+cp -r kb-ocr-processor/ ~/skills/
+pip install -r requirements.txt
+
+# 依赖说明
+# - easyocr: 主要OCR引擎（自动下载模型）
+# - pytesseract: Tesseract引擎
+# - ffmpeg: 视频截帧（apt install ffmpeg）
+```

kb-ocr-processor/ocr_processor.py

@@ -0,0 +1,360 @@
+#!/usr/bin/env python3
+"""
+OCR Processor - 视频帧文字识别工具
+支持 EasyOCR 和 Tesseract 引擎
+"""
+
+import argparse
+import json
+import os
+import glob
+from pathlib import Path
+from typing import List, Dict, Optional
+import easyocr
+import pytesseract
+from PIL import Image
+
+
+class OCRProcessor:
+    """OCR 文字识别处理器"""
+    
+    def __init__(self, engine: str = "easyocr", languages: List[str] = ["ch_sim", "en"]):
+        """
+        初始化 OCR 处理器
+        
+        Args:
+            engine: OCR 引擎 ("easyocr" 或 "tesseract")
+            languages: 语言列表
+        """
+        self.engine = engine
+        self.languages = languages
+        self.reader = None
+        
+        if engine == "easyocr":
+            self.reader = easyocr.Reader(languages, gpu=False)
+    
+    def process_image(self, image_path: str) -> Dict:
+        """
+        处理单张图片进行 OCR 识别
+        
+        Args:
+            image_path: 图片路径
+            
+        Returns:
+            包含 text 和 confidence 的字典
+        """
+        if not os.path.exists(image_path):
+            return {"text": "", "confidence": 0.0, "error": "File not found"}
+        
+        try:
+            if self.engine == "easyocr":
+                return self._process_with_easyocr(image_path)
+            elif self.engine == "tesseract":
+                return self._process_with_tesseract(image_path)
+            else:
+                return {"text": "", "confidence": 0.0, "error": f"Unknown engine: {self.engine}"}
+        except Exception as e:
+            return {"text": "", "confidence": 0.0, "error": str(e)}
+    
+    def _process_with_easyocr(self, image_path: str) -> Dict:
+        """使用 EasyOCR 处理图片"""
+        results = self.reader.readtext(image_path)
+        
+        if not results:
+            return {"text": "", "confidence": 0.0}
+        
+        texts = []
+        confidences = []
+        
+        for bbox, text, conf in results:
+            texts.append(text)
+            confidences.append(conf)
+        
+        merged_text = "\n".join(texts)
+        avg_confidence = sum(confidences) / len(confidences) if confidences else 0.0
+        
+        return {
+            "text": merged_text,
+            "confidence": round(avg_confidence, 4),
+            "details": [
+                {"text": text, "confidence": round(conf, 4)} 
+                for text, conf in zip(texts, confidences)
+            ]
+        }
+    
+    def _process_with_tesseract(self, image_path: str) -> Dict:
+        """使用 Tesseract 处理图片"""
+        image = Image.open(image_path)
+        text = pytesseract.image_to_string(image, lang="+".join(self.languages))
+        
+        # Tesseract 不直接返回置信度，返回 0.0
+        return {
+            "text": text.strip(),
+            "confidence": 0.0
+        }
+    
+    def process_frames(self, input_dir: str, fps: float = 0.1, progress_file: Optional[str] = None) -> List[Dict]:
+        """
+        处理目录下所有帧图片
+
+        Args:
+            input_dir: 帧图片目录
+            fps: 采样帧率
+            progress_file: 可选，进度文件路径（会实时更新）
+
+        Returns:
+            OCR 结果列表
+        """
+        if not os.path.isdir(input_dir):
+            print(f"Error: Directory not found: {input_dir}")
+            return []
+
+        # 支持的图片格式
+        image_extensions = ["*.jpg", "*.jpeg", "*.png", "*.bmp", "*.webp"]
+        image_files = []
+
+        for ext in image_extensions:
+            image_files.extend(glob.glob(os.path.join(input_dir, ext)))
+            image_files.extend(glob.glob(os.path.join(input_dir, "**", ext), recursive=True))
+
+        # 按文件名排序
+        image_files = sorted(image_files)
+
+        if not image_files:
+            print(f"No images found in {input_dir}")
+            return []
+
+        total = len(image_files)
+        print(f"Found {total} images in {input_dir}")
+        print(f"Processing with {self.engine} engine...")
+
+        def update_progress(current, status="running", last_result=None):
+            """更新进度文件"""
+            if progress_file:
+                import json
+                import time
+                prog = {
+                    "current": current,
+                    "total": total,
+                    "percent": round(current / total * 100, 1) if total > 0 else 0,
+                    "status": status,
+                    "last_result": last_result,
+                    "updated_at": time.strftime("%Y-%m-%d %H:%M:%S")
+                }
+                with open(progress_file, 'w', encoding='utf-8') as f:
+                    json.dump(prog, f, ensure_ascii=False, indent=2)
+
+        # 初始化进度
+        update_progress(0, "started")
+
+        results = []
+        for i, image_path in enumerate(image_files, 1):
+            basename = os.path.basename(image_path)
+            print(f"Processing [{i}/{total}]: {basename}")
+
+            ocr_result = self.process_image(image_path)
+            ocr_result["source"] = basename
+
+            # 记录结果摘要
+            text_count = len(ocr_result.get("text", "").split()) if ocr_result.get("text") else 0
+            last_result = f"✓ {text_count} 处文字" if ocr_result.get("text") else "✗ 无文字"
+
+            results.append(ocr_result)
+
+            # 实时更新进度
+            update_progress(i, "running", last_result)
+
+        # 完成
+        update_progress(total, "completed", f"完成 {total} 张")
+
+        print(f"\n✅ Processed {total} frames")
+        return results
+    
+    def merge_texts(self, results: List[Dict]) -> str:
+        """
+        合并多个 OCR 结果的文本
+        
+        Args:
+            results: OCR 结果列表
+            
+        Returns:
+            合并后的文本
+        """
+        texts = []
+        for result in results:
+            if result.get("text"):
+                texts.append(result["text"])
+        return "\n\n---\n\n".join(texts)
+    
+    def save_results(self, results: List[Dict], output_path: str, merge: bool = False):
+        """
+        保存 OCR 结果到 JSON 文件
+        
+        Args:
+            results: OCR 结果列表
+            output_path: 输出文件路径
+            merge: 是否同时保存合并后的文本文件
+        """
+        output_data = {
+            "engine": self.engine,
+            "total_frames": len(results),
+            "results": results
+        }
+        
+        # 保存 JSON
+        with open(output_path, "w", encoding="utf-8") as f:
+            json.dump(output_data, f, ensure_ascii=False, indent=2)
+        
+        print(f"Results saved to {output_path}")
+        
+        # 如果需要，同时保存合并的文本
+        if merge:
+            text_path = output_path.replace(".json", ".txt")
+            merged_text = self.merge_texts(results)
+            with open(text_path, "w", encoding="utf-8") as f:
+                f.write(merged_text)
+            print(f"Merged text saved to {text_path}")
+
+
+def extract_frames(video_path: str, output_dir: str, fps: float = 1.0) -> str:
+    """
+    使用 ffmpeg 从视频中截取帧图片
+
+    Args:
+        video_path: 视频文件路径
+        output_dir: 输出目录
+        fps: 每秒截取帧数 (默认: 1)
+
+    Returns:
+        帧图片目录路径
+    """
+    import subprocess
+
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # ffmpeg 命令: -i 输入 -vf fps=fps 输出格式
+    cmd = [
+        "ffmpeg", "-y",
+        "-i", video_path,
+        "-vf", f"fps={fps}",
+        "-q:v", "2",  # 质量等级 (2=高质量)
+        str(output_dir / "%04d.jpg")
+    ]
+
+    print(f"🔄 截取视频帧: {video_path}")
+    print(f"   输出目录: {output_dir}")
+
+    try:
+        result = subprocess.run(cmd, capture_output=True, text=True)
+        if result.returncode == 0:
+            # 统计截取的帧数
+            frame_count = len(list(output_dir.glob("*.jpg")))
+            print(f"✅ 截取完成: {frame_count} 帧")
+            return str(output_dir)
+        else:
+            print(f"❌ ffmpeg 错误: {result.stderr}")
+            return ""
+    except FileNotFoundError:
+        print("❌ 错误: 未找到 ffmpeg，请先安装: apt install ffmpeg")
+        return ""
+
+
+def main():
+    """命令行入口"""
+    parser = argparse.ArgumentParser(
+        description="OCR 视频帧文字识别工具",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+示例:
+  # 使用 EasyOCR 处理帧目录
+  python ocr_processor.py --input ./frames/ --output ./ocr.json
+
+  # 直接从视频截帧并 OCR
+  python ocr_processor.py --video ./video.flv --output ./ocr.json
+
+  # 使用 Tesseract 处理
+  python ocr_processor.py --input ./frames/ --output ./ocr.json --engine tesseract
+
+  # 指定截帧帧率
+  python ocr_processor.py --video ./video.flv --output ./ocr.json --fps 2
+
+  # 同时生成合并的文本文件
+  python ocr_processor.py --input ./frames/ --output ./ocr.json --merge
+        """
+    )
+
+    # 输入源：帧目录 或 视频文件（二选一）
+    input_group = parser.add_mutually_exclusive_group(required=True)
+    input_group.add_argument("--input", "-i", help="输入帧图片目录")
+    input_group.add_argument("--video", "-v", help="输入视频文件（会自动截帧）")
+
+    parser.add_argument("--output", "-o", required=True, help="输出 JSON 文件路径")
+    parser.add_argument("--fps", "-f", type=float, default=1.0, help="截帧帧率 (默认: 1帧/秒)")
+    parser.add_argument("--engine", "-e", choices=["easyocr", "tesseract"],
+                       default="easyocr", help="OCR 引擎 (默认: easyocr)")
+    parser.add_argument("--merge", action="store_true", help="同时保存合并的文本文件")
+    parser.add_argument("--languages", "-l", nargs="+",
+                       default=["ch_sim", "en"], help="语言列表")
+    parser.add_argument("--progress", "-p", help="可选：进度文件路径（会实时更新）")
+
+    args = parser.parse_args()
+
+    # 如果指定了视频文件，先截帧
+    input_dir = args.input
+    if args.video:
+        if not os.path.exists(args.video):
+            print(json.dumps({
+                "success": False,
+                "error": f"视频文件不存在: {args.video}"
+            }, ensure_ascii=False, indent=2))
+            return 1
+
+        # 创建临时帧目录
+        frames_dir = Path(args.video).stem + "_frames"
+        input_dir = extract_frames(args.video, frames_dir, fps=args.fps)
+
+        if not input_dir:
+            print(json.dumps({
+                "success": False,
+                "error": "截帧失败"
+            }, ensure_ascii=False, indent=2))
+            return 1
+
+    # 初始化处理器
+    processor = OCRProcessor(engine=args.engine, languages=args.languages)
+
+    # 处理帧
+    results = processor.process_frames(input_dir, fps=args.fps, progress_file=args.progress)
+    
+    if results:
+        # 保存结果
+        processor.save_results(results, args.output, merge=args.merge)
+        
+        # 统一输出格式
+        total_texts = sum(1 for r in results if r.get("text"))
+        avg_conf = sum(r.get("confidence", 0) for r in results) / len(results) if results else 0
+        
+        output = {
+            "success": True,
+            "filename": Path(args.output).name,
+            "filepath": str(Path(args.output).resolve()),
+            "message": f"OCR处理完成",
+            "stats": {
+                "total_frames": len(results),
+                "frames_with_text": total_texts,
+                "avg_confidence": round(avg_conf, 4)
+            }
+        }
+        print(json.dumps(output, ensure_ascii=False, indent=2))
+        return 0
+    else:
+        print(json.dumps({
+            "success": False,
+            "error": "未处理到任何结果"
+        }, ensure_ascii=False, indent=2))
+        return 1
+
+
+if __name__ == "__main__":
+    exit(main())

kb-ocr-processor/requirements.txt

@@ -0,0 +1,3 @@
+easyocr>=1.7.0
+pytesseract>=0.3.10
+Pillow>=9.0.0

kb-whisper-diarization/.env.example

@@ -0,0 +1,9 @@
+# KB Whisper Diarization 环境变量
+
+# Whisper.cpp 安装路径
+WHISPER_CPP_PATH=~/whisper.cpp
+
+# HuggingFace Token（必需，用于下载 pyannote 模型）
+# 获取方式: https://huggingface.co/settings/tokens
+# 注意: 需要先申请 pyannote/speaker-diarization 模型访问权限
+HF_TOKEN=hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

kb-whisper-diarization/SKILL.md

@@ -0,0 +1,127 @@
+# KB Whisper Diarization Skill
+
+**kb-whisper-diarization** - 使用 Whisper + pyannote.audio 实现语音识别和说话者分离。
+
+## 什么时候使用
+
+当用户需要：
+- 区分多个说话者（如访谈、对话）
+- 获取带说话者标签的字幕
+- 分析对话中谁说了什么
+
+## 环境变量
+
+在使用之前，必须检查环境变量是否已经设置。
+
+| 变量名 | 用途 | 示例 |
+|--------|------|------|
+| `WHISPER_CPP_PATH` | whisper.cpp 安装路径 | `~/whisper.cpp` |
+| `HF_TOKEN` | HuggingFace Token | `hf_xxx...` |
+
+### 获取 HuggingFace Token
+
+pyannote.audio 需要从 HuggingFace 下载模型，需要先：
+1. 注册 HuggingFace 账号：https://huggingface.co
+2. 申请模型访问权限：https://huggingface.co/pyannote/speaker-diarization
+3. 创建 Token：https://huggingface.co/settings/tokens
+
+## 使用方法
+
+```bash
+# 基本用法（带说话者分离）
+python whisper_diarization.py --input video.mp4
+
+# 说话者分离
+python whisper_diarization.py --input interview.mp4 --diarize
+
+# 指定说话者数量
+python whisper_diarization.py --input meeting.mp4 --diarize --num-speakers 2
+
+# 指定语言
+python whisper_diarization.py --input video.mp4 --language zh
+```
+
+## 命令参数
+
+| 参数 | 说明 | 默认值 |
+|------|------|--------|
+| `--input` | 输入音视频文件路径 | 必需 |
+| `--output` | 输出目录 | 文件所在目录 |
+| `--diarize` | 启用说话者分离 | False |
+| `--num-speakers` | 估计说话者数量 | None（自动检测） |
+| `--language` | 语言 | 自动检测 |
+| `--model` | Whisper 模型 | base |
+| `--background` | 后台异步执行 | False |
+
+## 模型选项
+
+- **Whisper 模型**: tiny, base, small, medium, large
+- **说话者分离**: pyannote/speaker-diarization (需申请权限)
+
+## 输出文件
+
+对于输入 `interview.mp4`，将生成：
+
+- `interview.mp4.json` - 完整结果（包含说话者标签）
+- `interview.mp4.txt` - 纯文本
+- `interview.mp4.srt` - 字幕格式（带说话者标签）
+
+## 输出格式示例
+
+### JSON 格式
+
+```json
+{
+  "success": true,
+  "input_file": "interview.mp4",
+  "diarization": true,
+  "speakers": ["SPEAKER_00", "SPEAKER_01"],
+  "segments": [
+    {
+      "id": 0,
+      "speaker": "SPEAKER_00",
+      "start": 0.0,
+      "end": 5.2,
+      "text": "大家好，欢迎来到访谈节目"
+    },
+    {
+      "id": 1,
+      "speaker": "SPEAKER_01",
+      "start": 5.2,
+      "end": 10.8,
+      "text": "谢谢邀请，我很荣幸"
+    }
+  ]
+}
+```
+
+### SRT 字幕格式
+
+```
+1
+00:00:00,000 --> 00:00:05,200
+[SPEAKER_00] 大家好，欢迎来到访谈节目
+
+2
+00:00:05,200 --> 00:00:10,800
+[SPEAKER_01] 谢谢邀请，我很荣幸
+```
+
+## 安装
+
+```bash
+# 1. 安装依赖
+pip install pyannote.audio torch
+
+# 2. 设置 HuggingFace Token
+export HF_TOKEN="hf_xxx..."
+
+# 3. 首次运行会自动下载模型
+python whisper_diarization.py --input test.mp4
+```
+
+## 注意事项
+
+- **说话者分离需要申请权限**: https://huggingface.co/pyannote/speaker-diarization
+- **处理时间较长**: 说话者分离会增加处理时间
+- **对立体声效果更好**: 多声道音频的分离效果更佳

kb-whisper-diarization/requirements.txt

@@ -0,0 +1,8 @@
+# KB Whisper Diarization 依赖
+
+# 核心依赖
+torch>=2.0.0
+pyannote.audio>=1.0.0
+
+# 可选: GPU 加速
+# torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

kb-whisper-diarization/whisper_diarization.py

@@ -0,0 +1,339 @@
+#!/usr/bin/env python3
+"""
+KB Whisper Diarization - 语音识别 + 说话者分离
+使用 Whisper 进行语音识别，pyannote.audio 进行说话者分离
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+import tempfile
+from datetime import datetime
+from pathlib import Path
+import re
+
+
+def parse_arguments():
+    """解析命令行参数"""
+    parser = argparse.ArgumentParser(
+        description='KB Whisper Diarization - 语音识别 + 说话者分离',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+示例:
+  python whisper_diarization.py --input video.mp4
+  python whisper_diarization.py --input interview.mp4 --diarize
+  python whisper_diarization.py --input meeting.mp4 --diarize --num-speakers 2
+        """
+    )
+    parser.add_argument('--input', required=True, help='输入音视频文件路径')
+    parser.add_argument('--output', help='输出目录 (默认: 文件所在目录)')
+    parser.add_argument('--diarize', action='store_true', help='启用说话者分离')
+    parser.add_argument('--num-speakers', type=int, help='估计说话者数量')
+    parser.add_argument('--language', help='指定语言 (默认: 自动检测)')
+    parser.add_argument('--model', default='base', choices=['tiny', 'base', 'small', 'medium', 'large'],
+                        help='Whisper 模型 (默认: base)')
+    parser.add_argument('--background', action='store_true', help='后台异步执行')
+    return parser.parse_args()
+
+
+def get_whisper_path():
+    """获取 whisper.cpp 路径"""
+    path = os.environ.get('WHISPER_CPP_PATH')
+    if path:
+        path = os.path.expanduser(path)
+        if os.path.exists(path):
+            return path
+    return os.path.expanduser('~/whisper.cpp')
+
+
+def check_whisper_model(whisper_path, model):
+    """检查 Whisper 模型"""
+    model_path = os.path.join(whisper_path, 'models', f'ggml-{model}.bin')
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(f"Whisper 模型不存在: {model_path}")
+    return model_path
+
+
+def convert_to_wav(input_path, output_dir):
+    """转换为 16kHz WAV"""
+    if input_path.lower().endswith('.wav'):
+        try:
+            result = subprocess.run(
+                ['ffprobe', '-v', 'error', '-show_entries', 'stream=sample_rate',
+                 '-of', 'default=noprint_wrappers=1:nokey=1', input_path],
+                capture_output=True, text=True
+            )
+            if result.returncode == 0 and int(result.stdout.strip()) == 16000:
+                return input_path
+        except Exception:
+            pass
+
+    output_path = os.path.join(output_dir, f"{Path(input_path).stem}_audio.wav")
+    print(f"转换音频: {input_path} -> {output_path}")
+
+    subprocess.run([
+        'ffmpeg', '-y', '-i', input_path,
+        '-acodec', 'pcm_s16le', '-ar', '16000', '-ac', '1',
+        '-loglevel', 'error', output_path
+    ], check=True, capture_output=True)
+
+    print("✓ 音频转换完成")
+    return output_path
+
+
+def run_whisper(whisper_path, audio_path, model, output_dir, language=None):
+    """运行 Whisper 识别"""
+    basename = Path(audio_path).stem.replace('_audio', '')
+    output_file = os.path.join(output_dir, basename)
+
+    cmd = [
+        os.path.join(whisper_path, 'whisper-cli'),
+        '-m', os.path.join(whisper_path, 'models', f'ggml-{model}.bin'),
+        '-f', audio_path,
+        '-of', output_file,
+        '-l', language or 'auto',
+        '--output-txt', '--output-srt', '--output-json',
+    ]
+
+    print(f"执行 Whisper: {' '.join(cmd[:6])} ...")
+
+    result = subprocess.run(
+        cmd, capture_output=True, text=True, cwd=whisper_path, timeout=3600
+    )
+
+    if result.returncode != 0 and not os.path.exists(f"{output_file}.txt"):
+        raise RuntimeError(f"Whisper 失败: {result.stderr}")
+
+    return basename
+
+
+def run_diarization(audio_path, num_speakers=None):
+    """运行 pyannote.audio 说话者分离"""
+    print("\n🎙️ 步骤2: 说话者分离...")
+
+    try:
+        import torch
+        from pyannote.audio import Pipeline
+    except ImportError as e:
+        print(f"✗ 缺少依赖: {e}")
+        print("请安装: pip install pyannote.audio torch")
+        raise
+
+    # 检查 HuggingFace Token
+    hf_token = os.environ.get('HF_TOKEN')
+    if not hf_token:
+        raise RuntimeError("需要设置 HF_TOKEN 环境变量\n"
+                          "获取方式: https://huggingface.co/settings/tokens")
+
+    # 新版 pyannote 需要先登录
+    from huggingface_hub import login
+    print("登录 HuggingFace...")
+    login(token=hf_token)
+
+    print("加载 pyannote 流水线...")
+    pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
+
+    # 设置设备
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"使用设备: {device}")
+    pipeline.to(device)
+
+    # 运行分离
+    print("执行说话者分离...")
+    diarization = pipeline(
+        audio_path,
+        num_speakers=num_speakers
+    )
+
+    # 解析结果
+    speakers = {}
+    for turn, _, speaker in diarization.itertracks(yield_label=True):
+        if speaker not in speakers:
+            speakers[speaker] = []
+        speakers[speaker].append({
+            "start": round(turn.start, 2),
+            "end": round(turn.end, 2)
+        })
+
+    print(f"✓ 检测到 {len(speakers)} 个说话者: {list(speakers.keys())}")
+    return speakers
+
+
+def merge_results(whisper_txt, whisper_srt, diarization, output_dir, basename):
+    """合并 Whisper 和说话者分离结果"""
+    print("\n📝 步骤3: 合并结果...")
+
+    # 读取 Whisper 文本
+    text_segments = []
+    if os.path.exists(whisper_txt):
+        with open(whisper_txt, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        # 简单按行分段
+        for i, line in enumerate(content.strip().split('\n')):
+            if line.strip():
+                text_segments.append({
+                    "id": i,
+                    "text": line.strip(),
+                    "start": i * 3.0,  # 估算
+                    "end": (i + 1) * 3.0
+                })
+
+    # 读取 SRT 获取精确时间
+    srt_segments = []
+    if os.path.exists(whisper_srt):
+        with open(whisper_srt, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        # 解析 SRT
+        pattern = r'(\d+)\n(\d{2}:\d{2}:\d{2},\d{3}) --> (\d{2}:\d{2}:\d{2},\d{3})\n(.+?)(?=\n\n|\Z)'
+        matches = re.findall(pattern, content, re.DOTALL)
+
+        def parse_time(t):
+            h, m, s_ms = t.split(':')
+            s, ms = s_ms.split(',')
+            return float(h) * 3600 + float(m) * 60 + float(s) + float(ms) / 1000
+
+        for idx, num, start, end, text in matches:
+            srt_segments.append({
+                "id": int(idx),
+                "start": parse_time(start),
+                "end": parse_time(end),
+                "text": text.strip()
+            })
+
+    # 为每个 Whisper 段落分配说话者
+    merged_segments = []
+    for seg in srt_segments:
+        speaker = assign_speaker(seg['start'], seg['end'], diarization)
+        merged_segments.append({
+            "id": len(merged_segments),
+            "speaker": speaker,
+            "start": seg['start'],
+            "end": seg['end'],
+            "text": seg['text']
+        })
+
+    # 生成带说话者的 SRT
+    output_srt = os.path.join(output_dir, f"{basename}_diarized.srt")
+    with open(output_srt, 'w', encoding='utf-8') as f:
+        for seg in merged_segments:
+            start = format_time(seg['start'])
+            end = format_time(seg['end'])
+            f.write(f"{seg['id'] + 1}\n")
+            f.write(f"{start} --> {end}\n")
+            f.write(f"[{seg['speaker']}] {seg['text']}\n\n")
+
+    # 生成合并的 TXT
+    output_txt = os.path.join(output_dir, f"{basename}_diarized.txt")
+    with open(output_txt, 'w', encoding='utf-8') as f:
+        for seg in merged_segments:
+            f.write(f"[{seg['speaker']}] {seg['text']}\n")
+
+    # 生成 JSON
+    output_json = os.path.join(output_dir, f"{basename}_diarized.json")
+    json_data = {
+        "success": True,
+        "tool": "whisper-diarization",
+        "diarization": True,
+        "speakers": list(diarization.keys()),
+        "input_file": basename,
+        "output_files": {
+            "txt": f"{basename}_diarized.txt",
+            "srt": f"{basename}_diarized.srt",
+            "json": f"{basename}_diarized.json"
+        },
+        "segments": merged_segments
+    }
+    with open(output_json, 'w', encoding='utf-8') as f:
+        json.dump(json_data, f, ensure_ascii=False, indent=2)
+
+    print(f"✓ 生成文件:")
+    print(f"  - {output_srt}")
+    print(f"  - {output_txt}")
+    print(f"  - {output_json}")
+
+    return json_data
+
+
+def assign_speaker(start, end, diarization):
+    """将时间段分配给说话者"""
+    best_speaker = "UNKNOWN"
+    max_overlap = 0
+
+    for speaker, segments in diarization.items():
+        for seg in segments:
+            overlap = min(end, seg['end']) - max(start, seg['start'])
+            if overlap > max_overlap:
+                max_overlap = overlap
+                best_speaker = speaker
+
+    return best_speaker
+
+
+def format_time(seconds):
+    """秒数转换为 SRT 时间格式"""
+    hours = int(seconds // 3600)
+    minutes = int((seconds % 3600) // 60)
+    secs = int(seconds % 60)
+    ms = int((seconds % 1) * 1000)
+    return f"{hours:02d}:{minutes:02d}:{secs:02d},{ms:03d}"
+
+
+def main():
+    args = parse_arguments()
+
+    input_file = os.path.abspath(args.input)
+    if not os.path.exists(input_file):
+        print(f"✗ 文件不存在: {input_file}")
+        sys.exit(1)
+
+    output_dir = os.path.abspath(args.output) if args.output else os.path.dirname(input_file)
+    os.makedirs(output_dir, exist_ok=True)
+
+    basename = Path(input_file).stem
+
+    print(f"\n📁 输入: {input_file}")
+    print(f"📁 输出: {output_dir}")
+    print(f"🎙️ 说话者分离: {'是' if args.diarize else '否'}")
+
+    try:
+        # 步骤1: Whisper 识别
+        print(f"\n🎙️ 步骤1: 语音识别...")
+        whisper_path = get_whisper_path()
+        check_whisper_model(whisper_path, args.model)
+
+        audio_path = convert_to_wav(input_file, output_dir)
+        run_whisper(whisper_path, audio_path, args.model, output_dir, args.language)
+
+        # 步骤2: 说话者分离
+        if args.diarize:
+            diarization = run_diarization(audio_path, args.num_speakers)
+        else:
+            diarization = {}
+
+        # 步骤3: 合并结果
+        whisper_txt = os.path.join(output_dir, f"{basename}.txt")
+        whisper_srt = os.path.join(output_dir, f"{basename}.srt")
+        result = merge_results(whisper_txt, whisper_srt, diarization, output_dir, basename)
+
+        # 清理临时音频
+        if audio_path != input_path and audio_path.endswith('_audio.wav'):
+            try:
+                os.remove(audio_path)
+            except Exception:
+                pass
+
+        print(f"\n✅ 处理完成!")
+        print(f"   说话者数量: {len(diarization) if args.diarize else 1}")
+        print(f"   段落数量: {len(result['segments'])}")
+
+    except Exception as e:
+        print(f"\n✗ 错误: {e}")
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    main()

kb-whisper/.env.example

@@ -0,0 +1,5 @@
+# KB Whisper 环境变量配置示例
+# 复制此文件为 .env 并填写实际值
+
+# Whisper.cpp 安装路径
+WHISPER_CPP_PATH=~/whisper.cpp

kb-whisper/README.md

@@ -0,0 +1,211 @@
+# KB Whisper - 本地语音识别
+
+基于 Whisper.cpp 的本地语音识别工具，支持将音视频文件转换为文本、字幕和 JSON 格式。
+
+## 功能特性
+
+- 🎯 **本地执行** - 完全离线运行，保护隐私
+- 🚀 **多模型支持** - 从 tiny 到 large，根据需求选择速度/精度
+- 📝 **多格式输出** - JSON、SRT、TXT 三种格式
+- ⚡ **异步执行** - 支持后台运行，不阻塞主代理
+- 🎬 **广泛兼容** - 支持常见音视频格式（MP3、MP4、WAV 等）
+
+## 安装前提
+
+### 1. 安装 whisper.cpp
+
+```bash
+# 克隆仓库
+git clone https://github.com/ggerganov/whisper.cpp.git ~/whisper.cpp
+
+# 编译
+cd ~/whisper.cpp
+make
+
+# 下载模型（至少需要一个模型）
+./models/download-ggml-model.sh tiny
+./models/download-ggml-model.sh base  # 可选
+```
+
+### 2. 配置环境变量
+
+在 `~/.clawdbot/clawdbot.json` 中添加：
+
+```json5
+{
+  skills: {
+    entries: {
+      "kb-whisper": {
+        env: {
+          WHISPER_CPP_PATH: "~/whisper.cpp"
+        }
+      }
+    }
+  }
+}
+```
+
+或通过命令行配置：
+
+```bash
+clawdbot skills config kb-whisper WHISPER_CPP_PATH "~/whisper.cpp"
+```
+
+## 使用方法
+
+### 基本用法
+
+```bash
+# 使用默认 tiny 模型处理
+python whisper.py --input video.mp4
+
+# 使用 base 模型（推荐，平衡速度和精度）
+python whisper.py --input video.mp4 --model base
+
+# 后台异步执行
+python whisper.py --input video.mp4 --background
+```
+
+### 指定输出目录
+
+```bash
+python whisper.py --input video.mp4 --output ./transcripts
+```
+
+### 处理音频文件
+
+```bash
+python whisper.py --input podcast.mp3 --model base
+```
+
+## 输出文件
+
+对于输入 `video.mp4`，将生成以下文件：
+
+```
+output/
+├── video.json   # 详细结果（含时间戳）
+├── video.srt    # 字幕格式
+└── video.txt    # 纯文本
+```
+
+### JSON 格式
+
+```json
+{
+  "success": true,
+  "input_file": "video.mp4",
+  "model": "base",
+  "output_files": {
+    "json": "video.json",
+    "srt": "video.srt",
+    "txt": "video.txt"
+  },
+  "duration_seconds": 120.5,
+  "segments": [
+    {
+      "id": 0,
+      "start": 0.0,
+      "end": 3.2,
+      "text": "这是第一段字幕"
+    },
+    {
+      "id": 1,
+      "start": 3.2,
+      "end": 6.8,
+      "text": "这是第二段字幕"
+    }
+  ]
+}
+```
+
+## 模型对比
+
+| 模型 | 速度 | 精度 | 适用场景 |
+|------|------|------|----------|
+| tiny | ⚡⚡⚡ | ⭐ | 快速预览、实时转录 |
+| base | ⚡⚡ | ⭐⭐⭐ | 日常使用（推荐） |
+| small | ⚡ | ⭐⭐⭐⭐ | 高质量需求 |
+| medium | ⭐ | ⭐⭐⭐⭐⭐ | 专业用途 |
+| large | 🐢 | ⭐⭐⭐⭐⭐ | 最高精度要求 |
+
+## 完整示例
+
+### 示例 1: 转录播客
+
+```bash
+python whisper.py --input podcast.mp3 --model base --background
+```
+
+### 示例 2: 生成视频字幕
+
+```bash
+python whisper.py --input tutorial.mp4 --model small
+```
+
+### 示例 3: 批量处理（配合脚本）
+
+```bash
+#!/bin/bash
+for video in videos/*.mp4; do
+  python whisper.py --input "$video" --model base
+done
+```
+
+## 在 Workflow 中使用
+
+在知识库处理流程中，如果视频没有字幕：
+
+```bash
+# 步骤4: 下载视频后，如果没有字幕
+# 步骤5: 使用 Whisper 生成字幕
+python whisper.py --input ${video_path} --model base --background
+
+# 等待 Whisper 完成，然后继续后续步骤
+```
+
+## 技术细节
+
+- **引擎**: Whisper.cpp（C++ 实现，性能优异）
+- **音频采样率**: 自动转换为 16kHz
+- **语言**: 自动检测（支持 99 种语言）
+- **GPU 支持**: whisper.cpp 支持 CUDA 加速（需编译时启用）
+
+## 故障排查
+
+### 问题: 找不到 whisper.cpp
+
+```bash
+# 检查环境变量
+echo $WHISPER_CPP_PATH
+
+# 手动测试
+~/whisper.cpp/main --help
+```
+
+### 问题: 模型未下载
+
+```bash
+cd ~/whisper.cpp
+./models/download-ggml-model.sh tiny
+```
+
+### 问题: 音频格式不支持
+
+```bash
+# 转换音频格式
+ffmpeg -i input.avi -acodec libmp3lame output.mp3
+python whisper.py --input output.mp3
+```
+
+## 性能参考
+
+在 CPU 环境下（Intel i7）：
+
+- **tiny 模型**: ~1x 实时速度（1分钟音频 ~1分钟处理）
+- **base 模型**: ~0.5x 实时速度（1分钟音频 ~2分钟处理）
+- **small 模型**: ~0.25x 实时速度（1分钟音频 ~4分钟处理）
+
+## 许可证
+
+本技能使用 MIT 许可证，Whisper.cpp 使用 MIT 许可证。

kb-whisper/SKILL.md

@@ -0,0 +1,124 @@
+---
+name: kb-whisper
+description: 本地语音识别，支持音视频转文字（基于 Whisper.cpp）
+---
+
+# KB Whisper Skill
+
+**kb-whisper** - 本地语音识别，使用 Whisper.cpp 将音视频文件转换为文本、字幕和 JSON 格式输出。
+
+## 什么时候使用
+
+当用户需要：
+- 将视频/音频文件转换为文字
+- 生成视频字幕（SRT 格式）
+- 获取带时间戳的详细识别结果
+
+## 环境变量
+
+在使用之前，必须检查环境变量是否已经设置。
+
+| 变量名 | 用途 | 示例 |
+|--------|------|------|
+| `WHISPER_CPP_PATH` | whisper.cpp 安装路径 | `~/whisper.cpp` |
+
+## 使用方法
+
+```bash
+# 基本用法
+python whisper.py --input video.mp4
+
+# 指定模型（默认 tiny）
+python whisper.py --input video.mp4 --model base
+
+# 指定输出目录
+python whisper.py --input video.mp4 --output ./output
+
+# 异步执行（推荐）
+python whisper.py --input video.mp4 --background
+```
+
+## 命令参数
+
+| 参数 | 说明 | 默认值 |
+|------|------|--------|
+| `--input` | 输入音视频文件路径 | 必需 |
+| `--model` | Whisper 模型类型 | `tiny` |
+| `--output` | 输出目录 | 文件所在目录 |
+| `--background` | 后台异步执行 | False |
+
+## 模型选项
+
+- `tiny` - 最小模型，速度快，精度较低（推荐快速识别）
+- `base` - 基础模型，平衡速度和精度（推荐）
+- `small` - 小型模型，精度更高，速度较慢
+- `medium` - 中型模型，高精度
+- `large` - 大型模型，最高精度，速度慢
+
+## 输出文件
+
+对于输入 `video.mp4`，将生成：
+
+- `video.json` - 详细结果（包含时间戳、分段信息）
+- `video.srt` - 字幕格式文件
+- `video.txt` - 纯文本内容
+
+## 输出格式示例
+
+### JSON 格式
+
+```json
+{
+  "success": true,
+  "input_file": "video.mp4",
+  "model": "tiny",
+  "output_files": {
+    "json": "video.json",
+    "srt": "video.srt",
+    "txt": "video.txt"
+  },
+  "duration_seconds": 120.5,
+  "segments": [
+    {
+      "id": 0,
+      "start": 0.0,
+      "end": 3.2,
+      "text": "这是第一段字幕"
+    }
+  ]
+}
+```
+
+## Workflow 中的使用
+
+```bash
+# 步骤4之后：如果已有音频但无字幕，可以使用 whisper
+python whisper.py --input ${video_path} --model base --background
+```
+
+## 安装
+
+```bash
+# 1. 克隆 whisper.cpp
+git clone https://github.com/ggerganov/whisper.cpp.git ~/whisper.cpp
+
+# 2. 编译
+cd ~/whisper.cpp
+make
+
+# 3. 下载模型（tiny）
+./models/download-ggml-model.sh tiny
+
+# 4. 下载更多模型（可选）
+# ./models/download-ggml-model.sh base
+# ./models/download-ggml-model.sh small
+
+# 5. 复制技能到 skills 目录
+cp -r kb-whisper/ ~/skills/
+```
+
+## 注意事项
+
+- **异步执行**: 使用 `--background` 参数时，命令会在后台运行，不阻塞主代理
+- **模型选择**: tiny 模型适合快速识别，base 模型适合一般用途，large 模型适合高精度需求
+- **文件格式**: 支持 MP3、MP4、WAV、FLAC、OGG、M4A 等常见格式

kb-whisper/examples.md

@@ -0,0 +1,273 @@
+# KB Whisper 使用示例
+
+## 快速开始
+
+### 1. 基础使用
+
+```bash
+# 使用默认 tiny 模型转录视频
+python whisper.py --input video.mp4
+
+# 使用 base 模型（推荐）
+python whisper.py --input video.mp4 --model base
+```
+
+### 2. 异步执行
+
+```bash
+# 后台处理，不阻塞主代理
+python whisper.py --input video.mp4 --model base --background
+```
+
+### 3. 指定输出目录
+
+```bash
+python whisper.py --input video.mp4 --output ./transcripts
+```
+
+### 4. 指定语言
+
+```bash
+# 指定中文
+python whisper.py --input video.mp4 --language zh
+
+# 指定英文
+python whisper.py --input video.mp4 --language en
+
+# 指定日语
+python whisper.py --input video.mp4 --language ja
+```
+
+## 实际应用场景
+
+### 场景 1: 转录播客
+
+```bash
+python whisper.py --input podcast.mp3 --model base --background
+```
+
+**输出**:
+- `podcast.json` - 带时间戳的详细结果
+- `podcast.srt` - 字幕格式
+- `podcast.txt` - 纯文本
+
+### 场景 2: 生成视频字幕
+
+```bash
+python whisper.py --input tutorial.mp4 --model small
+```
+
+**用途**:
+- 为教程视频生成字幕
+- 导出 SRT 文件到视频编辑软件
+- 生成多语言字幕版本
+
+### 场景 3: 会议录音转文字
+
+```bash
+python whisper.py --input meeting.mp3 --model base --output ./meetings
+```
+
+**输出示例**:
+```
+meetings/
+├── meeting.json   # 完整 JSON（含时间戳）
+├── meeting.srt    # 字幕格式
+└── meeting.txt    # 纯文本
+```
+
+### 场景 4: 批量处理
+
+创建脚本 `batch_transcribe.sh`:
+
+```bash
+#!/bin/bash
+
+OUTPUT_DIR="./transcripts"
+mkdir -p "$OUTPUT_DIR"
+
+for video in videos/*.mp4; do
+    echo "处理: $video"
+    python whisper.py --input "$video" --output "$OUTPUT_DIR" --model base
+done
+
+echo "批量处理完成!"
+```
+
+```bash
+chmod +x batch_transcribe.sh
+./batch_transcribe.sh
+```
+
+## 在 Workflow 中使用
+
+### 知识库处理流程
+
+在 `kb-workflow` 技能中，如果视频没有字幕：
+
+```bash
+# 步骤 4: 下载视频后
+# 检查是否有字幕文件
+if [ ! -f "${subtitle_file}" ]; then
+    echo "无字幕文件，使用 Whisper 生成..."
+
+    # 使用 Whisper 生成字幕（后台执行）
+    python /path/to/whisper.py \
+        --input "${video_path}" \
+        --model base \
+        --output "${content_dir}" \
+        --background
+
+    # 等待 Whisper 完成
+    sleep 5  # 等待后台任务启动
+    while ! [ -f "${content_dir}/$(basename ${video_path} .mp4).srt" ]; do
+        sleep 2
+    done
+
+    echo "Whisper 处理完成"
+fi
+```
+
+## 输出文件使用
+
+### JSON 格式
+
+```python
+import json
+
+# 读取 JSON 结果
+with open('video.json', 'r', encoding='utf-8') as f:
+    data = json.load(f)
+
+# 访问分段
+for segment in data['segments']:
+    print(f"[{segment['start']:.2f}-{segment['end']:.2f}] {segment['text']}")
+```
+
+### SRT 格式
+
+```bash
+# 直接在视频播放器中使用
+mpv video.mp4 --sub-file=video.srt
+
+# 或转换字幕格式
+ffmpeg -i video.srt video.ass
+```
+
+### TXT 格式
+
+```bash
+# 搜索关键词
+grep "重要信息" video.txt
+
+# 统计字数
+wc -m video.txt
+```
+
+## 性能优化建议
+
+### 1. 根据内容选择模型
+
+```bash
+# 快速预览（短视频、测试）
+python whisper.py --input video.mp4 --model tiny
+
+# 日常使用（播客、会议）
+python whisper.py --input video.mp4 --model base
+
+# 高质量需求（正式内容、多语言）
+python whisper.py --input video.mp4 --model small
+```
+
+### 2. 使用后台执行
+
+```bash
+# 处理长文件时使用后台模式
+python whisper.py --input long_video.mp4 --model base --background
+```
+
+### 3. 指定语言提升准确度
+
+```bash
+# 如果知道内容语言，指定后准确度更高
+python whisper.py --input video.mp4 --language zh
+```
+
+## 故障排查
+
+### 问题 1: whisper.cpp 未找到
+
+```bash
+# 检查环境变量
+echo $WHISPER_CPP_PATH
+
+# 或配置
+clawdbot skills config kb-whisper WHISPER_CPP_PATH "~/whisper.cpp"
+```
+
+### 问题 2: 模型未下载
+
+```bash
+cd ~/whisper.cpp
+./models/download-ggml-model.sh base
+```
+
+### 问题 3: 音频格式不支持
+
+```bash
+# 转换格式
+ffmpeg -i input.avi -acodec libmp3lame output.mp3
+python whisper.py --input output.mp3
+```
+
+### 问题 4: 后台任务未完成
+
+```bash
+# 查看日志
+cat video_whisper.log
+
+# 检查进程
+ps aux | grep whisper
+```
+
+## 集成到其他工具
+
+### 集成到 Python 脚本
+
+```python
+import subprocess
+import json
+
+def transcribe_audio(audio_path, model='base'):
+    """调用 whisper.py 转录音频"""
+    cmd = [
+        'python3', '/path/to/whisper.py',
+        '--input', audio_path,
+        '--model', model
+    ]
+
+    result = subprocess.run(cmd, capture_output=True, text=True)
+    return json.loads(result.stdout)
+
+# 使用
+result = transcribe_audio('podcast.mp3', 'base')
+print(f"识别到 {result['total_segments']} 个片段")
+```
+
+### 集成到 Shell 脚本
+
+```bash
+#!/bin/bash
+
+transcribe() {
+    local file=$1
+    local model=${2:-base}
+
+    echo "转录: $file"
+    python3 whisper.py --input "$file" --model "$model" --background
+}
+
+# 批量处理
+transcribe "video1.mp4"
+transcribe "video2.mp4" "tiny"
+```

kb-whisper/whisper.py

@@ -0,0 +1,403 @@
+#!/usr/bin/env python3
+"""
+KB Whisper - 本地语音识别工具
+基于 Whisper.cpp 的音视频转文字
+
+功能：
+- 自动将视频/音频转换为 16kHz WAV 格式
+- 支持指定语言（默认中文）
+- 输出标准 JSON 格式供 KB Workflow 使用
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+import tempfile
+from pathlib import Path
+import re
+
+
+def parse_arguments():
+    """解析命令行参数"""
+    parser = argparse.ArgumentParser(
+        description='KB Whisper - 本地语音识别工具',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+示例:
+  python whisper.py --input video.mp4
+  python whisper.py --input video.mp4 --model base
+  python whisper.py --input video.mp4 --output ./output --background
+        """
+    )
+    parser.add_argument(
+        '--input',
+        required=True,
+        help='输入音视频文件路径'
+    )
+    parser.add_argument(
+        '--model',
+        default='base',
+        choices=['tiny', 'base', 'small', 'medium', 'large'],
+        help='Whisper 模型类型 (默认: base)'
+    )
+    parser.add_argument(
+        '--output',
+        help='输出目录 (默认: 输入文件所在目录)'
+    )
+    parser.add_argument(
+        '--background',
+        action='store_true',
+        help='后台异步执行'
+    )
+    parser.add_argument(
+        '--language',
+        default='zh',
+        help='指定语言 (默认: zh)，例如: en, zh, ja'
+    )
+
+    return parser.parse_args()
+
+
+def get_whisper_cpp_path():
+    """获取 whisper.cpp 路径"""
+    # 优先从环境变量获取
+    path = os.environ.get('WHISPER_CPP_PATH')
+    if path:
+        path = os.path.expanduser(path)
+        if os.path.exists(path):
+            return path
+
+    # 尝试常见位置
+    common_paths = [
+        os.path.expanduser('~/whisper.cpp'),
+        '/usr/local/bin/whisper',
+        '/opt/whisper.cpp',
+    ]
+
+    for cp in common_paths:
+        if os.path.exists(cp):
+            return cp
+
+    raise FileNotFoundError(
+        "未找到 whisper.cpp。请设置环境变量 WHISPER_CPP_PATH "
+        "或安装到 ~/whisper.cpp\n"
+        "安装方法: git clone https://github.com/ggerganov/whisper.cpp.git ~/whisper.cpp && cd ~/whisper.cpp && make"
+    )
+
+
+def check_model(whisper_path, model):
+    """检查模型是否存在"""
+    model_path = os.path.join(whisper_path, 'models', f'ggml-{model}.bin')
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(
+            f"模型文件不存在: {model_path}\n"
+            f"请运行以下命令下载模型:\n"
+            f"cd {whisper_path} && ./models/download-ggml-model.sh {model}"
+        )
+    return model_path
+
+
+def convert_to_wav(input_path, output_dir):
+    """将音视频转换为 16kHz WAV 格式
+
+    Whisper 需要 WAV 格式的输入文件。
+    """
+    input_path = os.path.abspath(input_path)
+
+    # 如果已经是 WAV 且采样率正确，直接返回
+    if input_path.lower().endswith('.wav'):
+        try:
+            result = subprocess.run(
+                ['ffprobe', '-v', 'error', '-show_entries',
+                 'stream=sample_rate', '-of', 'default=noprint_wrappers=1:nokey=1',
+                 input_path],
+                capture_output=True, text=True
+            )
+            if result.returncode == 0 and int(result.stdout.strip()) == 16000:
+                print(f"输入文件已是 16kHz WAV 格式，直接使用")
+                return input_path
+        except Exception:
+            pass
+
+    # 转换
+    output_path = os.path.join(output_dir, f"{Path(input_path).stem}_audio.wav")
+
+    print(f"转换音频格式: {input_path} -> {output_path}")
+
+    try:
+        subprocess.run([
+            'ffmpeg', '-y', '-i', input_path,
+            '-acodec', 'pcm_s16le',
+            '-ar', '16000',
+            '-ac', '1',
+            '-loglevel', 'error',
+            output_path
+        ], check=True, capture_output=True)
+        print("音频转换完成")
+        return output_path
+    except subprocess.CalledProcessError as e:
+        print(f"音频转换失败: {e}")
+        raise RuntimeError(f"无法转换音频格式: {e}")
+    except FileNotFoundError:
+        raise RuntimeError("未找到 ffmpeg，请安装: sudo apt-get install ffmpeg")
+
+
+def run_whisper(whisper_path, audio_path, model, output_dir, language='zh'):
+    """运行 Whisper 语音识别
+
+    Args:
+        whisper_path: whisper.cpp 目录
+        audio_path: WAV 音频文件路径
+        model: 模型名称
+        output_dir: 输出目录
+        language: 语言代码
+    """
+    # 输出文件名（使用原始输入文件名，不是 audio.wav）
+    # 从 output_dir 和 audio_path 推断原始文件名
+    original_stem = Path(audio_path).stem.replace('_audio', '')
+    output_basename = original_stem
+
+    # 构建命令
+    cmd = [
+        os.path.join(whisper_path, 'whisper-cli'),
+        '-m', os.path.join(whisper_path, 'models', f'ggml-{model}.bin'),
+        '-f', audio_path,
+        '-of', os.path.join(output_dir, output_basename),
+        '-l', language,
+        '--output-txt',
+        '--output-srt',
+        '--output-json',
+    ]
+
+    print(f"执行命令: {' '.join(cmd)}")
+
+    # 运行
+    result = subprocess.run(
+        cmd,
+        capture_output=True,
+        text=True,
+        cwd=whisper_path,
+        timeout=3600  # 1小时超时
+    )
+
+    if result.returncode != 0:
+        # 检查是否有警告但仍成功
+        if "error" in result.stderr.lower() and not os.path.exists(
+            os.path.join(output_dir, f"{output_basename}.txt")
+        ):
+            raise RuntimeError(
+                f"Whisper 执行失败:\n"
+                f"标准输出: {result.stdout}\n"
+                f"错误输出: {result.stderr}"
+            )
+
+    return result
+
+
+def parse_srt_for_duration(srt_file):
+    """从 SRT 文件解析时长"""
+    if not os.path.exists(srt_file):
+        return 0
+
+    with open(srt_file, 'r', encoding='utf-8') as f:
+        content = f.read()
+
+    # 找到最后一个时间戳
+    pattern = r'(\d{2}:\d{2}:\d{2}),\d{3} --> \d{2}:\d{2}:\d{2},\d{3}'
+    matches = re.findall(pattern, content)
+
+    if not matches:
+        return 0
+
+    # 解析最后一个时间戳
+    last_time = matches[-1]
+    h, m, s = last_time.split(':')
+    return int(h) * 3600 + int(m) * 60 + int(s)
+
+
+def create_workflow_json(input_file, output_dir, model, language, duration):
+    """创建标准格式的 JSON 输出，供 KB Workflow 使用"""
+    input_path = Path(input_file)
+    basename = input_path.stem
+
+    txt_file = os.path.join(output_dir, f"{basename}.txt")
+    srt_file = os.path.join(output_dir, f"{basename}.srt")
+    json_file = os.path.join(output_dir, f"{basename}.json")
+
+    # 读取文本内容
+    text_content = ""
+    if os.path.exists(txt_file):
+        with open(txt_file, 'r', encoding='utf-8') as f:
+            text_content = f.read().strip()
+
+    # 解析 SRT 获取分段
+    segments = []
+    if os.path.exists(srt_file):
+        with open(srt_file, 'r', encoding='utf-8') as f:
+            content = f.read()
+
+        # SRT 格式: 序号 -> 时间 -> 文本
+        pattern = r'(\d+)\n(\d{2}:\d{2}:\d{2},\d{3}) --> (\d{2}:\d{2}:\d{2},\d{3})\n(.+?)(?=\n\n|\Z)'
+        matches = re.findall(pattern, content, re.DOTALL)
+
+        def parse_time(t):
+            h, m, s_ms = t.split(':')
+            s, ms = s_ms.split(',')
+            return float(h) * 3600 + float(m) * 60 + float(s) + float(ms) / 1000
+
+        for idx, (num, start, end, text) in enumerate(matches):
+            segments.append({
+                "id": idx,
+                "start": round(parse_time(start), 2),
+                "end": round(parse_time(end), 2),
+                "text": text.strip()
+            })
+
+    # 构建 JSON
+    workflow_json = {
+        "success": True,
+        "tool": "whisper",
+        "model": model,
+        "language": language,
+        "input_file": str(input_path),
+        "output_files": {
+            "txt": f"{basename}.txt",
+            "srt": f"{basename}.srt",
+            "json": f"{basename}.json"
+        },
+        "duration_seconds": duration,
+        "segments": segments,
+        "text": text_content
+    }
+
+    # 写入 JSON
+    with open(json_file, 'w', encoding='utf-8') as f:
+        json.dump(workflow_json, f, ensure_ascii=False, indent=2)
+
+    return workflow_json
+
+
+def main():
+    """主函数"""
+    args = parse_arguments()
+
+    # 检查输入文件
+    input_file = os.path.abspath(args.input)
+    if not os.path.exists(input_file):
+        print(f"错误: 输入文件不存在: {input_file}", file=sys.stderr)
+        sys.exit(1)
+
+    # 获取输出目录
+    if args.output:
+        output_dir = os.path.abspath(args.output)
+    else:
+        output_dir = os.path.dirname(input_file)
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    # 获取 whisper.cpp 路径
+    try:
+        whisper_path = get_whisper_cpp_path()
+        print(f"✓ Whisper.cpp 路径: {whisper_path}")
+    except FileNotFoundError as e:
+        print(f"✗ 错误: {e}", file=sys.stderr)
+        sys.exit(1)
+
+    # 检查模型
+    try:
+        check_model(whisper_path, args.model)
+        print(f"✓ 使用模型: {args.model}")
+    except FileNotFoundError as e:
+        print(f"✗ 错误: {e}", file=sys.stderr)
+        sys.exit(1)
+
+    # 后台执行
+    if args.background:
+        print("启动后台处理...")
+        log_file = os.path.join(output_dir, f"{Path(input_file).stem}_whisper.log")
+
+        with open(log_file, 'w') as log:
+            subprocess.Popen(
+                [sys.executable, __file__,
+                 '--input', input_file,
+                 '--model', args.model,
+                 '--output', output_dir,
+                 '--language', args.language],
+                stdout=log,
+                stderr=log,
+                start_new_session=True
+            )
+
+        print(f"✓ 后台任务已启动")
+        print(f"  日志: {log_file}")
+        print(f"  输出: {output_dir}")
+
+        result = {
+            "success": True,
+            "background": True,
+            "status": "running",
+            "log_file": log_file,
+            "output_dir": output_dir
+        }
+        print(json.dumps(result, ensure_ascii=False, indent=2))
+        return
+
+    # 前台执行
+    print(f"\n📁 处理文件: {input_file}")
+    print(f"📁 输出目录: {output_dir}")
+
+    try:
+        # 1. 转换为 WAV
+        print(f"\n🔄 步骤1: 转换音频格式...")
+        audio_path = convert_to_wav(input_file, output_dir)
+
+        # 2. 运行 Whisper
+        print(f"\n🎙️ 步骤2: 语音识别 (语言: {args.language})...")
+        run_whisper(whisper_path, audio_path, args.model, output_dir, args.language)
+
+        # 3. 清理临时 WAV 文件
+        if audio_path != input_file and audio_path.endswith('_audio.wav'):
+            try:
+                os.remove(audio_path)
+                print("✓ 清理临时文件")
+            except Exception:
+                pass
+
+        # 4. 获取时长并创建 JSON
+        print(f"\n📝 步骤3: 生成输出文件...")
+        basename = Path(input_file).stem
+        srt_file = os.path.join(output_dir, f"{basename}.srt")
+        duration = parse_srt_for_duration(srt_file)
+
+        workflow_json = create_workflow_json(
+            input_file, output_dir, args.model, args.language, duration
+        )
+
+        # 输出结果
+        print(f"\n✅ 处理完成!")
+        print(f"   时长: {duration} 秒")
+        print(f"   分段数: {len(workflow_json['segments'])}")
+        print(f"\n📄 输出文件:")
+        for fmt in ['txt', 'srt', 'json']:
+            fpath = os.path.join(output_dir, f"{basename}.{fmt}")
+            if os.path.exists(fpath):
+                size = os.path.getsize(fpath)
+                print(f"   • {fmt.upper()}: {fpath} ({size:,} bytes)")
+
+        print(f"\n📋 Workflow JSON:")
+        print(json.dumps({
+            "success": workflow_json["success"],
+            "tool": "whisper",
+            "text_length": len(workflow_json["text"]),
+            "segments_count": len(workflow_json["segments"])
+        }, ensure_ascii=False, indent=2))
+
+    except Exception as e:
+        print(f"\n✗ 错误: {e}", file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    main()

kb-workflow/.env

@@ -0,0 +1,14 @@
+# KB Workflow 环境变量配置
+export KB_BASE_PATH="~/work/knowledge-base"
+
+# 可选: B站 Cookie（用于下载会员视频）
+# export BILIBILI_COOKIE="SESSDATA=xxx; bili_jct=xxx"
+
+# 可选: AI 分析配置
+# export AI_API_KEY="your-api-key"
+# export AI_PROVIDER="openai"  # 或 zhipu, minimax
+# export AI_MODEL="gpt-4"
+
+# 可选: Git 配置
+# export KB_GIT_USER="Your Name"
+# export KB_GIT_EMAIL="your@email.com"

kb-workflow/.env.example

@@ -0,0 +1,14 @@
+# KB Workflow 环境变量配置
+export KB_BASE_PATH="~/work/knowledge-base"
+
+# 可选: B站 Cookie（用于下载会员视频）
+# export BILIBILI_COOKIE="SESSDATA=xxx; bili_jct=xxx"
+
+# 可选: AI 分析配置
+# export AI_API_KEY="your-api-key"
+# export AI_PROVIDER="openai"  # 或 zhipu, minimax
+# export AI_MODEL="gpt-4"
+
+# 可选: Git 配置
+# export KB_GIT_USER="Your Name"
+# export KB_GIT_EMAIL="your@email.com"

kb-workflow/README.md

@@ -0,0 +1,247 @@
+# KB Workflow 环境安装脚本
+
+一键检查并安装 kb-workflow 知识库工作流所需的全部环境依赖。
+
+## 功能特性
+
+- ✅ 自动检测系统类型 (Ubuntu/Debian/Fedora/Arch)
+- ✅ 检查系统依赖 (Python3, pip3, ffmpeg, tesseract, git)
+- ✅ 检查 Python 依赖包 (httpx, aiohttp, playwright, easyocr 等)
+- ✅ 自动创建知识库目录结构
+- ✅ 生成环境变量配置示例
+- ✅ 汇总显示所有需要执行的安装命令
+
+## 使用方法
+
+### 运行检查脚本
+
+```bash
+cd /home/starmen1265/.openclaw/workspace/skills
+./install-kb-workflow.sh
+```
+
+脚本会检查所有依赖，并显示需要执行的安装命令。
+
+### 示例输出
+
+```
+========================================
+第一步: 检查系统依赖
+========================================
+[SUCCESS] Python3 已安装: Python 3.12.3
+[WARN] pip3 未安装
+[WARN] ffmpeg 未安装
+...
+
+========================================
+需要安装的系统依赖
+========================================
+
+检测到以下依赖未安装:
+  - python3-pip
+  - ffmpeg
+  - tesseract-ocr
+
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+请运行以下命令安装:
+━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
+
+sudo apt-get update
+sudo apt-get install -y python3-pip ffmpeg tesseract-ocr tesseract-ocr-chi-sim tesseract-ocr-eng
+```
+
+## 依赖清单
+
+### 系统依赖
+
+| 依赖 | 用途 | 安装命令 |
+|------|------|----------|
+| **python3** | Python 运行时 | `sudo apt-get install python3` |
+| **python3-pip** | Python 包管理器 | `sudo apt-get install python3-pip` |
+| **ffmpeg** | 视频处理（OCR 截帧） | `sudo apt-get install ffmpeg` |
+| **tesseract-ocr** | OCR 引擎（备用方案） | `sudo apt-get install tesseract-ocr` |
+| **git** | 版本控制（归档提交） | `sudo apt-get install git` |
+
+### Tesseract 语言包
+
+```bash
+# Ubuntu/Debian
+sudo apt-get install tesseract-ocr-chi-sim tesseract-ocr-chi-tra tesseract-ocr-eng
+
+# Fedora/RHEL
+sudo dnf install tesseract-langpack-chi_sim tesseract-langpack-chi_tra tesseract-langpack-eng
+```
+
+### Python 依赖
+
+```bash
+# 基础 HTTP 库
+pip3 install --user httpx aiohttp requests
+
+# 图像和 OCR
+pip3 install --user Pillow easyocr pytesseract
+
+# 浏览器自动化
+pip3 install --user playwright
+python3 -m playwright install chromium
+```
+
+## 目录结构
+
+安装完成后会创建以下目录结构：
+
+```
+~/work/knowledge-base/           # 知识库根目录（可配置）
+├── archive/                     # 归档目录
+│   ├── pending/                # 待处理
+│   └── subscriptions/          # 订阅信息
+├── metadata/                    # 元数据文件
+├── content/                     # 内容文件
+│   ├── downloads/              # 视频下载
+│   └── subtitles/              # 字幕文件
+└── analyzed/                    # 深度分析报告
+```
+
+## 环境变量
+
+### 必需
+- `KB_BASE_PATH` - 知识库根目录（默认: `~/work/knowledge-base`）
+
+### 可选
+- `BILIBILI_COOKIE` - B站 Cookie，用于下载会员视频
+- `AI_API_KEY` - AI API 密钥，用于深度内容分析
+- `AI_PROVIDER` - AI 提供商（openai/zhipu/minimax）
+- `AI_MODEL` - AI 模型名称（如 gpt-4）
+- `AI_BASE_URL` - 自定义 API 端点
+- `KB_GIT_USER` - Git 用户名（用于归档提交）
+- `KB_GIT_EMAIL` - Git 邮箱（用于归档提交）
+- `KB_GIT_REMOTE` - Git 远程仓库地址
+
+### 配置方法
+
+将以下内容添加到 `~/.bashrc` 或 `~/.zshrc`：
+
+```bash
+# KB Workflow 配置
+export KB_BASE_PATH="~/work/knowledge-base"
+
+# 可选：B站 Cookie
+export BILIBILI_COOKIE="SESSDATA=xxx; bili_jct=xxx"
+
+# 可选：AI 配置
+export AI_API_KEY="your-api-key"
+export AI_PROVIDER="openai"
+export AI_MODEL="gpt-4"
+```
+
+然后重新加载配置：
+```bash
+source ~/.bashrc  # 或 source ~/.zshrc
+```
+
+## 故障排除
+
+### 1. pip3 命令不存在
+
+```bash
+# 方法 1: 安装 pip3
+sudo apt-get install python3-pip
+
+# 方法 2: 使用 python3 -m pip
+python3 -m pip install --user <package>
+```
+
+### 2. Playwright 浏览器安装失败
+
+```bash
+# 手动安装 Chromium
+python3 -m playwright install chromium
+
+# 如果仍然失败，尝试安装依赖
+sudo apt-get install libnss3 libatk-bridge2.0-0 libxss1 libgtk-3-0
+```
+
+### 3. EasyOCR 安装失败
+
+EasyOCR 依赖 PyTorch，可能需要较长时间安装：
+
+```bash
+# 先安装 PyTorch
+pip3 install --user torch torchvision --index-url https://download.pytorch.org/whl/cpu
+
+# 再安装 easyocr
+pip3 install --user easyocr
+```
+
+### 4. 权限问题
+
+如果使用 `--user` 安装的包无法找到，检查 PATH：
+
+```bash
+# 查看用户安装路径
+python3 -m site --user-base
+
+# 添加到 PATH（添加到 ~/.bashrc）
+export PATH="$PATH:$(python3 -m site --user-base)/bin"
+```
+
+### 5. OCR 中文识别问题
+
+确保已安装中文语言包：
+
+```bash
+# 检查已安装的语言包
+tesseract --list-langs
+
+# 应该能看到 chi_sim 和 chi_tra
+```
+
+## 更新
+
+如果技能更新了依赖，可以重新运行检查脚本：
+
+```bash
+./install-kb-workflow.sh
+```
+
+## 子技能清单
+
+| 技能 | 路径 | 功能 |
+|------|------|------|
+| kb-workflow | `kb-workflow/kb_workflow.py` | 主流程协调 |
+| kb-link-parser | `kb-link-parser/link-parser.py` | 链接解析 |
+| kb-bilibili-metadata | `kb-bilibili-metadata/bilibili_metadata.py` | B站元数据提取 |
+| kb-bilibili-content | `kb-bilibili-content/bilibili_content.py` | B站内容下载 |
+| kb-ocr-processor | `kb-ocr-processor/ocr_processor.py` | 视频 OCR 识别 |
+| kb-deep-analyzer | `kb-deep-analyzer/deep_analyzer.py` | AI 深度分析 |
+
+## 使用 kb-workflow
+
+安装完成后，可以通过以下方式使用：
+
+### 方式 1：通过主代理
+
+直接向主代理发送消息：
+```
+添加到知识库 https://www.bilibili.com/video/BVxxxxx
+```
+
+### 方式 2：手动运行子技能
+
+```bash
+# 1. 链接解析
+python3 kb-link-parser/link-parser.py --url "https://www.bilibili.com/video/BVxxxxx"
+
+# 2. 元数据提取
+python3 kb-bilibili-metadata/bilibili_metadata.py --id "BVxxxxx" --type video
+
+# 3. 内容下载
+python3 kb-bilibili-content/bilibili_content.py --id "BVxxxxx" --type subtitle
+
+# 4. 深度分析
+python3 kb-deep-analyzer/deep_analyzer.py --metadata meta.json --content content.json --output analysis.md
+```
+
+## 许可证
+
+MIT License

kb-workflow/SKILL.md

@@ -0,0 +1,360 @@
+---
+name: kb-workflow
+description: 当收到"添加到知识库 + 链接" 或"保存到知识库 + 链接"时使用
+---
+
+# KB Workflow
+
+## 什么时候使用
+
+当用户发送包含以下模式的消息时，此技能自动触发：
+
+| 触发词 | 行为 | 示例 |
+|--------|------|------|
+| `添加到知识库` + 链接 | 添加到知识库并处理 | "添加到知识库 https://www.bilibili.com/video/BVxxx" |
+| `保存到知识库` + 链接 | 同上 | "保存到知识库 https://www.bilibili.com/video/BVxxx" |
+
+## 环境变量
+
+### 执行前必须检查
+
+在执行 workflow 之前，必须检查环境变量是否已经设置。
+
+**必须的环境变量**（缺失则报错退出）：
+
+配置方式：将变量写入 `~/.kb_env` 文件：
+```bash
+export AI_API_KEY="your-api-key"
+export AI_PROVIDER="zhipu"
+export AI_MODEL="glm-5"
+export AI_BASE_URL="https://open.bigmodel.cn/api/coding/paas/v4"
+```
+
+**可选的环境变量**：
+| 变量名 | 用途 | 示例 |
+|--------|------|------|
+| `BILIBILI_COOKIE` | 下载会员视频 | `SESSDATA=xxx; bili_jct=xxx` |
+| `KB_GIT_USER` | Git提交用户名 | `Your Name` |
+| `KB_GIT_EMAIL` | Git提交邮箱 | `your@email.com` |
+
+### 检查命令
+
+```bash
+# 检查必须的环境变量
+echo "AI_API_KEY: ${AI_API_KEY:0:10}..."
+echo "AI_PROVIDER: $AI_PROVIDER"
+echo "AI_MODEL: $AI_MODEL"
+echo "AI_BASE_URL: $AI_BASE_URL"
+
+# 验证方式1: 检查变量是否存在
+if [ -z "$AI_API_KEY" ]; then
+    echo "❌ AI_API_KEY 未设置"
+    exit 1
+fi
+
+# 验证方式2: 执行测试调用
+python3 -c "
+import os
+if not os.environ.get('AI_API_KEY'):
+    raise RuntimeError('AI_API_KEY 未设置')
+print('✅ 环境变量检查通过')
+"
+```
+
+
+## Workflow 执行方式
+
+主代理直接执行以下步骤，无需启动 kb-workflow 子代理：
+
+1. 主代理启动子代理调用 kb-link-parser
+2. 主代理启动子代理调用 kb-平台-metadata
+3. 主代理启动子代理调用 kb-平台-content
+4. 主代理启动子代理调用 kb-ocr-processor（如需要）
+5. 主代理启动子代理调用 kb-deep-analyzer
+6. 主代理执行 `kb_workflow.py --archive-file` 归档
+
+
+## Workflow 执行步骤
+
+当触发后，按照以下步骤执行：
+
+### 步骤 1: 环境变量检查
+
+**❌ 禁止跳过此步骤！必须先检查环境变量。**
+
+```bash
+# 0. 从 ~/.kb_env 加载环境变量（专门的知识库配置文件，不受 bashrc 交互式检查影响）
+if [ -f ~/.kb_env ]; then
+    source ~/.kb_env
+fi
+
+# 1. 检查必须的环境变量是否存在
+if [ -z "$AI_API_KEY" ] || [ -z "$AI_PROVIDER" ] || [ -z "$AI_MODEL" ] || [ -z "$AI_BASE_URL" ]; then
+    echo "❌ 错误：必须的环境变量未设置"
+    echo "请配置以下环境变量："
+    echo "  - AI_API_KEY"
+    echo "  - AI_PROVIDER (zhipu/openai/minimax)"
+    echo "  - AI_MODEL"
+    echo "  - AI_BASE_URL"
+    exit 1
+fi
+
+echo "✅ 环境变量检查通过"
+echo "   AI_PROVIDER: $AI_PROVIDER"
+echo "   AI_MODEL: $AI_MODEL"
+```
+
+**如果检查不通过，必须**：
+1. 报错提示用户
+2. 退出，不执行后续步骤
+3. 等待用户配置好环境变量后重新执行
+
+## ⚠️ 严格失败处理原则
+
+> **深度分析步骤必须成功，否则整个流程失败**
+
+在执行工作流时，必须遵守以下原则：
+1. **深度分析不可跳过**：kb-deep-analyzer 是必须成功执行的步骤
+2. **失败即终止**：如果深度分析返回 `success: false`，必须立即停止并汇报失败
+3. **禁止降级处理**：不能使用预设模板或简单摘要来"假装成功"
+4. **每次执行都要使用 AI**：必须有真实的 AI API 调用记录
+
+### 步骤 2: 链接解析
+
+1. 获取目录路径：
+```bash
+python3 kb_workflow.py --pending
+```
+
+2. 启动子代理调用 kb-link-parser：
+```bash
+python3 /path/to/link-parser.py --url "[URL]"
+```
+
+3. 解析输出 JSON，提取 filename 和 platform
+
+### 步骤 3: 元数据提取
+
+1. 获取元数据目录路径：
+```bash
+python3 kb_workflow.py --metadata
+# 返回: {"path": "/home/admin/work/knowledge-base/metadata"}
+```
+
+2. 构建输出文件路径：`${metadata_dir}/${platform}_${id}.meta.json`
+
+3. 启动子代理调用对应平台的 metadata 技能（传入 `--output`）：
+```bash
+# Bilibili
+python3 /path/to/bilibili_metadata.py --id "[ID]" --type video --output "${metadata_dir}/bilibili_[ID].meta.json"
+
+# Zhihu
+python3 /path/to/zhihu_metadata.py --id "[ID]" --type question --output "${metadata_dir}/zhihu_[ID].meta.json"
+```
+
+4. 解析输出 JSON，提取 filename
+
+### 步骤 4: 内容下载
+
+1. 获取目录路径：
+```bash
+python3 kb_workflow.py --content_downloads
+python3 kb_workflow.py --content
+```
+
+2. 启动子代理调用对应平台的 content 技能：
+```bash
+# Bilibili 字幕下载（自动检测是否有官方字幕）
+python3 /path/to/bilibili_content.py --id "[ID]" --type subtitle-download --output [content_dir]
+
+# 返回 JSON 包含 has_subtitle 字段
+```
+
+3. 解析输出 JSON：
+- **has_subtitle = true**: 有官方字幕，直接进入步骤 6
+- **has_subtitle = false**: 没有官方字幕，继续步骤 5
+
+> ⚠️ **重要提示**：没有找到官方字幕是**正常的**！很多视频没有自动生成的字幕。不要因为没有字幕就停止工作流，继续执行 Whisper 步骤即可。
+
+### 步骤 5: 语音识别转文字（仅当没有官方字幕时执行）
+
+> ⚠️ **重要**：此步骤仅在**没有官方字幕**时执行。如果有官方字幕，**跳过此步骤**。
+
+此步骤使用 Whisper 进行语音识别：
+- **严禁跳过**：必须等待识别完成
+- **定时汇报**：每2-3分钟向用户汇报进度
+- **超时时间**：30分钟
+- **耐心等待**：不要中途启动其他步骤，必须串行执行
+
+1. 获取目录路径：
+```bash
+python3 kb_workflow.py --content
+```
+
+2. 启动子代理调用 kb-whisper（传入视频路径）：
+```bash
+# 必须传入 --output 完整路径
+python3 /path/to/whisper.py --input [video_path] --model base --output [content_dir]
+```
+
+3. 等待 Whisper 完成（定时汇报进度）
+
+4. 解析输出 JSON，生成 content 文件供步骤6使用
+
+### 步骤 6: 深度分析
+
+1. 获取目录路径：
+```bash
+python3 kb_workflow.py --analyzed
+```
+
+2. 准备 content JSON 文件（包含 subtitle/description）
+
+3. 如果有官方字幕，使用官方字幕的 SRT 文件
+4. 如果没有官方字幕，使用 Whisper 识别的结果
+
+5. 启动子代理调用 kb-deep-analyzer（使用 g5 模型）：
+```bash
+python3 /path/to/deep_analyzer.py --metadata [meta_path] --content [content_path] --output [analyzed_dir]/[filename].md --provider zhipu --model glm-5
+```
+
+**环境变量设置**（在子代理中设置）：
+- `AI_PROVIDER=zhipu`
+- `AI_MODEL=glm-5`
+
+6. 解析输出 JSON，检查 `success` 字段：
+
+> ⚠️ **深度分析失败 = 整个流程失败**
+> - 如果 `success = true`：继续步骤 7（归档）
+> - 如果 `success = false`：**立即终止流程**，向用户汇报失败原因
+> - **严禁**继续执行后续步骤或使用降级内容"假装成功"
+
+### 步骤 7: 归档
+
+运行归档命令：
+```bash
+python3 kb_workflow.py --archive-file "[分析报告文件路径]"
+```
+
+### 步骤 8: 汇报完成
+
+向用户汇报：
+- 视频标题
+- 处理阶段完成情况
+- 最终文件路径
+- Git commit 信息
+
+
+## 子技能清单
+
+| 步骤 | 子技能 | 命令示例 | 输出 | 备注 |
+|------|--------|----------|------|------|
+| 2 | kb-link-parser | `--url "BVxxx"` | `{p}_{id}.link.json` | |
+| 3 | kb-bilibili-metadata | `--id "BVxxx" --type video` | `{p}_{id}.meta.json` | |
+| 3 | kb-zhihu-metadata | `--id "xxx" --type question` | `{p}_{id}.meta.json` | |
+| 4 | kb-bilibili-content | `--id "BVxxx" --type subtitle-download` | `{p}_{id}.srt` | 如果没有字幕是正常的 |
+| 4 | kb-bilibili-content | `--id "BVxxx" --type video` | `{p}_{id}.mp4` | 如果需要下载视频 |
+| 5 | kb-whisper | `--input xxx.mp4 --model base` | `{p}_{id}.json`, `{p}_{id}.txt` | 仅当没有官方字幕时执行 |
+| 6 | kb-deep-analyzer | `--metadata x.meta.json --content x.json --output x.md --provider zhipu --model glm-5` | `{p}_{id}.md` | **使用 g5 模型，必须成功** |
+
+## 条件分支逻辑
+
+```
+步骤3: 获取元数据
+   ↓
+步骤4: 下载字幕 (--type subtitle-download)
+   ↓
+┌─────────────────────────────────────┐
+│ has_subtitle = true?                │
+├─────────────────────────────────────┤
+│ 是 → 直接进入步骤6（深度分析）       │
+│ 否 → 进入步骤5（Whisper语音识别）    │
+└─────────────────────────────────────┘
+           ↓
+步骤6: 深度分析
+   ↓
+┌─────────────────────────────────────┐
+│ kb-deep-analyzer 执行结果           │
+├─────────────────────────────────────┤
+│ success = true  → 步骤7（归档）     │
+│ success = false → ❌ 整个流程失败    │
+└─────────────────────────────────────┘
+```
+
+> ⚠️ **重要：深度分析失败则整个流程失败**
+> - 深度分析必须使用正确的 AI 模型（glm-4.7）完整分析字幕内容
+> - 如果 `kb-deep-analyzer` 返回 `success: false`，**必须汇报失败**，不能继续执行后续步骤
+> - 不能使用预设模板或降级内容来"假装成功"
+> - 失败时必须向用户报告具体的错误原因
+
+> ⚠️ **重要**：没有找到官方字幕是**正常的**！很多视频没有自动生成的字幕。
+> - 如果 `has_subtitle = false`，继续执行 Whisper 步骤
+> - 如果 `has_subtitle = true`，跳过 Whisper 步骤，直接使用官方字幕
+
+## 目录结构
+
+```
+~/work/knowledge-base/
+├── archive/
+│   └── YYYY-MM-DD/
+├── pending/
+│   └── {platform}_{id}.link.json
+├── metadata/
+│   └── {platform}_{id}.meta.json
+├── content/
+│   ├── downloads/
+│   │   └── {platform}_{id}.mp4
+│   ├── {platform}_{id}.txt        # Whisper 识别的文本
+│   └── {platform}_{id}.json       # Whisper 完整结果
+└── analyzed/
+    └── {platform}_{id}.md
+```
+
+## 辅助命令
+
+```bash
+# ------------------- 查重与归档 -------------------
+
+# 查重
+python3 kb_workflow.py --check --url "[URL]"
+
+# 归档分析报告
+python3 kb_workflow.py --archive-file "[分析报告路径]"
+
+
+# ------------------- 目录路径查询 -------------------
+
+# 获取知识库根目录（默认 ~/work/knowledge-base）
+python3 kb_workflow.py --base-path "~/custom/path"
+
+# 获取待处理目录（pending）
+python3 kb_workflow.py --pending
+
+# 获取元数据目录
+python3 kb_workflow.py --metadata
+
+# 获取内容目录
+python3 kb_workflow.py --content
+
+# 获取内容下载目录
+python3 kb_workflow.py --content_downloads
+
+# 获取字幕目录
+python3 kb_workflow.py --content_subtitles
+
+# 获取深度分析目录
+python3 kb_workflow.py --analyzed
+
+# 获取归档目录
+python3 kb_workflow.py --archive
+
+# 获取订阅信息目录
+python3 kb_workflow.py --archive_subscriptions
+
+
+# ------------------- 工具函数 -------------------
+
+# 从文件名提取平台名称
+python3 kb_workflow.py --extract_platform "bilibili_BVxxx.mp4"
+```
+

kb-workflow/SkillBug.md

@@ -0,0 +1,101 @@
+# SkillBug.md
+
+## 问题记录
+
+### 2026-02-10: 子代理权限问题
+
+**问题：** `sessions_spawn` 调用子代理时返回 `forbidden` 错误。
+
+**原因：** OpenClaw 配置中 allowlist 为空，Python 脚本没有权限调用子代理。
+
+**解决方案：** 改用 Prompt-based 模式（方案B）。
+
+---
+
+## 架构重构
+
+### 之前（Script-based）
+
+```
+kb_workflow.py (Python脚本)
+    ↓
+sessions_spawn()  # ❌ 失败（脚本没有权限）
+```
+
+### 之后（Prompt-based）
+
+```
+主代理读取 SKILL.md
+    ↓
+按照指导调用 sessions_spawn  # ✅ 成功（主代理有权限）
+```
+
+### 文件变更
+
+| 文件 | 变更 | 说明 |
+|------|------|------|
+| `SKILL.md` | 重写 | 详细定义 workflow 步骤，主代理直接读取执行 |
+| `kb_workflow.py` | 简化 | 保留为辅助工具函数库（查重、归档、Git） |
+| `skill_main.py` | 简化 | 只做触发检测，返回指导信息 |
+| `SkillBug.md` | 更新 | 记录架构变更 |
+
+### SKILL.md 结构
+
+```
+1. 触发条件
+2. 步骤 1: 查重
+3. 步骤 2: kb-link-parser
+4. 步骤 3: 元数据技能
+5. 步骤 4: 内容下载
+6. 步骤 5: OCR（30分钟超时）
+7. 步骤 6: 深度分析
+8. 步骤 7: 归档和 Git
+9. 步骤 8: 汇报完成
+10. 目录结构
+11. 支持平台
+12. 注意事项
+```
+
+### kb_workflow.py 保留的函数
+
+```python
+extract_platform(filename)           # 从文件名提取平台
+extract_base_name(filename)          # 从文件名提取基础名称
+get_knowledge_base_path()            # 获取知识库根目录
+ensure_directories(base_path)        # 确保目录存在
+check_duplicate(url)                 # 查重
+archive_and_git(analyzed_file)       # 归档和 Git
+generate_summary(...)                # 生成汇报
+```
+
+---
+
+### 2026-02-12: B站视频下载失败 - 403 Forbidden
+
+**问题：** 使用 kb-bilibili-content 下载视频时返回 403 Forbidden 错误。
+
+**原因：**
+1. 视频可能需要B站会员权限
+2. 或者需要登录cookie才能下载
+3. 当前未配置 `BILIBILI_COOKIE` 环境变量
+
+**复现场景：**
+- 链接：https://www.bilibili.com/video/BV1Z9nXzUEhv
+- 下载命令：`python3 bilibili_content.py --id "BV1Z9nXzUEhv" --type video`
+- 结果：返回403错误页面，文件只有548字节
+
+**影响：** 无法进行后续的OCR处理和深度分析
+
+**建议解决方案：**
+1. 配置 `BILIBILI_COOKIE` 环境变量
+2. 或提供cookies文件路径
+3. 或跳过OCR步骤，使用元数据+简介进行深度分析
+
+**当前进度：**
+- ✅ 步骤1：环境变量检查完成
+- ✅ 步骤2：链接解析成功（bilibili_BV1Z9nXzUEhv）
+- ✅ 步骤3：元数据提取成功（标题：新加坡分区漫游指南...）
+- ❌ 步骤4：视频下载失败（403）
+- ⏸️  步骤5：OCR处理（阻塞）
+- ⏸️  步骤6：深度分析（阻塞）
+- ⏸️  步骤7：归档（阻塞）

kb-workflow/install-env.sh

@@ -0,0 +1,321 @@
+#!/bin/bash
+# =============================================================================
+# KB Workflow 环境一键安装脚本
+# =============================================================================
+# 功能: 检查并安装 kb-workflow 技能所需的全部环境依赖
+# 支持: Ubuntu/Debian 系统
+# 
+# 使用方法:
+#   ./install-env.sh           # 检查环境并提示安装命令（检查模式）
+#   ./install-env.sh --auto    # 自动安装所有缺失依赖（自动模式）
+# =============================================================================
+
+# 颜色定义
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+BLUE='\033[0;34m'
+NC='\033[0m'
+
+# 日志函数
+log_info() { echo -e "${BLUE}[INFO]${NC} $1"; }
+log_success() { echo -e "${GREEN}[SUCCESS]${NC} $1"; }
+log_warn() { echo -e "${YELLOW}[WARN]${NC} $1"; }
+log_error() { echo -e "${RED}[ERROR]${NC} $1"; }
+log_section() {
+    echo ""
+    echo -e "${BLUE}========================================${NC}"
+    echo -e "${BLUE}$1${NC}"
+    echo -e "${BLUE}========================================${NC}"
+}
+
+# 获取脚本目录
+SCRIPT_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
+DEFAULT_KB_PATH="${HOME}/work/knowledge-base"
+
+# 解析参数
+AUTO_MODE=false
+if [ "$1" = "--auto" ]; then
+    AUTO_MODE=true
+    log_info "自动安装模式"
+fi
+
+# 检测 Linux 发行版
+detect_distro() {
+    if [ -f /etc/os-release ]; then
+        . /etc/os-release
+        echo "$ID"
+    elif [ -f /etc/debian_version ]; then
+        echo "debian"
+    elif [ -f /etc/redhat-release ]; then
+        echo "rhel"
+    fi
+}
+DISTRO=$(detect_distro)
+
+# 检查命令是否存在
+command_exists() {
+    command -v "$1" > /dev/null 2>&1
+}
+
+# 检查 Python 包
+check_python_package() {
+    python3 -c "import $1" 2>/dev/null
+}
+
+# =============================================================================
+# 1. 检查系统依赖
+# =============================================================================
+log_section "第一步: 检查系统依赖"
+
+MISSING_DEPS=()
+
+# 检查 Python3
+if command_exists python3; then
+    PYTHON_VERSION=$(python3 --version | cut -d' ' -f2)
+    log_success "Python3 已安装: $PYTHON_VERSION"
+else
+    log_warn "Python3 未安装"
+    MISSING_DEPS+=("python3")
+fi
+
+# 检查 pip3
+if python3 -m pip --version > /dev/null 2>&1 || command_exists pip3; then
+    log_success "pip3 已安装"
+else
+    log_warn "pip3 未安装"
+    MISSING_DEPS+=("python3-pip")
+fi
+
+# 检查 ffmpeg
+if command_exists ffmpeg; then
+    log_success "ffmpeg 已安装"
+else
+    log_warn "ffmpeg 未安装"
+    MISSING_DEPS+=("ffmpeg")
+fi
+
+# 检查 tesseract
+if command_exists tesseract; then
+    log_success "Tesseract OCR 已安装"
+else
+    log_warn "Tesseract OCR 未安装"
+    MISSING_DEPS+=("tesseract-ocr")
+fi
+
+# 检查 git
+if command_exists git; then
+    log_success "Git 已安装"
+else
+    log_warn "Git 未安装"
+    MISSING_DEPS+=("git")
+fi
+
+# =============================================================================
+# 2. 自动安装系统依赖（自动模式）
+# =============================================================================
+if [ ${#MISSING_DEPS[@]} -gt 0 ]; then
+    if [ "$AUTO_MODE" = true ]; then
+        log_section "自动安装系统依赖"
+        log_info "检测到系统: $DISTRO"
+        
+        case "$DISTRO" in
+            ubuntu|debian)
+                log_info "更新包列表..."
+                sudo apt-get update -qq
+                
+                log_info "安装依赖: ${MISSING_DEPS[*]}"
+                sudo apt-get install -y -qq "${MISSING_DEPS[@]}" tesseract-ocr-chi-sim tesseract-ocr-eng
+                ;;
+            fedora|rhel|centos)
+                log_info "更新包列表..."
+                sudo dnf check-update -y || true
+                
+                log_info "安装依赖: ${MISSING_DEPS[*]}"
+                sudo dnf install -y "${MISSING_DEPS[@]}" tesseract-langpack-chi_sim tesseract-langpack-eng
+                ;;
+            arch)
+                log_info "安装依赖: ${MISSING_DEPS[*]}"
+                sudo pacman -S --noconfirm "${MISSING_DEPS[@]}" tesseract-data-chi_sim tesseract-data-eng
+                ;;
+            *)
+                log_error "不支持的系统: $DISTRO"
+                exit 1
+                ;;
+        esac
+        
+        log_success "系统依赖安装完成"
+        MISSING_DEPS=()
+    else
+        log_section "需要安装的系统依赖"
+        echo ""
+        echo "检测到以下依赖未安装:"
+        for dep in "${MISSING_DEPS[@]}"; do
+            echo "  - $dep"
+        done
+        
+        echo ""
+        echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+        echo "请运行以下命令安装:"
+        echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+        echo ""
+        echo -e "${YELLOW}sudo apt-get update${NC}"
+        echo -e "${YELLOW}sudo apt-get install -y ${MISSING_DEPS[*]} tesseract-ocr-chi-sim tesseract-ocr-eng${NC}"
+        echo ""
+    fi
+fi
+
+# =============================================================================
+# 3. 检查 Python 依赖
+# =============================================================================
+log_section "第二步: 检查 Python 依赖"
+
+PYTHON_PACKAGES=(
+    "httpx:httpx"
+    "aiohttp:aiohttp"
+    "requests:requests"
+    "Pillow:PIL"
+)
+
+MISSING_PY_PKGS=()
+
+for pkg_info in "${PYTHON_PACKAGES[@]}"; do
+    pkg_name=$(echo "$pkg_info" | cut -d':' -f1)
+    import_name=$(echo "$pkg_info" | cut -d':' -f2)
+    if check_python_package "$import_name" 2>/dev/null; then
+        log_success "$pkg_name 已安装"
+    else
+        log_warn "$pkg_name 未安装"
+        MISSING_PY_PKGS+=("$pkg_name")
+    fi
+done
+
+# 检查特殊包
+check_python_package "playwright" 2>/dev/null && log_success "playwright 已安装" || MISSING_PY_PKGS+=("playwright")
+check_python_package "easyocr" 2>/dev/null && log_success "easyocr 已安装" || MISSING_PY_PKGS+=("easyocr")
+check_python_package "pytesseract" 2>/dev/null && log_success "pytesseract 已安装" || MISSING_PY_PKGS+=("pytesseract")
+
+# =============================================================================
+# 4. 自动安装 Python 依赖（自动模式）
+# =============================================================================
+if [ ${#MISSING_PY_PKGS[@]} -gt 0 ]; then
+    if [ "$AUTO_MODE" = true ]; then
+        log_section "自动安装 Python 包"
+        
+        log_info "安装: ${MISSING_PY_PKGS[*]}"
+        pip3 install --user "${MISSING_PY_PKGS[@]}" -q
+        
+        log_success "Python 包安装完成"
+        
+        # 安装 Playwright 浏览器
+        if [[ " ${MISSING_PY_PKGS[*]} " =~ " playwright " ]]; then
+            log_info "安装 Playwright Chromium 浏览器..."
+            python3 -m playwright install chromium
+            log_success "Chromium 浏览器安装完成"
+        fi
+        
+        MISSING_PY_PKGS=()
+    else
+        echo ""
+        echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+        echo "需要安装以下 Python 包:"
+        echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+        for pkg in "${MISSING_PY_PKGS[@]}"; do
+            echo "  - $pkg"
+        done
+        
+        echo ""
+        echo "请运行以下命令安装:"
+        echo ""
+        echo -e "${YELLOW}pip3 install --user ${MISSING_PY_PKGS[*]}${NC}"
+        
+        if [[ " ${MISSING_PY_PKGS[*]} " =~ " playwright " ]]; then
+            echo ""
+            echo -e "${YELLOW}安装完 playwright 后，还需要运行:${NC}"
+            echo -e "${YELLOW}  python3 -m playwright install chromium${NC}"
+        fi
+        echo ""
+    fi
+fi
+
+# =============================================================================
+# 5. 创建知识库目录结构
+# =============================================================================
+log_section "第三步: 创建知识库目录结构"
+
+KB_BASE_PATH="${KB_BASE_PATH:-$DEFAULT_KB_PATH}"
+log_info "知识库路径: $KB_BASE_PATH"
+
+mkdir -p "$KB_BASE_PATH"/{archive/{subscriptions,pending},metadata,content/{downloads,subtitles},analyzed}
+log_success "目录结构创建完成"
+
+# =============================================================================
+# 6. 环境变量配置提示
+# =============================================================================
+log_section "第四步: 环境变量配置"
+
+if [ -z "$KB_BASE_PATH_SET" ]; then
+    log_warn "KB_BASE_PATH 环境变量未设置"
+    echo ""
+    echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+    echo "建议添加到 ~/.bashrc 或 ~/.zshrc:"
+    echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+    echo ""
+    echo -e "${YELLOW}export KB_BASE_PATH=\"$KB_BASE_PATH\"${NC}"
+    echo ""
+else
+    log_success "KB_BASE_PATH 已设置: $KB_BASE_PATH"
+fi
+
+# 创建环境变量示例文件
+ENV_FILE="${SCRIPT_DIR}/.env.example"
+cat > "$ENV_FILE" << 'EOF'
+# KB Workflow 环境变量配置
+export KB_BASE_PATH="~/work/knowledge-base"
+
+# 可选: B站 Cookie（用于下载会员视频）
+# export BILIBILI_COOKIE="SESSDATA=xxx; bili_jct=xxx"
+
+# 可选: AI 分析配置
+# export AI_API_KEY="your-api-key"
+# export AI_PROVIDER="openai"  # 或 zhipu, minimax
+# export AI_MODEL="gpt-4"
+
+# 可选: Git 配置
+# export KB_GIT_USER="Your Name"
+# export KB_GIT_EMAIL="your@email.com"
+EOF
+
+log_info "环境变量示例已保存到: $ENV_FILE"
+
+# =============================================================================
+# 7. 完成总结
+# =============================================================================
+log_section "安装完成"
+
+echo ""
+if [ ${#MISSING_DEPS[@]} -eq 0 ] && [ ${#MISSING_PY_PKGS[@]} -eq 0 ]; then
+    echo -e "${GREEN}✅ 所有依赖已安装完成！${NC}"
+else
+    echo -e "${YELLOW}⚠️ 部分依赖需要手动安装${NC}"
+    echo ""
+    
+    if [ ${#MISSING_DEPS[@]} -gt 0 ]; then
+        echo "系统依赖:"
+        echo -e "${YELLOW}sudo apt-get install -y ${MISSING_DEPS[*]} tesseract-ocr-chi-sim tesseract-ocr-eng${NC}"
+    fi
+    
+    if [ ${#MISSING_PY_PKGS[@]} -gt 0 ]; then
+        echo ""
+        echo "Python 包:"
+        echo -e "${YELLOW}pip3 install --user ${MISSING_PY_PKGS[*]}${NC}"
+    fi
+fi
+
+echo ""
+echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+echo "使用方法:"
+echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
+echo "  export KB_BASE_PATH=\"~/work/knowledge-base\"
+echo "  python3 kb_workflow.py --check --url \"[URL]\""
+echo ""

kb-workflow/kb_workflow.py

@@ -0,0 +1,337 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+KB Workflow - Knowledge Base Workflow Orchestrator
+
+知识库工作流辅助工具函数。
+
+此模块提供查重、文件操作、Git 管理等工具函数，
+供主代理在执行 SKILL.md 中定义的 workflow 时调用。
+"""
+
+import os
+import sys
+import json
+import argparse
+import subprocess
+import shutil
+import re
+from datetime import datetime
+from pathlib import Path
+from typing import Dict, List, Optional, Any
+
+
+# ==================== 子技能映射 ====================
+
+METADATA_AGENTS = {
+    "bilibili": "kb-bilibili-metadata",
+    "zhihu": "kb-zhihu-metadata",
+}
+
+CONTENT_AGENTS = {
+    "bilibili": "kb-bilibili-content",
+}
+
+
+# ==================== 工具函数 ====================
+
+def extract_platform(filename: str) -> str:
+    """从文件名提取平台（第一个下划线之前）"""
+    match = re.match(r'^([a-zA-Z0-9]+)_(.+)$', filename)
+    if match:
+        return match.group(1)
+    return "unknown"
+
+
+def extract_base_name(filename: str) -> str:
+    """从文件名提取基础名称（去掉扩展名）"""
+    return Path(filename).stem
+
+
+def get_knowledge_base_path() -> Path:
+    """获取知识库根目录"""
+    base_path_str = os.environ.get('KB_BASE_PATH', '')
+    if base_path_str:
+        return Path(base_path_str).expanduser()
+    return Path.home() / "work" / "knowledge-base"
+
+
+def ensure_directories(base_path: Path = None) -> Dict[str, Path]:
+    """确保目录存在"""
+    if base_path is None:
+        base_path = get_knowledge_base_path()
+    
+    dirs = {
+        "base": base_path,
+        "pending": base_path / "pending",
+        "metadata": base_path / "metadata",
+        "content": base_path / "content",
+        "content_downloads": base_path / "content" / "downloads",
+        "analyzed": base_path / "analyzed",
+        "archive": base_path / "archive",
+        "archive_subscriptions": base_path / "archive" / "subscriptions",
+    }
+    
+    for name, dir_path in dirs.items():
+        if isinstance(dir_path, Path):
+            dir_path.mkdir(parents=True, exist_ok=True)
+    
+    return dirs
+
+
+def check_duplicate(base_path: Path = None, url: str = None, platform: str = None, content_id: str = None) -> Dict[str, Any]:
+    """查重检查"""
+    if base_path is None:
+        base_path = get_knowledge_base_path()
+    
+    metadata_dir = base_path / "metadata"
+    archive_dir = base_path / "archive"
+    
+    # 从URL提取ID
+    if url and not content_id:
+        bv_match = re.search(r'BV[a-zA-Z0-9]+', url)
+        content_id = bv_match.group() if bv_match else None
+    
+    result = {"duplicate": False, "status": None, "files": []}
+    
+    if metadata_dir.exists():
+        for f in metadata_dir.iterdir():
+            if f.is_file() and content_id and content_id in f.name:
+                result["duplicate"] = True
+                result["status"] = "metadata"
+                result["files"].append(str(f))
+    
+    if archive_dir.exists():
+        for date_dir in archive_dir.iterdir():
+            if date_dir.is_dir():
+                for f in date_dir.iterdir():
+                    if f.is_file() and content_id and content_id in f.name:
+                        result["duplicate"] = True
+                        result["status"] = "archived"
+                        result["files"].append(str(f))
+    
+    return result
+
+
+def archive_and_git(
+    analyzed_file: str,
+    base_path: Path = None,
+    git_root: Path = None,
+    git_user: str = None,
+    git_email: str = None,
+    git_remote: str = None
+) -> Dict[str, Any]:
+    """归档并提交Git"""
+    if base_path is None:
+        base_path = get_knowledge_base_path()
+    if git_root is None:
+        git_root = base_path / "archive"
+    if git_user is None:
+        git_user = os.environ.get('KB_GIT_USER', '')
+    if git_email is None:
+        git_email = os.environ.get('KB_GIT_EMAIL', '')
+    if git_remote is None:
+        git_remote = os.environ.get('KB_GIT_REMOTE', '')
+    
+    result = {
+        "success": False,
+        "archive_path": "",
+        "git_commit": False,
+        "git_push": None,
+        "error": None
+    }
+    
+    try:
+        # 1. 检查并初始化 Git 仓库
+        git_dir = git_root / ".git"
+        if not git_dir.exists():
+            # 初始化仓库
+            subprocess.run(['git', 'init'], cwd=str(git_root), capture_output=True)
+            subprocess.run(['git', 'config', 'user.name', git_user], cwd=str(git_root))
+            subprocess.run(['git', 'config', 'user.email', git_email], cwd=str(git_root))
+            
+            # 关联远端仓库
+            if git_remote:
+                subprocess.run(['git', 'remote', 'add', 'origin', git_remote], cwd=str(git_root), capture_output=True)
+                # 拉取远端分支
+                subprocess.run(['git', 'fetch', 'origin'], cwd=str(git_root), capture_output=True)
+                # 设置 main 分支并拉取
+                subprocess.run(['git', 'branch', '--set-upstream-to=origin/main', 'main'], cwd=str(git_root), capture_output=True)
+                subprocess.run(['git', 'pull', 'origin', 'main', '--rebase'], cwd=str(git_root), capture_output=True, timeout=120)
+        else:
+            # 已有仓库，检查远端是否配置
+            if git_remote:
+                remote_check = subprocess.run(['git', 'remote', '-v'], cwd=str(git_root), capture_output=True, text=True)
+                if 'origin' not in remote_check.stdout:
+                    subprocess.run(['git', 'remote', 'add', 'origin', git_remote], cwd=str(git_root), capture_output=True)
+                
+                # 拉取远端最新代码
+                subprocess.run(['git', 'fetch', 'origin'], cwd=str(git_root), capture_output=True)
+                # 尝试拉取 main 分支，如果失败则尝试 master
+                pull_result = subprocess.run(['git', 'pull', 'origin', 'main', '--rebase'], cwd=str(git_root), capture_output=True, timeout=120)
+                if pull_result.returncode != 0:
+                    subprocess.run(['git', 'pull', 'origin', 'master', '--rebase'], cwd=str(git_root), capture_output=True, timeout=120)
+        
+        # 2. 创建归档目录
+        date_str = datetime.now().strftime('%Y-%m-%d')
+        date_dir = base_path / "archive" / date_str
+        date_dir.mkdir(parents=True, exist_ok=True)
+        
+        # 3. 复制分析报告
+        src_file = Path(analyzed_file)
+        archive_file = date_dir / src_file.name
+        shutil.copy(src_file, archive_file)
+        result["archive_path"] = str(archive_file)
+        
+        # 4. 复制元数据
+        meta_file = base_path / "metadata" / f"{src_file.stem}.meta.json"
+        if meta_file.exists():
+            shutil.copy(meta_file, date_dir / meta_file.name)
+        
+        # 5. Git 提交并推送
+        if (git_root / ".git").exists():
+            # 添加所有变更
+            subprocess.run(['git', 'add', '.'], cwd=str(git_root), capture_output=True)
+            
+            # 检查是否有变更需要提交
+            status_result = subprocess.run(['git', 'status', '--porcelain'], cwd=str(git_root), capture_output=True, text=True)
+            
+            if status_result.stdout.strip():
+                commit_msg = f"Archive: {src_file.name} - {datetime.now().isoformat()}"
+                commit_result = subprocess.run(
+                    ['git', 'commit', '-m', commit_msg],
+                    cwd=str(git_root), capture_output=True, text=True
+                )
+                result["git_commit"] = commit_result.returncode == 0
+                
+                # 推送到远端
+                if git_remote and commit_result.returncode == 0:
+                    push_result = subprocess.run(
+                        ['git', 'push', 'origin', 'main'],
+                        cwd=str(git_root), capture_output=True, text=True, timeout=60
+                    )
+                    if push_result.returncode != 0:
+                        # 如果 main 分支推送失败，尝试 master
+                        push_result = subprocess.run(
+                            ['git', 'push', 'origin', 'master'],
+                            cwd=str(git_root), capture_output=True, text=True, timeout=60
+                        )
+                    result["git_push"] = {"success": push_result.returncode == 0, "output": push_result.stdout}
+        
+        result["success"] = True
+    
+    except Exception as e:
+        result["error"] = str(e)
+    
+    return result
+
+
+def generate_summary(url: str, stages: List[Dict], final_file: str, archive_result: Dict = None) -> str:
+    """生成处理完成汇报"""
+    # 提取视频标题（如果有）
+    title_match = re.search(r'com/video/([^?]+)', url)
+    title = title_match.group(1) if title_match else "未知"
+    
+    summary = f"""
+✅ **处理完成！**
+
+**视频:** {title}
+
+**处理阶段:**
+"""
+    for stage in stages:
+        stage_name = stage.get('stage', 'unknown')
+        emoji = "✓" if stage.get('success') else "○"
+        summary += f"{emoji} {stage_name}\n"
+    
+    if final_file:
+        summary += f"\n**最终文件:** `{final_file}`"
+    
+    if archive_result:
+        if archive_result.get('git_commit'):
+            summary += f"\n**Git:** 已提交"
+            if archive_result.get('git_push', {}).get('success'):
+                summary += " ✓"
+            elif archive_result.get('git_push'):
+                summary += " ✗"
+    
+    return summary
+
+
+# ==================== CLI ====================
+
+def main():
+    """CLI入口 - 用于命令行测试"""
+    parser = argparse.ArgumentParser(
+        description='KB Workflow - Knowledge Base Workflow Orchestrator',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+使用示例:
+  # 查重
+  python kb_workflow.py --check --url "https://www.bilibili.com/video/BVxxx"
+  
+  # 归档
+  python kb_workflow.py --archive "/path/to/analyzed.md"
+        """
+    )
+    
+    parser.add_argument('--check', action='store_true', help='查重')
+    parser.add_argument('--url', type=str, help='要检查的URL')
+    parser.add_argument('--archive-file', type=str, help='归档的文件路径')
+    parser.add_argument('--base-path', type=str, help='获取知识库根目录')
+
+    parser.add_argument('--pending', action='store_true', help='获取url解析目录')
+    parser.add_argument('--metadata', action='store_true', help='获取元数据目录')
+    parser.add_argument('--content', action='store_true', help='获取内容目录')
+    parser.add_argument('--content_downloads', action='store_true', help='获取主内容下载目录')
+    parser.add_argument('--analyzed', action='store_true', help='获取深度分析目录')
+    parser.add_argument('--archive_dir', action='store_true', help='获取归档目录')
+    parser.add_argument('--archive_subscriptions', action='store_true', help='获取订阅信息目录')
+    parser.add_argument('--extract_platform', type=str, help='要提取平台名称的文件名')
+    args = parser.parse_args()
+    
+    base_path = Path(args.base_path) if args.base_path else None
+    dirs = ensure_directories(base_path)
+    
+    if args.check and args.url:
+        result = check_duplicate(base_path, args.url)
+        print(json.dumps(result, ensure_ascii=False, indent=2))
+    
+    elif args.archive_file:
+        result = archive_and_git(args.archive_file, base_path)
+        print(json.dumps(result, ensure_ascii=False, indent=2))
+    
+    elif args.pending:
+        print(json.dumps({"path": str(dirs["pending"])}, ensure_ascii=False, indent=2))
+    
+    elif args.metadata:
+        print(json.dumps({"path": str(dirs["metadata"])}, ensure_ascii=False, indent=2))
+    
+    elif args.content:
+        print(json.dumps({"path": str(dirs["content"])}, ensure_ascii=False, indent=2))
+    
+    elif args.content_downloads:
+        print(json.dumps({"path": str(dirs["content_downloads"])}, ensure_ascii=False, indent=2))
+    
+    elif args.analyzed:
+        print(json.dumps({"path": str(dirs["analyzed"])}, ensure_ascii=False, indent=2))
+    
+    elif args.archive_dir:
+        print(json.dumps({"path": str(dirs["archive"])}, ensure_ascii=False, indent=2))
+    
+    elif args.archive_subscriptions:
+        print(json.dumps({"path": str(dirs["archive_subscriptions"])}, ensure_ascii=False, indent=2))
+    
+    elif args.extract_platform:
+        platform = extract_platform(args.extract_platform)
+        print(json.dumps({"platform": platform}, ensure_ascii=False, indent=2))
+    
+    elif args.base_path:
+        print(json.dumps({"path": str(dirs["base"])}, ensure_ascii=False, indent=2))
+    
+    else:
+        parser.print_help()
+
+
+if __name__ == '__main__':
+    main()

kb-zhihu-metadata/SKILL.md

@@ -0,0 +1,44 @@
+# KB Zhihu Metadata Skill
+
+**kb-zhihu-metadata** - Extract Zhihu question/answer/user metadata.
+
+## Features
+
+- Question metadata extraction
+- Answer metadata extraction
+- User information extraction
+- JSON output format
+- Markdown conversion support
+
+## Commands
+
+```bash
+# Question metadata
+python zhihu_metadata.py --id "266309957" --type question
+
+# Answer metadata
+python zhihu_metadata.py --id "2003427891925509507" --type answer
+```
+
+## Output
+
+```json
+{
+  "success": true,
+  "data": {
+    "platform": "zhihu",
+    "type": "question",
+    "id": "266309957",
+    "title": "问题标题",
+    "author": "作者名字",
+    "answer_count": 50,
+    "followers": 1000
+  }
+}
+```
+
+## Installation
+
+```bash
+cp -r kb-zhihu-metadata/ ~/skills/
+```

kb-zhihu-metadata/markdown_converter.py

@@ -0,0 +1,17 @@
+from markdownify import MarkdownConverter as _MarkdownConverter
+
+class MarkdownConverter:
+    """HTML 转 Markdown 转换器，保留完整格式"""
+
+    def __init__(self):
+        self.converter = _MarkdownConverter(
+            heading_style="ATX",
+            bullets="*,-",
+            code_blocks=True,
+            tables=True,
+            strip=["script", "style", "nav", "footer", "aside"]
+        )
+
+    def convert(self, html: str) -> str:
+        """转换 HTML 为 Markdown"""
+        return self.converter.convert(html)

kb-zhihu-metadata/zhihu_extractor.py

@@ -0,0 +1,335 @@
+"""
+知乎 Playwright 内容提取器
+使用 Playwright 浏览器自动化工具抓取知乎内容
+"""
+
+import re
+import sys
+import os
+from typing import Dict, Optional, Any
+from urllib.parse import urlparse
+
+# 添加当前目录到 Python 路径以支持绝对导入
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+
+try:
+    from .markdown_converter import MarkdownConverter
+except ImportError:
+    from markdown_converter import MarkdownConverter
+
+
+class PlaywrightBrowser:
+    """Playwright 浏览器管理器"""
+
+    def __init__(self, headless: bool = True, timeout: int = 30):
+        self.headless = headless
+        self.timeout = timeout * 1000
+        self.browser = None
+        self.context = None
+        self.page = None
+
+    def start(self):
+        """启动浏览器"""
+        from playwright.sync_api import sync_playwright
+        self.playwright = sync_playwright().start()
+        self.browser = self.playwright.chromium.launch(headless=self.headless)
+        self.context = self.browser.new_context(
+            user_agent='Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/122.0.0.0 Safari/537.36'
+        )
+        self.page = self.context.new_page()
+        self.page.set_default_timeout(self.timeout)
+
+    def close(self):
+        """关闭浏览器"""
+        if self.page:
+            self.page.close()
+        if self.context:
+            self.context.close()
+        if self.browser:
+            self.browser.close()
+        if hasattr(self, 'playwright'):
+            self.playwright.stop()
+
+    def goto(self, url: str, wait_until: str = "networkidle"):
+        """导航到指定URL"""
+        return self.page.goto(url, wait_until=wait_until)
+
+    def wait_for_selector(self, selector: str, timeout: int = None):
+        """等待选择器出现"""
+        timeout_ms = timeout * 1000 if timeout else self.timeout
+        return self.page.wait_for_selector(selector, timeout=timeout_ms)
+
+    def query_selector(self, selector: str):
+        """查询选择器"""
+        return self.page.query_selector(selector)
+
+    def query_selector_all(self, selector: str):
+        """查询所有匹配的选择器"""
+        return self.page.query_selector_all(selector)
+
+    def evaluate(self, script: str):
+        """执行 JavaScript 代码"""
+        return self.page.evaluate(script)
+
+
+class ZhihuPlaywrightExtractor:
+    """知乎内容提取器 - 使用 Playwright"""
+
+    URL_PATTERNS = {
+        'zvideo': r'zhihu\.com/zvideo/(\d+)',
+        'zhuanlan': r'zhuanlan\.zhihu\.com/p/(\d+)',
+    }
+
+    def __init__(self, headless: bool = True, timeout: int = 30):
+        """
+        初始化提取器
+
+        Args:
+            headless: 是否使用无头浏览器
+            timeout: 超时时间（秒）
+        """
+        self.browser = PlaywrightBrowser(headless=headless, timeout=timeout)
+        self.markdown_converter = MarkdownConverter()
+
+    def identify_url_type(self, url: str) -> Optional[str]:
+        """
+        识别 URL 类型
+
+        Returns:
+            'answer', 'article', 'question' 或 None
+        """
+        for url_type, pattern in self.URL_PATTERNS.items():
+            if re.search(pattern, url):
+                return url_type
+        return None
+
+    def extract_id(self, url: str) -> Optional[str]:
+        """
+        从 URL 中提取内容 ID
+
+        Returns:
+            内容 ID 或 None
+        """
+        url_type = self.identify_url_type(url)
+        if url_type:
+            pattern = self.URL_PATTERNS[url_type]
+            match = re.search(pattern, url)
+            if match:
+                return match.group(1)
+        return None
+
+    def _extract_text_content(self, element) -> str:
+        """提取元素的文本内容"""
+        if element is None:
+            return ''
+        return element.inner_text() if hasattr(element, 'inner_text') else ''
+
+    def _extract_html_content(self, element) -> str:
+        """提取元素的 HTML 内容"""
+        if element is None:
+            return ''
+        return element.inner_html() if hasattr(element, 'inner_html') else ''
+
+    def _extract_zvideo_content(self) -> Dict[str, Any]:
+        """提取知乎视频内容"""
+        result = {
+            'type': 'zvideo',
+            'title': '',
+            'author': '',
+            'content': '',
+            'content_markdown': '',
+            'publish_time': '',
+            'vote_up': 0,
+        }
+
+        # 打印页面标题用于调试
+        page_title = self.browser.page.title()
+        print(f"页面标题: {page_title}")
+
+        # 提取视频标题
+        title_elem = self.browser.query_selector('.VideoPlaylistInfo-title')
+        if title_elem:
+            result['title'] = self._extract_text_content(title_elem)
+        
+        # 备用：尝试其他选择器
+        if not result['title']:
+            title_elem = self.browser.query_selector('h1')
+            if title_elem:
+                result['title'] = self._extract_text_content(title_elem)
+
+        # 提取作者信息
+        author_elem = self.browser.query_selector('.AuthorInfo-name')
+        if author_elem:
+            result['author'] = self._extract_text_content(author_elem)
+
+        # 提取视频描述
+        desc_elem = self.browser.query_selector('.Video-desc')
+        if desc_elem:
+            html_content = self._extract_html_content(desc_elem)
+            result['content'] = html_content
+            result['content_markdown'] = self.markdown_converter.convert(html_content)
+
+        # 提取发布时间
+        time_elem = self.browser.query_selector('.Video-meta time')
+        if time_elem:
+            result['publish_time'] = time_elem.get_attribute('datetime') or self._extract_text_content(time_elem)
+
+        # 提取点赞数
+        vote_elem = self.browser.query_selector('.VideoLikeButton-count')
+        if vote_elem:
+            vote_text = self._extract_text_content(vote_elem)
+            try:
+                result['vote_up'] = int(vote_text.replace(',', ''))
+            except (ValueError, AttributeError):
+                pass
+
+        return result
+
+    def _extract_zhuanlan_content(self) -> Dict[str, Any]:
+        """提取知乎专栏文章内容"""
+        result = {
+            'type': 'zhuanlan',
+            'title': '',
+            'author': '',
+            'content': '',
+            'content_markdown': '',
+            'publish_time': '',
+            'vote_up': 0,
+        }
+
+        # 打印页面标题用于调试
+        page_title = self.browser.page.title()
+        print(f"页面标题: {page_title}")
+
+        # 提取文章标题
+        title_elem = self.browser.query_selector('.Post-Title')
+        if title_elem:
+            result['title'] = self._extract_text_content(title_elem)
+
+        # 备用
+        if not result['title']:
+            title_elem = self.browser.query_selector('h1')
+            if title_elem:
+                result['title'] = self._extract_text_content(title_elem)
+
+        # 提取作者信息
+        author_elem = self.browser.query_selector('.AuthorInfo-name')
+        if author_elem:
+            result['author'] = self._extract_text_content(author_elem)
+
+        # 提取文章内容
+        content_elem = self.browser.query_selector('.Post-RichText')
+        if content_elem:
+            html_content = self._extract_html_content(content_elem)
+            result['content'] = html_content
+            result['content_markdown'] = self.markdown_converter.convert(html_content)
+
+        # 提取发布时间
+        time_elem = self.browser.query_selector('.ContentItem-time')
+        if time_elem:
+            result['publish_time'] = time_elem.get_attribute('data-tooltip') or self._extract_text_content(time_elem)
+
+        # 提取点赞数
+        vote_elem = self.browser.query_selector('.VoteButton--up .VoteButton-label')
+        if vote_elem:
+            vote_text = self._extract_text_content(vote_elem)
+            try:
+                result['vote_up'] = int(vote_text.replace(',', ''))
+            except (ValueError, AttributeError):
+                pass
+
+        return result
+
+    def extract(self, url: str) -> Optional[Dict[str, Any]]:
+        """
+        从 URL 提取知乎内容
+
+        Args:
+            url: 知乎 URL
+
+        Returns:
+            包含提取内容的字典，结构如下:
+            {
+                'type': 'zvideo' | 'zhuanlan',
+                'title': str,
+                'author': str,
+                'content': str (HTML),
+                'content_markdown': str (Markdown),
+                'publish_time': str,
+                'vote_up': int,
+            }
+            如果提取失败返回 None
+        """
+        try:
+            # 识别 URL 类型
+            url_type = self.identify_url_type(url)
+            if not url_type:
+                print(f"无法识别 URL 类型: {url}")
+                return None
+
+            print(f"URL 类型: {url_type}")
+            print(f"启动浏览器...")
+
+            # 启动浏览器
+            self.browser.start()
+
+            print(f"访问页面: {url}")
+            # 访问页面
+            self.browser.goto(url, wait_until="domcontentloaded")
+
+            print("等待页面加载...")
+            import time
+            time.sleep(3)  # 等待 JavaScript 执行
+
+            # 根据类型提取内容
+            if url_type == 'zvideo':
+                result = self._extract_zvideo_content()
+            elif url_type == 'zhuanlan':
+                result = self._extract_zhuanlan_content()
+            else:
+                print(f"不支持的类型: {url_type}")
+                return None
+
+            # 添加额外信息
+            result['url'] = url
+            result['content_id'] = self.extract_id(url)
+
+            print(f"提取成功! 标题: {result.get('title', '')[:50]}...")
+            return result
+
+        except Exception as e:
+            print(f"提取知乎内容时出错: {e}")
+            import traceback
+            traceback.print_exc()
+            return None
+        finally:
+            # 关闭浏览器
+            self.browser.close()
+
+    def __enter__(self):
+        """上下文管理器入口"""
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        """上下文管理器出口"""
+        self.browser.close()
+        return False
+
+
+# 便捷函数
+def extract_zhihu_content(url: str, headless: bool = True, timeout: int = 30) -> Optional[Dict[str, Any]]:
+    """
+    便捷函数：提取知乎内容
+
+    Args:
+        url: 知乎 URL
+        headless: 是否使用无头浏览器
+        timeout: 超时时间（秒）
+
+    Returns:
+        提取的内容字典
+    """
+    with ZhihuPlaywrightExtractor(headless=headless, timeout=timeout) as extractor:
+        return extractor.extract(url)

kb-zhihu-metadata/zhihu_metadata.py

@@ -0,0 +1,153 @@
+#!/usr/bin/env python3
+"""
+知乎元数据获取工具
+
+仅支持两种内容类型：
+- zvideo: 知乎视频 (https://www.zhihu.com/zvideo/xxx)
+- zhuanlan: 知乎专栏文章 (https://zhuanlan.zhihu.com/p/xxx)
+"""
+
+import argparse
+import json
+import sys
+import os
+from typing import Dict, Optional, Any
+
+# 添加当前目录到 Python 路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+if current_dir not in sys.path:
+    sys.path.insert(0, current_dir)
+
+from zhihu_extractor import ZhihuPlaywrightExtractor
+
+
+def extract_zvideo(video_id: str, headless: bool = True) -> Dict[str, Any]:
+    """
+    获取知乎视频元数据
+
+    Args:
+        video_id: 视频 ID (数字字符串)
+        headless: 是否使用无头浏览器
+
+    Returns:
+        视频元数据字典
+    """
+    url = f"https://www.zhihu.com/zvideo/{video_id}"
+    return _extract_metadata(url, "zvideo", headless)
+
+
+def extract_zhuanlan(article_id: str, headless: bool = True) -> Dict[str, Any]:
+    """
+    获取知乎专栏文章元数据
+
+    Args:
+        article_id: 文章 ID (数字字符串)
+        headless: 是否使用无头浏览器
+
+    Returns:
+        文章元数据字典
+    """
+    url = f"https://zhuanlan.zhihu.com/p/{article_id}"
+    return _extract_metadata(url, "zhuanlan", headless)
+
+
+def _extract_metadata(url: str, content_type: str, headless: bool) -> Dict[str, Any]:
+    """
+    内部函数：提取元数据
+
+    Args:
+        url: 知乎 URL
+        content_type: 内容类型 (zvideo/zhuanlan)
+        headless: 是否使用无头浏览器
+
+    Returns:
+        元数据字典
+    """
+    extractor = ZhihuPlaywrightExtractor(headless=headless)
+    result = extractor.extract(url)
+
+    if result:
+        # 标准化输出格式
+        metadata = {
+            "type": content_type,
+            "id": result.get("content_id", ""),
+            "url": result.get("url", url),
+            "title": result.get("title", ""),
+            "author": result.get("author", ""),
+            "publish_time": result.get("publish_time", ""),
+            "vote_up": result.get("vote_up", 0),
+            "content_markdown": result.get("content_markdown", ""),
+            "raw_html": result.get("content", ""),
+        }
+        return metadata
+    else:
+        return {
+            "type": content_type,
+            "error": "Failed to extract content",
+            "url": url
+        }
+
+
+def main():
+    """命令行入口"""
+    parser = argparse.ArgumentParser(
+        description="知乎元数据获取工具",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+支持的类型:
+  zvideo   - 知乎视频 (https://www.zhihu.com/zvideo/123456)
+  zhuanlan - 知乎专栏文章 (https://zhuanlan.zhihu.com/p/123456)
+
+示例:
+  python zhihu_metadata.py --id "123456" --type zvideo
+  python zhihu_metadata.py --id "123456" --type zhuanlan
+        """
+    )
+
+    parser.add_argument(
+        "--id", "-i",
+        required=True,
+        help="知乎内容 ID (视频ID或文章ID)"
+    )
+
+    parser.add_argument(
+        "--type", "-t",
+        required=True,
+        choices=["zvideo", "zhuanlan"],
+        help="内容类型: zvideo=视频, zhuanlan=专栏文章"
+    )
+
+    parser.add_argument(
+        "--output", "-o",
+        default=None,
+        help="输出文件路径 (默认: stdout)"
+    )
+
+    parser.add_argument(
+        "--headless",
+        action="store_true",
+        default=True,
+        help="使用无头浏览器模式 (默认: True)"
+    )
+
+    args = parser.parse_args()
+
+    # 提取元数据
+    if args.type == "zvideo":
+        result = extract_zvideo(args.id, headless=args.headless)
+    else:  # zhuanlan
+        result = extract_zhuanlan(args.id, headless=args.headless)
+
+    # 输出结果
+    json_output = json.dumps(result, ensure_ascii=False, indent=2)
+
+    if args.output:
+        with open(args.output, 'w', encoding='utf-8') as f:
+            f.write(json_output)
+        print(f"结果已保存到: {args.output}")
+    else:
+        print(json_output)
+
+
+if __name__ == "__main__":
+    main()