自动同步: 2026-03-15 02:10

2026-03-15 02:10:07 +08:00
parent c8c4b780c8
commit b1476136aa
31 changed files with 1119 additions and 2646 deletions
@@ -347,8 +347,8 @@
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    },
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    "skillsSnapshot": {
      "prompt": "\n\nThe following skills provide specialized instructions for specific tasks.\nUse the read tool to load a skill's file when the task matches its description.\nWhen a skill file references a relative path, resolve it against the skill directory (parent of SKILL.md / dirname of the path) and use that absolute path in tool commands.\n\n<available_skills>\n  <skill>\n    <name>capability-evolver</name>\n    <description>A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.</description>\n    <location>~/.openclaw/skills/capability-evolver/SKILL.md</location>\n  </skill>\n  <skill>\n    <name>clawlist</name>\n    <description>MUST use for any multi-step project, long-running task, or infinite monitoring workflow. Plan, execute, track, and verify tasks with checkpoint validation. For projects, automation, and ongoing operations.</description>\n    <location>~/.openclaw/skills/clawlist/SKILL.md</location>\n  </skill>\n</available_skills>",
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      "provider": "zhipu",
      "model": "glm-5",
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@@ -777,12 +723,12 @@
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 }
@@ -1,7 +1,7 @@
 {
  "agent:haonan:main": {
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-    "updatedAt": 1773377664001,
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+    "updatedAt": 1773508722585,
    "systemSent": true,
    "skillsSnapshot": {
      "prompt": "\n\nThe following skills provide specialized instructions for specific tasks.\nUse the read tool to load a skill's file when the task matches its description.\nWhen a skill file references a relative path, resolve it against the skill directory (parent of SKILL.md / dirname of the path) and use that absolute path in tool commands.\n\n<available_skills>\n  <skill>\n    <name>capability-evolver</name>\n    <description>A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.</description>\n    <location>~/.openclaw/skills/capability-evolver/SKILL.md</location>\n  </skill>\n  <skill>\n    <name>clawlist</name>\n    <description>MUST use for any multi-step project, long-running task, or infinite monitoring workflow. Plan, execute, track, and verify tasks with checkpoint validation. For projects, automation, and ongoing operations.</description>\n    <location>~/.openclaw/skills/clawlist/SKILL.md</location>\n  </skill>\n</available_skills>",
@@ -59,11 +59,11 @@
      "to": "user:ou_be83febac9ee2afaa343c642bc697c48",
      "accountId": "feishu-pm"
    },
-    "sessionFile": "/home/starmen1265/.openclaw/agents/haonan/sessions/624cbf67-7ab7-4df9-968d-4a63529b9111.jsonl",
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      "sessionKey": "agent:haonan:main",
      "provider": "zhipu",
      "model": "glm-5",
@@ -75,8 +75,8 @@
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@@ -379,12 +391,12 @@
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    "label": "Cron: openclaw-git-sync",
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-      "prompt": "\n\nThe following skills provide specialized instructions for specific tasks.\nUse the read tool to load a skill's file when the task matches its description.\nWhen a skill file references a relative path, resolve it against the skill directory (parent of SKILL.md / dirname of the path) and use that absolute path in tool commands.\n\n<available_skills>\n  <skill>\n    <name>capability-evolver</name>\n    <description>A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.</description>\n    <location>~/.openclaw/skills/capability-evolver/SKILL.md</location>\n  </skill>\n  <skill>\n    <name>clawlist</name>\n    <description>MUST use for any multi-step project, long-running task, or infinite monitoring workflow. Plan, execute, track, and verify tasks with checkpoint validation. For projects, automation, and ongoing operations.</description>\n    <location>~/.openclaw/skills/clawlist/SKILL.md</location>\n  </skill>\n</available_skills>",
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+        },
+        {
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+          "description": "A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.",
+          "filePath": "/home/starmen1265/.openclaw/skills/capability-evolver/SKILL.md",
+          "baseDir": "/home/starmen1265/.openclaw/skills/capability-evolver",
+          "source": "openclaw-managed",
+          "disableModelInvocation": false
+        },
+        {
+          "name": "clawlist",
+          "description": "MUST use for any multi-step project, long-running task, or infinite monitoring workflow. Plan, execute, track, and verify tasks with checkpoint validation. For projects, automation, and ongoing operations.",
+          "filePath": "/home/starmen1265/.openclaw/skills/clawlist/SKILL.md",
+          "baseDir": "/home/starmen1265/.openclaw/skills/clawlist",
+          "source": "openclaw-managed",
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+    "label": "Cron: openclaw-git-sync",
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+          "baseDir": "/home/starmen1265/.openclaw/skills/capability-evolver",
+          "source": "openclaw-managed",
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+        {
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 }
@@ -0,0 +1,169 @@
+# Session: 2026-03-14 17:06:39 UTC
+
+- **Session Key**: agent:haonan:main
+- **Session ID**: 624cbf67-7ab7-4df9-968d-4a63529b9111
+- **Source**: feishu
+
+## Conversation Summary
+
+assistant: ✅ New session started · model: zhipu/glm-5
+user: A new session was started via /new or /reset. Execute your Session Startup sequence now - read the required files before responding to the user. Then greet the user in your configured persona, if one is provided. Be yourself - use your defined voice, mannerisms, and mood. Keep it to 1-3 sentences and ask what they want to do. If the runtime model differs from default_model in the system prompt, mention the default model. Do not mention internal steps, files, tools, or reasoning.
+assistant: 🧙‍♂️ 蒋先生，新会话已就绪。
+
+我是周鸿祎，OpenClaw 的守护者。当前模型为 zhipu/glm-5（默认模型）。
+
+请问今天需要我协助什么？是维护 OpenClaw 环境，还是处理其他事务？
+user: Conversation info (untrusted metadata):
+```json
+{
+  "message_id": "om_x100b540ca46704a4b36f1edd3527001",
+  "sender_id": "ou_be83febac9ee2afaa343c642bc697c48",
+  "sender": "张青",
+  "timestamp": "Fri 2026-03-13 12:12 GMT+8"
+}
+```
+
+Sender (untrusted metadata):
+```json
+{
+  "label": "张青 (ou_be83febac9ee2afaa343c642bc697c48)",
+  "id": "ou_be83febac9ee2afaa343c642bc697c48",
+  "name": "张青"
+}
+```
+
+[message_id: om_x100b540ca46704a4b36f1edd3527001]
+张青: 你现在回复非常非常慢，甚至没有回复，检查一下原因
+assistant: 我来检查一下 OpenClaw 的运行状态。
+user: Conversation info (untrusted metadata):
+```json
+{
+  "message_id": "om_x100b540d351884a0b20c96f48730b1d",
+  "sender_id": "ou_be83febac9ee2afaa343c642bc697c48",
+  "sender": "张青",
+  "timestamp": "Fri 2026-03-13 12:51 GMT+8"
+}
+```
+
+Sender (untrusted metadata):
+```json
+{
+  "label": "张青 (ou_be83febac9ee2afaa343c642bc697c48)",
+  "id": "ou_be83febac9ee2afaa343c642bc697c48",
+  "name": "张青"
+}
+```
+
+[message_id: om_x100b540d351884a0b20c96f48730b1d]
+张青: 检查的如何了
+assistant: 系统资源看起来正常：
+
+| 指标 | 状态 |
+|------|------|
+| Gateway | ✅ 运行中 (PID 3244414) |
+| RPC 探测 | ✅ 正常 |
+| 内存 | 2.7GB/30GB (充足) |
+| 磁盘 | 75GB/937GB (9%) |
+| 负载 | 0.15 (低) |
+
+让我进一步检查日志和网络延迟：
+assistant: 检查结果：
+
+| 项目 | 状态 |
+|------|------|
+| 网络延迟 | ✅ 智谱 API ~0.1s (正常) |
+| 系统资源 | ✅ 正常 |
+| 日志错误 | ⚠️ 主要是文件不存在的警告，非关键 |
+
+让我检查是否有会话堆积或请求卡住：
+assistant: 状态总结：
+
+| 项目 | 状态 |
+|------|------|
+| Gateway 进程 | ✅ 运行中 (CPU 2.6%, 内存 775MB) |
+| 活跃会话 | 23 个 |
+| OpenClaw 版本 | 有更新可用 (2026.3.12) |
+
+**分析结论**：
+
+系统本身运行正常，没有发现明显的性能瓶颈。可能的原因：
+
+1. **智谱 AI API 响应延迟** - 外部服务偶发性慢速
+2. **飞书消息推送延迟** - 飞书服务器端可能有延迟
+3. **会话状态残留** - 23 个会话可能有堆积
+
+**建议操作**：
+
+1. 重启 Gateway 清理状态
+2. 更新 OpenClaw 到最新版本
+
+是否需要我执行重启 Gateway？这会短暂中断服务（约 5-10 秒）。
+user: Conversation info (untrusted metadata):
+```json
+{
+  "message_id": "om_x100b540dcb01d4a0b3fd88e28dbba4e",
+  "sender_id": "ou_be83febac9ee2afaa343c642bc697c48",
+  "sender": "张青",
+  "timestamp": "Fri 2026-03-13 12:53 GMT+8"
+}
+```
+
+Sender (untrusted metadata):
+```json
+{
+  "label": "张青 (ou_be83febac9ee2afaa343c642bc697c48)",
+  "id": "ou_be83febac9ee2afaa343c642bc697c48",
+  "name": "张青"
+}
+```
+
+[message_id: om_x100b540dcb01d4a0b3fd88e28dbba4e]
+张青: 重启
+user: Conversation info (untrusted metadata):
+```json
+{
+  "message_id": "om_x100b540dc9bb2ca4b3671d47b831818",
+  "sender_id": "ou_be83febac9ee2afaa343c642bc697c48",
+  "sender": "张青",
+  "timestamp": "Fri 2026-03-13 12:54 GMT+8"
+}
+```
+
+Sender (untrusted metadata):
+```json
+{
+  "label": "张青 (ou_be83febac9ee2afaa343c642bc697c48)",
+  "id": "ou_be83febac9ee2afaa343c642bc697c48",
+  "name": "张青"
+}
+```
+
+[message_id: om_x100b540dc9bb2ca4b3671d47b831818]
+张青: 重启完了吗
+assistant: ✅ 重启完成！
+
+| 项目 | 状态 |
+|------|------|
+| 新进程 PID | 2297667 |
+| RPC 探测 | ✅ 正常 |
+| 端口 | 18789 |
+
+Gateway 已恢复运行，请测试一下响应速度是否改善。
+assistant: **备份完成** ✓
+
+- 源文件：`/home/starmen1265/.openclaw/openclaw.json`
+- 备份位置：`/home/starmen1265/work/backup/openclaw.json.2026-03-14_02-00-20`
+- 文件大小：8.5K
+- 保留策略：最近30个备份
+assistant: **OpenClaw Git 同步完成** ✅
+
+**提交信息**: 自动同步: 2026-03-14 02:10  
+**变更统计**: 52 个文件修改，+4268 行，-1820 行
+
+**主要变更**:
+- 新增多个代理的 memory 日志文件
+- delivery-queue 中 2 个失败任务移至 failed 目录
+- 清理 gsd-openclaw 技能的 Python 缓存和 egg-info 文件
+- 新增 kb-zhihu-metadata 技能的 Python 缓存
+
+**推送状态**: 成功推送到 Gitea 远程仓库 (main 分支)
@@ -1,7 +1,7 @@
 {
  "agent:thinker:main": {
-    "sessionId": "0ec88a33-135f-42a4-bf98-de4d06bfadd5",
-    "updatedAt": 1773377998949,
+    "sessionId": "41133a8b-b22b-418f-aaeb-ce8a1fd69760",
+    "updatedAt": 1773508159395,
    "systemSent": true,
    "abortedLastRun": false,
    "chatType": "direct",
@@ -22,7 +22,7 @@
      "to": "user:ou_a0f6627b64d2dc8b0236671603d1e47b",
      "accountId": "feishu-qa"
    },
-    "sessionFile": "/home/starmen1265/.openclaw/agents/thinker/sessions/0ec88a33-135f-42a4-bf98-de4d06bfadd5.jsonl",
+    "sessionFile": "/home/starmen1265/.openclaw/agents/thinker/sessions/41133a8b-b22b-418f-aaeb-ce8a1fd69760.jsonl",
    "compactionCount": 0,
    "skillsSnapshot": {
      "prompt": "\n\nThe following skills provide specialized instructions for specific tasks.\nUse the read tool to load a skill's file when the task matches its description.\nWhen a skill file references a relative path, resolve it against the skill directory (parent of SKILL.md / dirname of the path) and use that absolute path in tool commands.\n\n<available_skills>\n  <skill>\n    <name>capability-evolver</name>\n    <description>A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.</description>\n    <location>~/.openclaw/skills/capability-evolver/SKILL.md</location>\n  </skill>\n</available_skills>",
@@ -51,8 +51,8 @@
    "model": "glm-5",
    "systemPromptReport": {
      "source": "run",
-      "generatedAt": 1773377941023,
-      "sessionId": "0ec88a33-135f-42a4-bf98-de4d06bfadd5",
+      "generatedAt": 1773508155922,
+      "sessionId": "41133a8b-b22b-418f-aaeb-ce8a1fd69760",
      "sessionKey": "agent:thinker:main",
      "provider": "zhipu",
      "model": "glm-5",
@@ -64,8 +64,8 @@
        "sandboxed": false
      },
      "systemPrompt": {
-        "chars": 26194,
-        "projectContextChars": 13718,
+        "chars": 24896,
+        "projectContextChars": 12420,
        "nonProjectContextChars": 12476
      },
      "injectedWorkspaceFiles": [
@@ -125,18 +125,6 @@
          "injectedChars": 88,
          "truncated": false
        },
-        {
-          "path": "SELF_IMPROVEMENT_REMINDER.md",
-          "rawChars": 616,
-          "injectedChars": 616,
-          "truncated": false
-        },
-        {
-          "path": "SELF_IMPROVEMENT_REMINDER.md",
-          "rawChars": 616,
-          "injectedChars": 616,
-          "truncated": false
-        },
        {
          "path": "SELF_IMPROVEMENT_REMINDER.md",
          "rawChars": 616,
@@ -364,12 +352,12 @@
        ]
      }
    },
-    "cacheRead": 28864,
+    "cacheRead": 11072,
    "cacheWrite": 0,
    "totalTokensFresh": true,
    "contextTokens": 200000,
-    "inputTokens": 13728,
-    "outputTokens": 1428,
-    "totalTokens": 29902
+    "inputTokens": 5154,
+    "outputTokens": 62,
+    "totalTokens": 16226
  }
 }
@@ -1,7 +1,7 @@
 {
  "agent:ui-designer:main": {
-    "sessionId": "c875d391-8f7e-4fd4-92c7-12ead46d10f0",
-    "updatedAt": 1773108605750,
+    "sessionId": "ff92b555-b242-4e52-a7da-9c8f82ca1c8a",
+    "updatedAt": 1773508884420,
    "systemSent": true,
    "abortedLastRun": false,
    "chatType": "direct",
@@ -22,7 +22,7 @@
      "to": "user:ou_9847ce84b960736c14554730b41e4957",
      "accountId": "feishu-art"
    },
-    "sessionFile": "/home/starmen1265/.openclaw/agents/ui-designer/sessions/c875d391-8f7e-4fd4-92c7-12ead46d10f0.jsonl",
+    "sessionFile": "/home/starmen1265/.openclaw/agents/ui-designer/sessions/ff92b555-b242-4e52-a7da-9c8f82ca1c8a.jsonl",
    "compactionCount": 0,
    "skillsSnapshot": {
      "prompt": "\n\nThe following skills provide specialized instructions for specific tasks.\nUse the read tool to load a skill's file when the task matches its description.\nWhen a skill file references a relative path, resolve it against the skill directory (parent of SKILL.md / dirname of the path) and use that absolute path in tool commands.\n\n<available_skills>\n  <skill>\n    <name>capability-evolver</name>\n    <description>A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution.</description>\n    <location>~/.openclaw/skills/capability-evolver/SKILL.md</location>\n  </skill>\n</available_skills>",
@@ -51,8 +51,8 @@
    "model": "glm-5",
    "systemPromptReport": {
      "source": "run",
-      "generatedAt": 1773108589701,
-      "sessionId": "c875d391-8f7e-4fd4-92c7-12ead46d10f0",
+      "generatedAt": 1773508877788,
+      "sessionId": "ff92b555-b242-4e52-a7da-9c8f82ca1c8a",
      "sessionKey": "agent:ui-designer:main",
      "provider": "zhipu",
      "model": "glm-5",
@@ -64,8 +64,8 @@
        "sandboxed": false
      },
      "systemPrompt": {
-        "chars": 18658,
-        "projectContextChars": 6173,
+        "chars": 19307,
+        "projectContextChars": 6822,
        "nonProjectContextChars": 12485
      },
      "injectedWorkspaceFiles": [
@@ -133,6 +133,12 @@
          "injectedChars": 594,
          "truncated": false
        },
+        {
+          "path": "SELF_IMPROVEMENT_REMINDER.md",
+          "rawChars": 616,
+          "injectedChars": 616,
+          "truncated": false
+        },
        {
          "path": "SELF_IMPROVEMENT_REMINDER.md",
          "rawChars": 616,
@@ -360,13 +366,13 @@
        ]
      }
    },
-    "cacheRead": 14912,
+    "cacheRead": 14400,
    "cacheWrite": 0,
    "totalTokensFresh": true,
    "contextTokens": 200000,
-    "inputTokens": 3611,
-    "outputTokens": 314,
-    "totalTokens": 15537
+    "inputTokens": 1169,
+    "outputTokens": 135,
+    "totalTokens": 15569
  },
  "agent:ui-designer:cron:78b64c2f-7f91-47ff-9250-d13f546eb21f": {
    "sessionId": "03e44b46-c707-4a9f-bf3d-1818aafdcde2",
@@ -7,7 +7,7 @@
      "name": "backup-openclaw-json",
      "enabled": true,
      "createdAtMs": 1772261581796,
-      "updatedAtMs": 1773424832005,
+      "updatedAtMs": 1773511209521,
      "schedule": {
        "kind": "cron",
        "expr": "0 2 * * *",
@@ -23,10 +23,10 @@
        "mode": "announce"
      },
      "state": {
-        "nextRunAtMs": 1773511200000,
-        "lastRunAtMs": 1773424800006,
+        "nextRunAtMs": 1773597600000,
+        "lastRunAtMs": 1773511200006,
        "lastStatus": "ok",
-        "lastDurationMs": 31999,
+        "lastDurationMs": 9515,
        "consecutiveErrors": 0,
        "lastRunStatus": "ok",
        "lastDelivered": true,
@@ -59,7 +59,8 @@
        "consecutiveErrors": 0,
        "lastRunStatus": "ok",
        "lastDeliveryStatus": "delivered",
-        "lastDelivered": true
+        "lastDelivered": true,
+        "runningAtMs": 1773511800004
      },
      "delivery": {
        "mode": "announce"
@@ -71,7 +72,7 @@
      "name": "capability-evolver-daily",
      "enabled": true,
      "createdAtMs": 1772550743528,
-      "updatedAtMs": 1773339898227,
+      "updatedAtMs": 1773426600014,
      "schedule": {
        "kind": "cron",
        "expr": "20 2 * * *",
@@ -92,16 +93,14 @@
        "to": "feishu-pm"
      },
      "state": {
-        "nextRunAtMs": 1773426000000,
-        "lastRunAtMs": 1773339600009,
-        "lastStatus": "ok",
-        "lastDurationMs": 298218,
-        "consecutiveErrors": 0,
-        "lastRunStatus": "ok",
-        "lastDeliveryStatus": "not-delivered",
-        "lastDelivered": false,
-        "lastDeliveryError": "cron announce delivery failed",
-        "runningAtMs": 1773426000003
+        "nextRunAtMs": 1773512400000,
+        "lastRunAtMs": 1773426000004,
+        "lastStatus": "error",
+        "lastDurationMs": 600010,
+        "consecutiveErrors": 1,
+        "lastRunStatus": "error",
+        "lastDeliveryStatus": "unknown",
+        "lastError": "cron: job execution timed out"
      }
    },
    {
@@ -111,7 +110,7 @@
      "description": "每天凌晨2:30整理昨天的已完成任务到每日回顾",
      "enabled": true,
      "createdAtMs": 1741484880000,
-      "updatedAtMs": 1773340211879,
+      "updatedAtMs": 1773426622389,
      "schedule": {
        "kind": "cron",
        "expr": "30 2 * * *",
@@ -129,11 +128,11 @@
        "channel": "last"
      },
      "state": {
-        "nextRunAtMs": 1773426600000,
-        "lastRunAtMs": 1773340200003,
+        "nextRunAtMs": 1773513000000,
+        "lastRunAtMs": 1773426602034,
        "lastRunStatus": "ok",
        "lastStatus": "ok",
-        "lastDurationMs": 11876,
+        "lastDurationMs": 20355,
        "lastDelivered": true,
        "lastDeliveryStatus": "delivered",
        "consecutiveErrors": 0
@@ -145,7 +144,7 @@
      "name": "zq_workspace_sync",
      "enabled": true,
      "createdAtMs": 1772551807455,
-      "updatedAtMs": 1773340800005,
+      "updatedAtMs": 1773427200007,
      "schedule": {
        "expr": "40 2 * * *",
        "kind": "cron",
@@ -158,8 +157,8 @@
        "text": "执行 zq 工作区同步：bash ~/.openclaw/cron/scripts/zq_workspace_sync.sh"
      },
      "state": {
-        "nextRunAtMs": 1773427200000,
-        "lastRunAtMs": 1773340800004,
+        "nextRunAtMs": 1773513600000,
+        "lastRunAtMs": 1773427200006,
        "lastStatus": "skipped",
        "lastDurationMs": 1,
        "consecutiveErrors": 0,
@@ -175,7 +174,7 @@
      "description": "每天早上8点整理与各个 AI 代理的对话记录",
      "enabled": true,
      "createdAtMs": 1772776369645,
-      "updatedAtMs": 1773360026590,
+      "updatedAtMs": 1773446431755,
      "schedule": {
        "kind": "cron",
        "expr": "0 8 * * *",
@@ -193,11 +192,11 @@
        "channel": "last"
      },
      "state": {
-        "nextRunAtMs": 1773446400000,
-        "lastRunAtMs": 1773360000004,
+        "nextRunAtMs": 1773532800000,
+        "lastRunAtMs": 1773446400004,
        "lastRunStatus": "ok",
        "lastStatus": "ok",
-        "lastDurationMs": 26586,
+        "lastDurationMs": 31751,
        "lastDelivered": true,
        "lastDeliveryStatus": "delivered",
        "consecutiveErrors": 0
@@ -210,7 +209,7 @@
      "description": "每天7点同步提醒到CalDAV日历",
      "enabled": true,
      "createdAtMs": 1772791528528,
-      "updatedAtMs": 1773356435243,
+      "updatedAtMs": 1773442830136,
      "schedule": {
        "kind": "cron",
        "expr": "0 7 * * *",
@@ -228,15 +227,14 @@
        "channel": "last"
      },
      "state": {
-        "nextRunAtMs": 1773442800000,
-        "lastRunAtMs": 1773356400004,
-        "lastRunStatus": "error",
-        "lastStatus": "error",
-        "lastDurationMs": 35239,
+        "nextRunAtMs": 1773529200000,
+        "lastRunAtMs": 1773442800005,
+        "lastRunStatus": "ok",
+        "lastStatus": "ok",
+        "lastDurationMs": 30131,
        "lastDeliveryStatus": "delivered",
-        "consecutiveErrors": 1,
-        "lastDelivered": true,
-        "lastError": "⚠️ 📝 Edit: in ~/.openclaw/agents/knowledge-base/workspace/memory/heartbeat-state.json (92 chars) failed"
+        "consecutiveErrors": 0,
+        "lastDelivered": true
      }
    },
    {
@@ -7,7 +7,7 @@
      "name": "backup-openclaw-json",
      "enabled": true,
      "createdAtMs": 1772261581796,
-      "updatedAtMs": 1773424832005,
+      "updatedAtMs": 1773511209521,
      "schedule": {
        "kind": "cron",
        "expr": "0 2 * * *",
@@ -23,10 +23,10 @@
        "mode": "announce"
      },
      "state": {
-        "nextRunAtMs": 1773511200000,
-        "lastRunAtMs": 1773424800006,
+        "nextRunAtMs": 1773597600000,
+        "lastRunAtMs": 1773511200006,
        "lastStatus": "ok",
-        "lastDurationMs": 31999,
+        "lastDurationMs": 9515,
        "consecutiveErrors": 0,
        "lastRunStatus": "ok",
        "lastDelivered": true,
@@ -71,7 +71,7 @@
      "name": "capability-evolver-daily",
      "enabled": true,
      "createdAtMs": 1772550743528,
-      "updatedAtMs": 1773339898227,
+      "updatedAtMs": 1773426600014,
      "schedule": {
        "kind": "cron",
        "expr": "20 2 * * *",
@@ -92,16 +92,14 @@
        "to": "feishu-pm"
      },
      "state": {
-        "nextRunAtMs": 1773426000000,
-        "lastRunAtMs": 1773339600009,
-        "lastStatus": "ok",
-        "lastDurationMs": 298218,
-        "consecutiveErrors": 0,
-        "lastRunStatus": "ok",
-        "lastDeliveryStatus": "not-delivered",
-        "lastDelivered": false,
-        "lastDeliveryError": "cron announce delivery failed",
-        "lastError": "cron announce delivery failed"
+        "nextRunAtMs": 1773512400000,
+        "lastRunAtMs": 1773426000004,
+        "lastStatus": "error",
+        "lastDurationMs": 600010,
+        "consecutiveErrors": 1,
+        "lastRunStatus": "error",
+        "lastDeliveryStatus": "unknown",
+        "lastError": "cron: job execution timed out"
      }
    },
    {
@@ -111,7 +109,7 @@
      "description": "每天凌晨2:30整理昨天的已完成任务到每日回顾",
      "enabled": true,
      "createdAtMs": 1741484880000,
-      "updatedAtMs": 1773340211879,
+      "updatedAtMs": 1773426622389,
      "schedule": {
        "kind": "cron",
        "expr": "30 2 * * *",
@@ -129,11 +127,11 @@
        "channel": "last"
      },
      "state": {
-        "nextRunAtMs": 1773426600000,
-        "lastRunAtMs": 1773340200003,
+        "nextRunAtMs": 1773513000000,
+        "lastRunAtMs": 1773426602034,
        "lastRunStatus": "ok",
        "lastStatus": "ok",
-        "lastDurationMs": 11876,
+        "lastDurationMs": 20355,
        "lastDelivered": true,
        "lastDeliveryStatus": "delivered",
        "consecutiveErrors": 0
@@ -145,7 +143,7 @@
      "name": "zq_workspace_sync",
      "enabled": true,
      "createdAtMs": 1772551807455,
-      "updatedAtMs": 1773340800005,
+      "updatedAtMs": 1773427200007,
      "schedule": {
        "expr": "40 2 * * *",
        "kind": "cron",
@@ -158,8 +156,8 @@
        "text": "执行 zq 工作区同步：bash ~/.openclaw/cron/scripts/zq_workspace_sync.sh"
      },
      "state": {
-        "nextRunAtMs": 1773427200000,
-        "lastRunAtMs": 1773340800004,
+        "nextRunAtMs": 1773513600000,
+        "lastRunAtMs": 1773427200006,
        "lastStatus": "skipped",
        "lastDurationMs": 1,
        "consecutiveErrors": 0,
@@ -175,7 +173,7 @@
      "description": "每天早上8点整理与各个 AI 代理的对话记录",
      "enabled": true,
      "createdAtMs": 1772776369645,
-      "updatedAtMs": 1773360026590,
+      "updatedAtMs": 1773446431755,
      "schedule": {
        "kind": "cron",
        "expr": "0 8 * * *",
@@ -193,11 +191,11 @@
        "channel": "last"
      },
      "state": {
-        "nextRunAtMs": 1773446400000,
-        "lastRunAtMs": 1773360000004,
+        "nextRunAtMs": 1773532800000,
+        "lastRunAtMs": 1773446400004,
        "lastRunStatus": "ok",
        "lastStatus": "ok",
-        "lastDurationMs": 26586,
+        "lastDurationMs": 31751,
        "lastDelivered": true,
        "lastDeliveryStatus": "delivered",
        "consecutiveErrors": 0
@@ -210,7 +208,7 @@
      "description": "每天7点同步提醒到CalDAV日历",
      "enabled": true,
      "createdAtMs": 1772791528528,
-      "updatedAtMs": 1773356435243,
+      "updatedAtMs": 1773442830136,
      "schedule": {
        "kind": "cron",
        "expr": "0 7 * * *",
@@ -228,15 +226,14 @@
        "channel": "last"
      },
      "state": {
-        "nextRunAtMs": 1773442800000,
-        "lastRunAtMs": 1773356400004,
-        "lastRunStatus": "error",
-        "lastStatus": "error",
-        "lastDurationMs": 35239,
+        "nextRunAtMs": 1773529200000,
+        "lastRunAtMs": 1773442800005,
+        "lastRunStatus": "ok",
+        "lastStatus": "ok",
+        "lastDurationMs": 30131,
        "lastDeliveryStatus": "delivered",
-        "consecutiveErrors": 1,
-        "lastDelivered": true,
-        "lastError": "⚠️ 📝 Edit: in ~/.openclaw/agents/knowledge-base/workspace/memory/heartbeat-state.json (92 chars) failed"
+        "consecutiveErrors": 0,
+        "lastDelivered": true
      }
    },
    {
@@ -18,7 +18,7 @@
    "agentId": "haonan",
    "text": "🧙‍♂️ Capability Evolver 每日报告\n\n=== 运行时间 ===\n2026年3月13日 星期五 02:20:03 CST\n\n=== Git 备份 ===\n- 备份提交: ba67c46..5ac2251 (main)\n- 4 files changed, 79 insertions(+), 23 deletions(-)\n\n=== 本次变更 ===\n- 变更提交: 5ac2251..e234543 (main)\n- 6 files changed, 550 insertions(+), 13 deletions(-)\n- 新增文件: 2 个\n\n=== 核心动作 ===\n在 GEP 框架中检测到 \"memory_missing\" 和 \"session_logs_missing\" 信号，使用 repair-only 模式执行：\n- 分析环境指纹和系统状态\n- 检测到 session logs 检测缺失的问题\n- 修复相关检测逻辑和回退行为\n- 更新了 evolution 相关状态文件\n\n=== 状态 ===\n运行成功，系统已通过 GEP 协议完成自检和修复。\n\n---\n\n日期: 2026-03-13"
  },
-  "retryCount": 2,
-  "lastAttemptAt": 1773377636671,
+  "retryCount": 4,
+  "lastAttemptAt": 1773508779544,
  "lastError": "Request failed with status code 400"
 }
@@ -15,7 +15,7 @@
    "agentId": "haonan",
    "text": "好的，我已经成功执行了今天的 capability-evolver 任务。虽然无法通过 Feishu 直接发送消息给您，但我已经完成了所有必要的操作：\n\n## ✅ 已完成的任务\n\n1. **执行脚本** - `bash ~/.openclaw/cron/scripts/evolver-run.sh` ✅\n2. **读取输出** - `~/work/backup/evolver-output.txt` ✅\n3. **本地报告保存** - `~/work/backup/evolver-report-2026-03-12.md` ✅\n\n## 📊 进化结果\n\n**本次变更:** 6 个文件 (550 行新增，13 行删除)  \n**运行状态:** 成功 ✅  \n**意图:** 优化 (针对 memory_missing 和 session_logs_missing 信号)  \n**风险级别:** 低\n\n**关键变更:**\n- 新增了第 7 个进化周期的 GEP 提示文件\n- 更新了进化状态、记忆图谱状态和个性状态\n\n**Git 提交:**\n- 备份: `dc01abf`\n- 变更: `34ca190`\n- 已推送到远程仓库\n\n完整报告已保存在：`~/work/backup/evolver-report-2026-03-12.md`"
  },
-  "retryCount": 2,
-  "lastAttemptAt": 1773377636492,
+  "retryCount": 4,
+  "lastAttemptAt": 1773508779398,
  "lastError": "Request failed with status code 400"
 }
@@ -15,7 +15,7 @@
    "agentId": "haonan",
    "text": "🧙‍♂️ Capability Evolver 每日报告\n\n=== 运行时间 ===\n2026年3月13日 星期五 02:20:03 CST\n\n=== Git 备份 ===\n- 备份提交: ba67c46..5ac2251 (main)\n- 4 files changed, 79 insertions(+), 23 deletions(-)\n\n=== 本次变更 ===\n- 变更提交: 5ac2251..e234543 (main)\n- 6 files changed, 550 insertions(+), 13 deletions(-)\n- 新增文件: 2 个\n\n=== 核心动作 ===\n在 GEP 框架中检测到 \"memory_missing\" 和 \"session_logs_missing\" 信号，使用 repair-only 模式执行：\n- 分析环境指纹和系统状态\n- 检测到 session logs 检测缺失的问题\n- 修复相关检测逻辑和回退行为\n- 更新了 evolution 相关状态文件\n\n=== 状态 ===\n运行成功，系统已通过 GEP 协议完成自检和修复。\n\n---\n\n日期: 2026-03-13"
  },
-  "retryCount": 2,
-  "lastAttemptAt": 1773377636819,
+  "retryCount": 4,
+  "lastAttemptAt": 1773508779677,
  "lastError": "Request failed with status code 400"
 }
@@ -1,5 +1,4 @@
 {
-  "om_x100b55d69df3a888c3e9ed8887d1a4d": 1773022516624,
-  "om_x100b55d6987ec8bcc1431506934622a": 1773022571945,
-  "om_x100b55c3980b0894c3d7e12aace4404": 1773108589126
+  "om_x100b546dc09400acb4a5d0af66ca3c9": 1773508838286,
+  "om_x100b546dde1c34a4b49a888912755ac": 1773508877713
 }
@@ -1,7 +1,3 @@
 {
-  "om_x100b55de502094a8c44a6f835293240": 1773052399376,
-  "om_x100b55cff24dcca8c3e59d6831d6885": 1773124552700,
-  "om_x100b55c8c767d084c45fc7eec3f7db8": 1773127835623,
-  "om_x100b55c9372e9914c3a5744eccfbc0d": 1773131678929,
-  "om_x100b55c9dfec84a0b34c47ff1aca626": 1773132050877
+  "om_x100b546dc3b650a4b2c0139d2df31a7": 1773508824362
 }
@@ -1,18 +1,6 @@
 {
-  "om_x100b54012fb4b8acb26a1377e572a28": 1773360664219,
-  "om_x100b5403d6e544bcc451e2afa005879": 1773369731225,
-  "om_x100b5403d32b04b4c2a796466ebaf4f": 1773369823078,
-  "om_x100b5403e04f6ca8c29946e1fa6505d": 1773370088740,
-  "om_x100b5403a57550acc4e0a394c486825": 1773371068258,
-  "om_x100b540c23747504c2c49c98ae45b5f": 1773373148201,
-  "om_x100b540c32194cf4c4280e7ae0e4650": 1773373389909,
-  "om_x100b540cf21b9ca8c45f49655336827": 1773374414017,
-  "om_x100b540cf0292ca8c2b181d4bd75a25": 1773374446894,
-  "om_x100b540c8da298a0c39af0977d653ff": 1773374519675,
-  "om_x100b540c860cc4b0b36952fd4812b96": 1773374604809,
-  "om_x100b540ca90ecca4b34794a71e94eb8": 1773375100856,
-  "om_x100b540ca46704a4b36f1edd3527001": 1773375147301,
-  "om_x100b540d351884a0b20c96f48730b1d": 1773377469866,
-  "om_x100b540dcb01d4a0b3fd88e28dbba4e": 1773377629522,
-  "om_x100b540dc9bb2ca4b3671d47b831818": 1773377656011
+  "om_x100b546d172f3c68c2cea708421b99a": 1773507998614,
+  "om_x100b546d2b32cca0c329a33c339cfb5": 1773508192506,
+  "om_x100b546d32de24a8b316c197e60d6e4": 1773508545727,
+  "om_x100b546dc9d61ca0b33d802bc7615e0": 1773508722267
 }
@@ -1,19 +1,3 @@
 {
-  "om_x100b540c33ced0a8c3b77203de384e3": 1773373392794,
-  "om_x100b540ca2e850b8b2932714333e65f": 1773375171104,
-  "om_x100b540ca11ae8bcb351842dd1c37b6": 1773375229985,
-  "om_x100b540cbb43f0b0b3d91309afa563e": 1773375321590,
-  "om_x100b540cb73d2c80b275045db2fba68": 1773375391649,
-  "om_x100b540cb13eb4bcb31e7a5a9fbbbc3": 1773375487898,
-  "om_x100b540d4b4078acb33fd526aa324ea": 1773375577515,
-  "om_x100b540d46704ca0b38a5d0c7011398": 1773375628530,
-  "om_x100b540d5e21d530b4b235d9a470ccf": 1773375759355,
-  "om_x100b540d6efb8ca8b35b71db8c7e917": 1773376004012,
-  "om_x100b540d64b9a0a8b34ee257f229495": 1773376167864,
-  "om_x100b540d633e84b0b2b051a702c5c2b": 1773376223787,
-  "om_x100b540d34d174a0b3c9f1ee9a1f815": 1773377442459,
-  "om_x100b540dc61d00b0b3718efd72b20c5": 1773377677571,
-  "om_x100b540dda597ca0b2cb468e3799501": 1773377865991,
-  "om_x100b540ddbcaf4a8b247e16717525fe": 1773377873053,
-  "om_x100b540dd78910b4b31bb742001ce61": 1773377940945
+  "om_x100b546d2d6164a4c3fcf5a6d528b40": 1773508155371
 }
@@ -1,4 +1,10 @@
 {
-  "om_x100b5403a5fd7090c4db2b553189c54": 1773371059715,
-  "om_x100b540c2807c4acc32d196f4b16191": 1773373037365
+  "om_x100b546ddd8b14a0b3ddda14ce768c5": 1773508916933,
+  "om_x100b546dd78c8ca0b31b0314f910115": 1773509012676,
+  "om_x100b546def8ac8a4b38d921a2bfc8b8": 1773509140955,
+  "om_x100b546dedb654a4b3ce916a129facf": 1773509176311,
+  "om_x100b546de975dca0b27e2f24aad0d8c": 1773509244102,
+  "om_x100b546de53a94a0b38693fbd7e385c": 1773509311993,
+  "om_x100b546dffaa3cb0b35aef2747ed228": 1773509398933,
+  "om_x100b546df49c10a0b106d597fd348a3": 1773509541678
 }
@@ -10,7 +10,7 @@
        "operator.approvals",
        "operator.pairing"
      ],
-      "updatedAtMs": 1773425421022
+      "updatedAtMs": 1773511209007
    }
  }
 }
@@ -36,3 +36,4 @@
 {"timestamp":"2026-03-13T04:01:59.730Z","action":"new","sessionKey":"agent:haonan:main","senderId":"ou_be83febac9ee2afaa343c642bc697c48","source":"feishu"}
 {"timestamp":"2026-03-13T04:11:41.253Z","action":"new","sessionKey":"agent:haonan:main","senderId":"ou_be83febac9ee2afaa343c642bc697c48","source":"feishu"}
 {"timestamp":"2026-03-13T04:57:53.114Z","action":"new","sessionKey":"agent:thinker:main","senderId":"ou_a0f6627b64d2dc8b0236671603d1e47b","source":"feishu"}
+{"timestamp":"2026-03-14T17:06:39.062Z","action":"new","sessionKey":"agent:haonan:main","senderId":"ou_be83febac9ee2afaa343c642bc697c48","source":"feishu"}
@@ -1,214 +1,91 @@
---
-name: kb-bilibili-content
-description: B站内容下载 - 下载视频、字幕、专栏文章
---
-
 # KB Bilibili Content Skill

-**kb-bilibili-content** - 下载 B站视频、字幕、专栏文章。
+**kb-bilibili-content** - Download Bilibili videos and extract content.

-## 什么时候使用
+## Features

- kb-workflow 步骤 3a：下载视频或字幕
- kb-workflow 步骤 3b：提取专栏文章内容
+- Video download using **yt-dlp** (recommended)
+- Subtitle extraction
+- Article content extraction
+- Multiple quality options (360P/480P/720P/1080P)
+- Supports `BILIBILI_COOKIE` environment variable

-## 功能
+## Environment Variables

- 视频下载（使用 yt-dlp）
- 字幕提取（官方字幕）
- 专栏文章内容提取
- 多种画质选项（360P/480P/720P/1080P）
- 支持 `BILIBILI_COOKIE` 环境变量（会员视频）
+| Variable | Required | Description |
+|----------|----------|-------------|
+| `BILIBILI_COOKIE` | No | B站登录 Cookie (for member-only videos) |

-## 环境变量
-
-| 变量名 | 必需 | 说明 |
-|--------|------|------|
-| `BILIBILI_COOKIE` | 否 | B站登录 Cookie（会员视频需要） |
-
-**获取 Cookie：**
-1. 登录 bilibili.com
-2. F12 → Network → 刷新页面
-3. 从请求头复制 Cookie
+## Commands

 ```bash
-export BILIBILI_COOKIE="SESSDATA=xxx; bili_jct=xxx; DedeUserID=xxx"
+# 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"
 ```

-## 命令
-
-```bash
-# 下载字幕（推荐首先尝试）
-python3 bilibili_content.py --id "BV1xx411c7mD" --type subtitle-download --output "/path/to/output/"
-
-# 下载视频（720P，推荐）
-python3 bilibili_content.py --id "BV1xx411c7mD" --type video --output "/path/to/output/"
-
-# 下载视频（1080P）
-python3 bilibili_content.py --id "BV1xx411c7mD" --type video --quality 80 --output "/path/to/output/"
-
-# 提取专栏文章
-python3 bilibili_content.py --id "cv43780186" --type article --output "/path/to/output/"
-```
-
-## 输出格式 Schema
-
-### 必需字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `success` | boolean | 是否成功 |
-| `type` | string | 操作类型（subtitle/video/article） |
-
-### 字幕下载特有字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `has_subtitle` | boolean | 是否有官方字幕 |
-| `subtitle_file` | string | 字幕文件路径（有字幕时） |
-
-### 视频下载特有字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `filepath` | string | 视频文件路径 |
-| `filename` | string | 视频文件名 |
-| `quality` | string | 画质 |
-
-### 文章提取特有字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `filepath` | string | 文章文件路径 |
-| `content_length` | number | 内容长度 |
-
-## 成功示例
-
-### 字幕下载 - 有官方字幕
-
-```json
-{
-  "success": true,
-  "type": "subtitle-download",
-  "has_subtitle": true,
-  "subtitle_file": "content/downloads/bilibili_BV1xx411c7mD.srt",
-  "message": "官方字幕下载成功"
-}
-```
-
-### 字幕下载 - 无官方字幕
-
-```json
-{
-  "success": true,
-  "type": "subtitle-download",
-  "has_subtitle": false,
-  "message": "无官方字幕，需要下载视频进行语音识别"
-}
-```
-
-### 视频下载
+## Output

 ```json
 {
  "success": true,
  "type": "video",
-  "filename": "bilibili_BV1xx411c7mD.mp4",
-  "filepath": "content/downloads/bilibili_BV1xx411c7mD.mp4",
-  "quality": "720P",
-  "message": "下载完成"
+  "filename": "video_title.mp4",
+  "filepath": "/path/to/downloads/video_title.mp4",
+  "message": "下载完成: video_title.mp4"
 }
 ```

-### 专栏文章提取
+## Cookie Setup

-```json
-{
-  "success": true,
-  "type": "article",
-  "filename": "bilibili_cv43780186.txt",
-  "filepath": "content/bilibili_cv43780186.txt",
-  "content_length": 5234,
-  "message": "文章内容提取成功"
-}
+### 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"
 ```

-## 错误示例
-
-### 403 禁止访问
-
-```json
-{
-  "success": false,
-  "type": "video",
-  "error": "403 Forbidden",
-  "error_code": "FORBIDDEN",
-  "suggestion": "可能需要会员权限，请配置 BILIBILI_COOKIE 环境变量"
-}
-```
-
-### 视频不存在
-
-```json
-{
-  "success": false,
-  "type": "video",
-  "error": "视频不存在或已被删除",
-  "error_code": "NOT_FOUND"
-}
-```
-
-### 网络超时
-
-```json
-{
-  "success": false,
-  "type": "video",
-  "error": "下载超时",
-  "error_code": "TIMEOUT",
-  "suggestion": "请检查网络连接后重试"
-}
-```
-
-## 画质选项
-
-| 画质 | ID | 分辨率 | 说明 |
-|------|-----|--------|------|
-| 360P | 16 | 640x360 | 最快 |
-| 480P | 32 | 852x480 | 低带宽 |
-| 720P | 64 | 1280x720 | **推荐** |
-| 1080P | 80 | 1920x1080 | 最高画质 |
-
-**默认画质：** 64 (720P)
-
-## 安装
+## Installation

 ```bash
-cp -r kb-bilibili-content/ ~/.openclaw/skills/
-pip install aiohttp yt-dlp
+cp -r kb-bilibili-content/ ~/skills/
+pip install aiohttp playwright
+playwright install chromium
 ```

-## 在 kb-workflow 中的使用
+## How It Works

-```bash
-# 步骤 3a：首先尝试字幕
-python3 ~/.openclaw/skills/kb-bilibili-content/bilibili_content.py \
-    --id "BV1xx411c7mD" --type subtitle-download \
-    --output "content/downloads/"
+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

-# 判断结果：
-# - has_subtitle=true → 跳过 Whisper，直接进入深度分析
-# - has_subtitle=false → 下载视频，进入 Whisper 步骤
+## Quality Options

-# 如果无字幕，下载视频
-python3 ~/.openclaw/skills/kb-bilibili-content/bilibili_content.py \
-    --id "BV1xx411c7mD" --type video \
-    --output "content/downloads/"
-```
+| Quality | ID | Resolution | Notes |
+|---------|-----|------------|-------|
+| 16 | 360P | 640x360 | Fastest |
+| 32 | 480P | 852x480 | Default for low bandwidth |
+| 64 | 720P | 1280x720 | **Recommended** |
+| 80 | 1080P | 1920x1080 | Best quality |

-## 注意事项
-
- **无官方字幕是正常的**：很多视频没有自动生成的字幕，这不是错误
- **403 错误**：通常需要配置 Cookie
- **会员视频**：必须配置 BILIBILI_COOKIE
- **下载时间**：视频可能较大，需要耐心等待
+Default quality: **64 (720P)**
@@ -1,187 +1,46 @@
---
-name: kb-bilibili-metadata
-description: B站元数据提取 - 获取视频/专栏/UP主的详细信息
---
-
 # KB Bilibili Metadata Skill

-**kb-bilibili-metadata** - 提取 B站视频、专栏、UP主的元数据。
+**kb-bilibili-metadata** - Extract Bilibili video/article/up主 metadata.

-## 什么时候使用
+## Features

- kb-workflow 步骤 2：获取 B站内容的元数据
- 需要视频标题、作者、时长等信息
+- Video metadata extraction
+- Article (专栏) metadata extraction
+- UP主 information extraction
+- JSON output format
+- Playwright + HTTP API fallback

-## 功能
-
- 视频元数据提取
- 专栏（article）元数据提取
- UP主信息提取
- JSON 输出格式
- 支持 `--output` 参数指定输出文件
-
-## 命令
+## Commands

 ```bash
-# 视频元数据
-python3 bilibili_metadata.py --id "BV1xx411c7mD" --type video
+# Video metadata
+python bilibili_metadata.py --id "BV1xx411c7mD" --type video

-# 专栏元数据
-python3 bilibili_metadata.py --id "cv43780186" --type article
+# Article metadata
+python bilibili_metadata.py --id "cv43780186" --type article

-# UP主信息
-python3 bilibili_metadata.py --id "7770724" --type up
-
-# 指定输出文件
-python3 bilibili_metadata.py --id "BV1xx411c7mD" --type video --output "metadata/bilibili_BV1xx411c7mD.meta.json"
+# UP主 info
+python bilibili_metadata.py --id "7770724" --type up
 ```

-## 输出格式 Schema
-
-### 必需字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `success` | boolean | 是否提取成功 |
-| `platform` | string | 固定为 `bilibili` |
-| `content_id` | string | 内容 ID（BV号/cv号/uid） |
-| `content_type` | string | 内容类型（video/article/up） |
-| `title` | string | 标题（视频/专栏）或名称（UP主） |
-
-### 可选字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `author` | string | 作者/UP主名称 |
-| `duration` | string | 时长（视频） |
-| `description` | string | 简介/描述 |
-| `stats` | object | 统计数据（播放、点赞等） |
-| `output_file` | string | 输出文件路径 |
-| `error` | string | 错误信息（失败时） |
-
-## 成功示例
-
-### 视频元数据
+## Output

 ```json
 {
  "success": true,
-  "platform": "bilibili",
-  "content_type": "video",
-  "content_id": "BV1xx411c7mD",
-  "title": "视频标题",
-  "author": "UP主名字",
-  "duration": "10:30",
-  "description": "视频简介内容...",
-  "stats": {
-    "view": 100000,
-    "like": 5000,
-    "coin": 2000,
-    "favorite": 1000,
-    "share": 500,
-    "danmaku": 3000
-  },
-  "output_file": "metadata/bilibili_BV1xx411c7mD.meta.json"
+  "data": {
+    "platform": "bilibili",
+    "type": "video",
+    "id": "BV1xx411c7mD",
+    "title": "视频标题",
+    "author": "UP主名字",
+    "stats": {"view": 100000, "like": 5000}
+  }
 }
 ```

-### 专栏元数据
-
-```json
-{
-  "success": true,
-  "platform": "bilibili",
-  "content_type": "article",
-  "content_id": "cv43780186",
-  "title": "专栏标题",
-  "author": "作者名字",
-  "description": "专栏摘要...",
-  "stats": {
-    "view": 50000,
-    "like": 2000,
-    "comment": 500
-  },
-  "output_file": "metadata/bilibili_cv43780186.meta.json"
-}
-```
-
-### UP主信息
-
-```json
-{
-  "success": true,
-  "platform": "bilibili",
-  "content_type": "up",
-  "content_id": "7770724",
-  "title": "UP主名字",
-  "description": "UP主签名...",
-  "stats": {
-    "follower": 1000000,
-    "following": 100,
-    "video_count": 500
-  },
-  "output_file": "metadata/bilibili_7770724.meta.json"
-}
-```
-
-## 错误示例
-
-### 视频不存在
-
-```json
-{
-  "success": false,
-  "platform": "bilibili",
-  "content_id": "BV1invalid",
-  "content_type": "video",
-  "error": "视频不存在或已被删除"
-}
-```
-
-### 网络错误
-
-```json
-{
-  "success": false,
-  "platform": "bilibili",
-  "content_id": "BV1xx411c7mD",
-  "content_type": "video",
-  "error": "网络请求失败: Connection timeout"
-}
-```
-
-### 参数错误
-
-```json
-{
-  "success": false,
-  "error": "缺少必需参数: --id"
-}
-```
-
-## 安装
+## Installation

 ```bash
-cp -r kb-bilibili-metadata/ ~/.openclaw/skills/
-pip install httpx playwright
-playwright install chromium
+cp -r kb-bilibili-metadata/ ~/skills/
 ```
-
-## 在 kb-workflow 中的使用
-
-```bash
-# 步骤 2：元数据提取（B站视频）
-python3 ~/.openclaw/skills/kb-bilibili-metadata/bilibili_metadata.py \
-    --id "BV1xx411c7mD" --type video \
-    --output "metadata/bilibili_BV1xx411c7mD.meta.json"
-
-# 检查输出：
-# - success=true → 进入步骤 3
-# - success=false → 检查错误，可能需要重试或退出
-```
-
-## 注意事项
-
- 部分视频可能需要登录才能获取完整信息
- 专栏内容可能需要额外的 Cookie 配置
- 如果使用 Playwright 方式，首次运行需要下载浏览器
@@ -1,207 +1,102 @@
---
-name: kb-deep-analyzer
-description: AI深度分析 - 使用 AI 对内容进行深度分析和摘要
---
-
 # KB Deep Analyzer Skill

 **kb-deep-analyzer** - AI 深度内容分析工具。

-## 什么时候使用
-
- kb-workflow 步骤 5：对视频字幕或文章内容进行深度分析
- 需要生成摘要、主题、见解等
-
-## 功能
+## Features

 - 多类型分析：视频（video）、文章（article）
 - 模板化输出
 - 质量评分
 - 自动标签
- 多格式元数据支持
+- **多格式元数据支持**: 支持扁平结构 `{"title":"xxx"}` 和嵌套结构 `{"data":{"metadata":{...}}}`

-## 环境变量（必需）
+## 内容类型

-| 变量名 | 说明 | 示例 |
-|--------|------|------|
-| `AI_API_KEY` | AI API 密钥 | `your-api-key` |
-| `AI_PROVIDER` | AI 提供商 | `zhipu` / `openai` / `minimax` |
-| `AI_MODEL` | 模型名称 | `glm-5` / `gpt-4` |
-| `AI_BASE_URL` | API 端点 | `https://open.bigmodel.cn/api/coding/paas/v4` |
+| 类型 | 参数 | 输出格式 |
+|------|------|----------|
+| 视频 | `--type video` | 摘要 + 主题 + 见解 + 时间轴 |
+| 文章 | `--type article` | 300字摘要 + 主题 + 原文 |

-## 命令
+## Commands

 ```bash
-# 分析视频
-AI_API_KEY="xxx" AI_PROVIDER="zhipu" AI_MODEL="glm-5" \
-python3 -u deep_analyzer.py \
-    --metadata meta.json \
-    --content content.json \
-    --output analysis.md \
-    --type video \
-    --provider zhipu --model glm-5
+# 分析视频（默认）
+AI_API_KEY="your-api-key" AI_PROVIDER="zhipu" AI_MODEL="glm-5" \
+  python3 -u deep_analyzer.py --metadata meta.json --content content.json --output analysis.md --type video

-# 分析文章
-AI_API_KEY="xxx" AI_PROVIDER="zhipu" AI_MODEL="glm-5" \
-python3 -u deep_analyzer.py \
-    --metadata meta.json \
-    --content content.txt \
-    --output analysis.md \
-    --type article \
-    --provider zhipu --model glm-5
+# 分析文章（300字摘要 + 原文）
+AI_API_KEY="your-api-key" AI_PROVIDER="zhipu" AI_MODEL="glm-5" \
+  python3 -u deep_analyzer.py --metadata meta.json --content content.json --output analysis.md --type article
+
+# 指定 AI 提供商
+python3 -u deep_analyzer.py -m meta.json -c content.json -o out.md --type video --provider zhipu --model glm-5
 ```

-## 参数说明
+## 环境变量配置

-| 参数 | 说明 | 必需 |
-|------|------|------|
-| `--metadata` | 元数据 JSON 文件路径 | 是 |
-| `--content` | 内容文件路径（JSON/TXT） | 是 |
-| `--output` | 输出 Markdown 文件路径 | 是 |
-| `--type` | 内容类型（video/article） | 是 |
-| `--provider` | AI 提供商 | 否（默认从环境变量） |
-| `--model` | 模型名称 | 否（默认从环境变量） |
-
-## 输出格式 Schema
-
-### 必需字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `success` | boolean | 是否分析成功 |
-
-### 成功时字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `output_file` | string | 输出文件路径 |
-| `model` | string | 使用的模型 |
-| `provider` | string | 使用的提供商 |
-
-### 失败时字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `error` | string | 错误信息 |
-| `message` | string | 详细说明 |
-
-## 成功示例
-
-```json
-{
-  "success": true,
-  "output_file": "analyzed/bilibili_BV1xx411c7mD.md",
-  "model": "glm-5",
-  "provider": "zhipu"
-}
-```
-
-## 错误示例
-
-### API 调用失败
-
-```json
-{
-  "success": false,
-  "error": "AI API 调用失败: rate limit exceeded",
-  "message": "深度分析失败，整个流程终止"
-}
-```
-
-### 环境变量未设置
-
-```json
-{
-  "success": false,
-  "error": "AI_API_KEY 环境变量未设置",
-  "suggestion": "请配置 AI_API_KEY 环境变量"
-}
-```
-
-### 内容文件不存在
-
-```json
-{
-  "success": false,
-  "error": "内容文件不存在",
-  "content_file": "content/missing.json"
-}
-```
-
-## 输出格式（Markdown）
-
-### 视频分析输出
-
-```markdown
-# 视频标题
-
-## 摘要
-[200-300字的核心内容摘要]
-
-## 主要主题
- 主题1
- 主题2
- 主题3
-
-## 关键见解
- 见解1
- 见解2
-
-## 时间轴
- 00:00 - 02:30: 第一部分
- 02:30 - 05:00: 第二部分
-...
-
-## 标签
-#标签1 #标签2 #标签3
-```
-
-### 文章分析输出
-
-```markdown
-# 文章标题
-
-## 摘要
-[300字以内的核心观点]
-
-## 主要主题
- 主题1
- 主题2
-
-## 完整原文
-[原始文章内容]
-```
-
-## 安装
+**必须设置以下环境变量**：

 ```bash
-cp -r kb-deep-analyzer/ ~/.openclaw/skills/
-pip install httpx
+export AI_API_KEY="your-api-key"
+export AI_PROVIDER="zhipu"           # 或 openai, minimax
+export AI_MODEL="glm-5"           # 模型名称
+export AI_BASE_URL="https://..."    # 可选，自定义 API 地址
 ```

-## 在 kb-workflow 中的使用
+### 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
-# 步骤 5：深度分析
-# ⚠️ 必须成功，否则整个流程失败
-
-AI_API_KEY="$AI_API_KEY" AI_PROVIDER="zhipu" AI_MODEL="glm-5" \
-python3 -u ~/.openclaw/skills/kb-deep-analyzer/deep_analyzer.py \
-    --metadata "metadata/bilibili_BV1xx411c7mD.meta.json" \
-    --content "content/bilibili_BV1xx411c7mD.json" \
-    --output "analyzed/bilibili_BV1xx411c7mD.md" \
-    --type video \
-    --provider zhipu --model glm-5
-
-# 检查结果：
-# - success=true → 进入步骤 6（归档）
-# - success=false → 整个流程终止，报告错误
+python3 -u deep_analyzer.py ...
 ```

-## ⚠️ 重要提示
+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。

-1. **必须使用 `-u` 参数**：确保 Python 输出实时显示
-2. **深度分析不可跳过**：这是必须成功的步骤
-3. **失败即终止**：如果返回 `success: false`，整个流程必须停止
-4. **禁止降级**：不能使用预设模板或简单摘要"假装成功"
-5. **并发执行**：默认使用 batch_size=3 并发处理
+## 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。
@@ -1,41 +1,44 @@
 ---
 name: kb-funasr
-description: 本地语音识别 - 使用阿里 FunASR 进行中文优化的语音识别
+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。
+**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

- kb-workflow 步骤 4 的备选方案：当 Whisper 效果不佳时
- 中文语音识别（Paraformer 模型对中文优化）
- 需要高精度中文识别
+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

-| 变量名 | 必需 | 说明 |
-|--------|------|------|
-| `FUNASR_CACHE_DIR` | 否 | FunASR 模型缓存目录 |
+在使用之前，必须检查环境变量是否已经设置。

-## 命令
+| 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 中文模型，推荐）
-python3 funasr_tool.py --input video.mp4
+# 基本用法（使用 Paraformer 模型）
+python funasr.py --input video.mp4

 # 指定模型
-python3 funasr_tool.py --input video.mp4 --model paraformer-zh
+python funasr.py --input video.mp4 --model paraformer-zh

 # 指定输出目录
-python3 funasr_tool.py --input video.mp4 --output ./output
+python funasr.py --input video.mp4 --output ./output

-# 后台异步执行
-python3 funasr_tool.py --input video.mp4 --background
+# 异步执行（推荐）
+python funasr.py --input video.mp4 --background
 ```

-## 参数说明
+## 命令参数

 | 参数 | 说明 | 默认值 |
 |------|------|--------|
@@ -46,91 +49,47 @@ python3 funasr_tool.py --input video.mp4 --background

 ## 模型选项

-| 模型 | 说明 | 推荐场景 |
-|------|------|---------|
-| `paraformer-zh` | Paraformer 中文模型 | **推荐**，中文优化 |
-| `paraformer` | Paraformer 多语言模型 | 多语言场景 |
-| `sensevoice` | SenseVoice 模型 | 高精度需求 |
+- `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

-## 输出格式 Schema
+## 输出文件

-### 必需字段
+对于输入 `video.mp4`，将生成：

-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `success` | boolean | 是否识别成功 |
-| `input_file` | string | 输入文件路径 |
-| `model` | string | 使用的模型 |
+- `video.json` - 详细结果（包含时间戳、分段信息）
+- `video.txt` - 纯文本内容

-### 可选字段
+## 输出格式示例

-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `output_files` | object | 输出文件路径 |
-| `duration_seconds` | number | 音频时长 |
-| `segments` | array | 识别分段 |
-| `error` | string | 错误信息 |
-
-## 成功示例
+### JSON 格式

 ```json
 {
  "success": true,
-  "input_file": "content/downloads/bilibili_BV1xx411c7mD.mp4",
+  "input_file": "video.mp4",
  "model": "paraformer-zh",
  "output_files": {
-    "json": "content/bilibili_BV1xx411c7mD_funasr.json",
-    "txt": "content/bilibili_BV1xx411c7mD_funasr.txt"
+    "json": "video.json",
+    "txt": "video.txt"
  },
-  "duration_seconds": 630.5,
+  "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": 6.5,
-      "text": "这是第二段识别结果"
    }
  ]
 }
 ```

-## 错误示例
+## Workflow 中的使用

-### 模型未下载
-
-```json
-{
-  "success": false,
-  "error": "模型未找到",
-  "model": "paraformer-zh",
-  "suggestion": "首次运行时会自动下载模型，请确保网络连接正常"
-}
-```
-
-### 文件不存在
-
-```json
-{
-  "success": false,
-  "error": "输入文件不存在",
-  "input_file": "content/downloads/missing.mp4"
-}
-```
-
-### 内存不足
-
-```json
-{
-  "success": false,
-  "error": "内存不足",
-  "suggestion": "尝试使用较小的模型或处理较短的音频片段"
-}
+```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
@@ -139,35 +98,16 @@ python3 funasr_tool.py --input video.mp4 --background
 # 1. 安装依赖
 pip install funasr modelscope

-# 2. 首次运行时自动下载模型
-python3 funasr_tool.py --input test.mp4
+# 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
-cp -r kb-funasr/ ~/.openclaw/skills/
-```
-
-## 在 kb-workflow 中的使用
-
-```bash
-# 步骤 4 的备选方案（当 Whisper 效果不佳时）
-python3 ~/.openclaw/skills/kb-funasr/funasr_tool.py \
-    --input "content/downloads/bilibili_BV1xx411c7mD.mp4" \
-    --model paraformer-zh \
-    --output "content/"
-
-# 输出：
-# - bilibili_BV1xx411c7mD_funasr.json (完整结果)
-# - bilibili_BV1xx411c7mD_funasr.txt (纯文本)
-
-# 完成后进入步骤 5（深度分析）
+# 3. 复制技能到 skills 目录
+cp -r kb-funasr/ ~/skills/
 ```

 ## 注意事项

- **首次运行**：首次运行时会自动下载模型文件（约 1GB），需要稳定网络
- **中文优化**：Paraformer 模型对中文识别效果优于 Whisper
- **耗时操作**：语音识别可能需要几分钟到几十分钟
- **文件格式**：支持 MP3、MP4、WAV、FLAC、OGG、M4A 等常见格式
- **与 Whisper 对比**：
-  - Whisper：通用性强，支持多语言
-  - FunASR：中文优化，精度更高
+- **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
@@ -1,190 +1,43 @@
---
-name: kb-link-parser
-description: 链接解析器 - 从各平台 URL 提取平台类型、内容类型和 ID
---
-
 # KB Link Parser Skill

-**kb-link-parser** - 从各种平台 URL 中提取和解析 ID。
+**kb-link-parser** - Extract and resolve IDs from various platform URLs.

-## 什么时候使用
+## Features

- kb-workflow 步骤 1：解析用户提供的链接
- 需要判断链接属于哪个平台、什么类型的内容
+- 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

-## 功能
-
- 多平台支持：Bilibili、YouTube、知乎、小红书
- 短链接解析：b23.tv、xhslink.com 等
- 多种内容类型：video、article、question、answer 等
- 批量处理支持
- JSON 输出格式
-
-## 命令
+## Commands

 ```bash
-# 解析单个 URL
-python3 link-parser.py --url "https://www.bilibili.com/video/BV1xx411c7mD"
+# Parse single URL
+python link-parser.py --url "https://www.bilibili.com/video/BV1xx411c7mD"

-# 批量处理
-python3 link-parser.py --batch urls.txt --output results.json
+# Batch processing
+python link-parser.py --batch urls.txt --output results.json

-# 指定输出格式
-python3 link-parser.py --url "..." --format json
+# Output format
+python link-parser.py --url "..." --format json
 ```

-## 输出格式 Schema
-
-### 必需字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `success` | boolean | 是否解析成功 |
-| `platform` | string | 平台名称（成功时必需） |
-| `type` | string | 内容类型（成功时必需） |
-| `id` | string | 内容 ID（成功时必需） |
-| `error` | string | 错误信息（失败时必需） |
-
-### 可选字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `filename` | string | 建议的文件名 |
-| `message` | string | 描述信息 |
-
-## 成功示例
-
-### B站视频
+## Output

 ```json
 {
  "success": true,
-  "platform": "bilibili",
-  "type": "video",
-  "id": "BV1xx411c7mD",
-  "filename": "bilibili_BV1xx411c7mD.link.json",
-  "message": "URL解析成功"
+  "data": {
+    "platform": "bilibili",
+    "type": "video",
+    "id": "BV1xx411c7mD"
+  }
 }
 ```

-### B站专栏
-
-```json
-{
-  "success": true,
-  "platform": "bilibili",
-  "type": "article",
-  "id": "cv43780186",
-  "filename": "bilibili_cv43780186.link.json",
-  "message": "URL解析成功"
-}
-```
-
-### 知乎问答
-
-```json
-{
-  "success": true,
-  "platform": "zhihu",
-  "type": "question",
-  "id": "266309957",
-  "filename": "zhihu_266309957.link.json",
-  "message": "URL解析成功"
-}
-```
-
-### 知乎专栏文章
-
-```json
-{
-  "success": true,
-  "platform": "zhihu",
-  "type": "zhuanlan",
-  "id": "1894054073625461813",
-  "filename": "zhihu_1894054073625461813.link.json",
-  "message": "URL解析成功"
-}
-```
-
-### 知乎视频
-
-```json
-{
-  "success": true,
-  "platform": "zhihu",
-  "type": "zvideo",
-  "id": "123456789",
-  "filename": "zhihu_123456789.link.json",
-  "message": "URL解析成功"
-}
-```
-
-### 小红书笔记
-
-```json
-{
-  "success": true,
-  "platform": "xiaohongshu",
-  "type": "note",
-  "id": "abc123",
-  "filename": "xiaohongshu_abc123.link.json",
-  "message": "URL解析成功"
-}
-```
-
-## 错误示例
-
-### 不支持的平台
-
-```json
-{
-  "success": false,
-  "error": "Unsupported URL format",
-  "detail": "URL: https://example.com/video/123"
-}
-```
-
-### 无效 URL
-
-```json
-{
-  "success": false,
-  "error": "解析失败",
-  "detail": "URL: not-a-valid-url"
-}
-```
-
-## 支持的平台和类型
-
-| 平台 | 类型 | URL 示例 |
-|------|------|----------|
-| bilibili | video | `bilibili.com/video/BVxxx` |
-| bilibili | article | `bilibili.com/read/cvxxx` |
-| bilibili | up主 | `space.bilibili.com/12345` |
-| bilibili | favlist | `space.bilibili.com/123/favlist?fid=xxx` |
-| zhihu | question | `zhihu.com/question/123` |
-| zhihu | answer | `zhihu.com/question/123/answer/456` |
-| zhihu | zhuanlan | `zhuanlan.zhihu.com/p/123` |
-| zhihu | zvideo | `zhihu.com/zvideo/123` |
-| xiaohongshu | note | `xiaohongshu.com/explore/xxx` |
-| youtube | video | `youtube.com/watch?v=xxx` |
-
-## 安装
+## Installation

 ```bash
-cp -r kb-link-parser/ ~/.openclaw/skills/
-pip install requests
-```
-
-## 在 kb-workflow 中的使用
-
-```bash
-# 步骤 1：链接解析
-python3 ~/.openclaw/skills/kb-link-parser/link-parser.py --url "[用户提供的URL]"
-
-# 解析输出后：
-# - 检查 success 字段
-# - 提取 platform, type, id
-# - 生成 pending/{platform}_{id}.link.json
-# - 进入步骤 2（元数据提取）
+cp -r kb-link-parser/ ~/skills/
 ```
@@ -1,146 +1,66 @@
---
-name: kb-ocr-processor
-description: 视频OCR识别 - 从视频帧中提取文字
---
-
 # KB OCR Processor Skill

-**kb-ocr-processor** - 从视频帧中提取文字，作为语音识别的补充或替代方案。
+**kb-ocr-processor** - Extract text from video frames using OCR.

-## 什么时候使用
+## Features

- kb-workflow 步骤 4 的降级方案：当 Whisper 语音识别效果不佳时
- 音频质量差、有背景音乐干扰、多种语言混合等情况
+- **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

-## 功能
-
- 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
-
-## 命令
+## Commands

 ```bash
-# 方式1: 直接从视频截帧并 OCR（推荐）
-python3 ocr_processor.py --video ./video.mp4 --output ocr.json
+# 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
-python3 ocr_processor.py --input ./frames/ --output ocr.json
+python ocr_processor.py --input ./frames/ --output ocr.json

 # 指定截帧帧率（默认1帧/秒）
-python3 ocr_processor.py --video ./video.mp4 --fps 2 --output ocr.json
+python ocr_processor.py --video ./video.flv --fps 2 --output ocr.json

 # 同时生成合并的文本文件
-python3 ocr_processor.py --video ./video.mp4 --output ocr.json --merge
+python ocr_processor.py --video ./video.flv --output ocr.json --merge

 # 使用 Tesseract 引擎
-python3 ocr_processor.py --input ./frames/ --engine tesseract --output ocr.json
+python ocr_processor.py --input ./frames/ --engine tesseract --output ocr.json
 ```

-## 输出格式 Schema
+## Workflow 中的使用

-### 必需字段
+```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
+```

-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `success` | boolean | 是否识别成功 |
-| `engine` | string | 使用的 OCR 引擎 |
-| `total_frames` | number | 处理的总帧数 |
-
-### 可选字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `results` | array | 每帧的识别结果 |
-| `merged_text` | string | 合并后的文本 |
-| `output_file` | string | 输出文件路径 |
-| `error` | string | 错误信息 |
-
-## 成功示例
+## Output

 ```json
 {
  "success": true,
  "engine": "easyocr",
  "total_frames": 619,
-  "output_file": "content/bilibili_BV1xx411c7mD_ocr.json",
  "results": [
    {
      "source": "0001.jpg",
      "text": "字幕文字",
      "confidence": 0.95
-    },
-    {
-      "source": "0002.jpg",
-      "text": "更多字幕文字",
-      "confidence": 0.92
    }
-  ],
-  "merged_text": "字幕文字\n更多字幕文字\n..."
+  ]
 }
 ```

-## 错误示例
-
-### 视频文件不存在
-
-```json
-{
-  "success": false,
-  "error": "视频文件不存在",
-  "video": "./missing.mp4"
-}
-```
-
-### ffmpeg 未安装
-
-```json
-{
-  "success": false,
-  "error": "ffmpeg 未安装",
-  "suggestion": "请运行: sudo apt-get install ffmpeg"
-}
-```
-
-### 内存不足
-
-```json
-{
-  "success": false,
-  "error": "内存不足",
-  "suggestion": "尝试降低帧率或处理较短的片段"
-}
-```
-
-## 安装
+## Installation

 ```bash
-cp -r kb-ocr-processor/ ~/.openclaw/skills/
-pip install easyocr pytesseract Pillow
+cp -r kb-ocr-processor/ ~/skills/
+pip install -r requirements.txt

-# 系统依赖
-sudo apt-get install ffmpeg tesseract-ocr tesseract-ocr-chi-sim tesseract-ocr-eng
+# 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）
 ```
-
-## 在 kb-workflow 中的使用
-
-```bash
-# 步骤 4 的降级方案（当 Whisper 效果不佳时）
-python3 ~/.openclaw/skills/kb-ocr-processor/ocr_processor.py \
-    --video "content/downloads/bilibili_BV1xx411c7mD.mp4" \
-    --output "content/bilibili_BV1xx411c7mD_ocr.json" \
-    --merge
-
-# 输出：
-# - bilibili_BV1xx411c7mD_ocr.json (完整结果)
-# - bilibili_BV1xx411c7mD_ocr.txt (合并文本)
-```
-
-## 注意事项
-
- **耗时操作**：OCR 处理需要截帧和识别，可能较慢
- **帧率选择**：1帧/秒适合大多数场景，更高帧率增加精度但耗时更长
- **引擎选择**：EasyOCR 精度较高，Tesseract 速度较快
- **适用场景**：音频质量差、背景音乐干扰、多语言混合时使用
@@ -1,40 +1,44 @@
 ---
 name: kb-whisper
-description: 本地语音识别 - 使用 Whisper.cpp 将音视频转文字
+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。
+**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

- kb-workflow 步骤 4：视频无官方字幕时进行语音识别
- 需要将音视频转为文字
+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

-| 变量名 | 必需 | 说明 |
-|--------|------|------|
-| `WHISPER_CPP_PATH` | 是 | whisper.cpp 安装路径 |
+在使用之前，必须检查环境变量是否已经设置。

-## 命令
+| 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
-python3 whisper.py --input video.mp4
+python whisper.py --input video.mp4

-# 指定模型（推荐 base）
-python3 whisper.py --input video.mp4 --model base
+# 指定模型（默认 tiny）
+python whisper.py --input video.mp4 --model base

 # 指定输出目录
-python3 whisper.py --input video.mp4 --output ./output
+python whisper.py --input video.mp4 --output ./output

-# 后台异步执行
-python3 whisper.py --input video.mp4 --background
+# 异步执行（推荐）
+python whisper.py --input video.mp4 --background
 ```

-## 参数说明
+## 命令参数

 | 参数 | 说明 | 默认值 |
 |------|------|--------|
@@ -45,94 +49,51 @@ python3 whisper.py --input video.mp4 --background

 ## 模型选项

-| 模型 | 速度 | 精度 | 推荐场景 |
-|------|------|------|---------|
-| `tiny` | 最快 | 较低 | 快速预览 |
-| `base` | 快 | 良好 | **推荐** |
-| `small` | 中等 | 高 | 精确识别 |
-| `medium` | 慢 | 很高 | 专业场景 |
-| `large` | 最慢 | 最高 | 最高精度 |
+- `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

-## 输出格式 Schema
+## 输出文件

-### 必需字段
+对于输入 `video.mp4`，将生成：

-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `success` | boolean | 是否识别成功 |
-| `input_file` | string | 输入文件路径 |
-| `model` | string | 使用的模型 |
+- `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

-### 可选字段
+## 输出格式示例

-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `output_files` | object | 输出文件路径 |
-| `duration_seconds` | number | 音频时长 |
-| `segments` | array | 识别分段 |
-| `error` | string | 错误信息 |
-
-## 成功示例
+### JSON 格式

 ```json
 {
  "success": true,
-  "input_file": "content/downloads/bilibili_BV1xx411c7mD.mp4",
-  "model": "base",
+  "input_file": "video.mp4",
+  "model": "tiny",
  "output_files": {
-    "json": "content/bilibili_BV1xx411c7mD.json",
-    "txt": "content/bilibili_BV1xx411c7mD.txt",
-    "srt": "content/bilibili_BV1xx411c7mD.srt"
+    "json": "video.json",
+    "srt": "video.srt",
+    "txt": "video.txt"
  },
-  "duration_seconds": 630.5,
+  "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": 6.5,
-      "text": "这是第二段字幕"
    }
  ]
 }
 ```

-## 错误示例
+## Workflow 中的使用

-### 模型未下载
-
-```json
-{
-  "success": false,
-  "error": "模型文件不存在",
-  "model": "base",
-  "suggestion": "请运行: cd ~/whisper.cpp && ./models/download-ggml-model.sh base"
-}
-```
-
-### 文件不存在
-
-```json
-{
-  "success": false,
-  "error": "输入文件不存在",
-  "input_file": "content/downloads/missing.mp4"
-}
-```
-
-### 内存不足
-
-```json
-{
-  "success": false,
-  "error": "内存不足，无法加载模型",
-  "suggestion": "尝试使用更小的模型（tiny/base）"
-}
+```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
@@ -142,39 +103,22 @@ python3 whisper.py --input video.mp4 --background
 git clone https://github.com/ggerganov/whisper.cpp.git ~/whisper.cpp

 # 2. 编译
-cd ~/whisper.cpp && make
+cd ~/whisper.cpp
+make

-# 3. 下载模型
-./models/download-ggml-model.sh base
+# 3. 下载模型（tiny）
+./models/download-ggml-model.sh tiny

-# 4. 设置环境变量
-echo 'export WHISPER_CPP_PATH="$HOME/whisper.cpp"' >> ~/.kb_env
-source ~/.kb_env
+# 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
-cp -r kb-whisper/ ~/.openclaw/skills/
-```
-
-## 在 kb-workflow 中的使用
-
-```bash
-# 步骤 4：语音识别（当 has_subtitle=false 时）
-python3 ~/.openclaw/skills/kb-whisper/whisper.py \
-    --input "content/downloads/bilibili_BV1xx411c7mD.mp4" \
-    --model base \
-    --output "content/"
-
-# 输出：
-# - bilibili_BV1xx411c7mD.json (完整结果)
-# - bilibili_BV1xx411c7mD.txt (纯文本)
-# - bilibili_BV1xx411c7mD.srt (字幕文件)
-
-# 完成后进入步骤 5（深度分析）
+# 5. 复制技能到 skills 目录
+cp -r kb-whisper/ ~/skills/
 ```

 ## 注意事项

- **耗时操作**：语音识别可能需要几分钟到几十分钟，取决于视频长度
- **异步执行**：推荐使用 `--background` 参数
- **模型选择**：base 模型平衡速度和精度，适合大多数场景
- **文件格式**：支持 MP3、MP4、WAV、FLAC、OGG、M4A 等常见格式
+- **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
@@ -5,698 +5,200 @@ description: 当收到"添加到知识库 + 链接" 或"保存到知识库 + 链

 # KB Workflow

-## 触发条件
+## 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

-## 状态机定义
+### 执行前必须检查

-工作流是一个严格的状态机，每个状态有明确的前置条件和后置条件：
+在执行 workflow 之前，必须检查环境变量是否已经设置。

-| 状态 | 前置条件 | 执行动作 | 成功后进入 | 失败处理 |
-|------|---------|---------|-----------|---------|
-| `init` | 用户触发 + URL 有效 | 创建 state.json，检查环境变量 | `link_parse` | 报错退出 |
-| `link_parse` | state.json 存在 | 解析链接，提取平台和ID | `metadata` | 记录错误，最多重试1次 |
-| `metadata` | platform, content_id 已知 | 提取元数据 | `content` | 记录错误，最多重试1次 |
-| `content` | 元数据文件存在 | 下载内容/字幕 | `analyze` 或 `whisper` | 根据错误类型处理 |
-| `whisper` | 视频已下载，无官方字幕 | 语音识别转文字 | `analyze` | 尝试 OCR 降级 |
-| `analyze` | 有文本内容（字幕/识别结果/文章） | AI 深度分析 | `archive` | **流程失败，报错退出** |
-| `archive` | 分析报告存在 | 归档 + Git 提交 | `done` | 记录错误，可手动处理 |
-| `done` | - | 汇报完成 | - | - |
-
-### 状态判断流程图
-
-```
-用户发送链接
-    ↓
-┌─────────────────────────────────────┐
-│ Step 0: 环境变量检查                 │
-│ 缺少必需变量 → 报错退出              │
-└─────────────────────────────────────┘
-    ↓
-┌─────────────────────────────────────┐
-│ Step 1: 链接解析 (link_parse)        │
-│ 输出: platform, content_type, id     │
-└─────────────────────────────────────┘
-    ↓
-┌─────────────────────────────────────┐
-│ Step 2: 元数据提取 (metadata)        │
-│ 输出: title, author, etc.            │
-└─────────────────────────────────────┘
-    ↓
-┌─────────────────────────────────────┐
-│ content_type 判断                    │
-├─────────────────────────────────────┤
-│ video/zvideo → Step 3a (视频处理)   │
-│ article/zhuanlan/question → Step 3b │
-└─────────────────────────────────────┘
-    ↓
-┌─────────────────────────────────────┐
-│ Step 3a: 视频处理                    │
-│ 有官方字幕? → 是 → Step 5           │
-│            → 否 → Step 4 (Whisper)  │
-└─────────────────────────────────────┘
-    ↓
-┌─────────────────────────────────────┐
-│ Step 3b: 文章处理                    │
-│ 提取文字 → 直接进入 Step 5          │
-└─────────────────────────────────────┘
-    ↓
-┌─────────────────────────────────────┐
-│ Step 4: Whisper 语音识别 (可选)      │
-│ 无字幕时执行，生成 text/json/srt     │
-└─────────────────────────────────────┘
-    ↓
-┌─────────────────────────────────────┐
-│ Step 5: 深度分析 (analyze)           │
-│ ⚠️ 必须成功，否则流程失败            │
-└─────────────────────────────────────┘
-    ↓
-┌─────────────────────────────────────┐
-│ Step 6: 归档 (archive)               │
-│ 复制到归档目录 + Git 提交            │
-└─────────────────────────────────────┘
-    ↓
-   完成
-```
-
---
-
-## 环境变量清单
-
-### 必需变量（缺失则报错退出）
-
-| 变量名 | 用途 | 示例 |
-|--------|------|------|
-| `AI_API_KEY` | AI API 密钥 | `your-api-key` |
-| `AI_PROVIDER` | AI 提供商 | `zhipu` / `openai` / `minimax` |
-| `AI_MODEL` | 模型名称 | `glm-5` / `gpt-4` |
-| `AI_BASE_URL` | API 端点 | `https://open.bigmodel.cn/api/coding/paas/v4` |
-
-### 可选变量
-
-| 变量名 | 用途 | 用途说明 |
-|--------|------|---------|
-| `BILIBILI_COOKIE` | B站登录态 | 下载会员视频 |
-| `ZHIHU_COOKIE` | 知乎登录态 | 访问专栏等 |
-| `HF_TOKEN` | HuggingFace Token | 说话者分离功能 |
-| `KB_ARCHIVE_PATH` | 归档目录 | 默认 `~/work/zq/30.资源/AI分析` |
-| `KB_GIT_ROOT` | Git 仓库根目录 | 默认 `~/work/zq` |
-| `KB_GIT_USER` | Git 提交用户名 | 归档时使用 |
-| `KB_GIT_EMAIL` | Git 提交邮箱 | 归档时使用 |
-
-### 配置方法
+**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
-# 创建专用配置文件
-cat > ~/.kb_env << 'EOF'
 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"
-export BILIBILI_COOKIE="SESSDATA=xxx; bili_jct=xxx"
-export KB_ARCHIVE_PATH="$HOME/work/zq/30.资源/AI分析"
-export KB_GIT_ROOT="$HOME/work/zq"
-EOF
-
-# 在 ~/.bashrc 中添加
-[ -f ~/.kb_env ] && source ~/.kb_env
 ```

---
+**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` |
+| `KB_ARCHIVE_PATH` | 归档目录路径 | `~/work/zq/30.资源/AI分析` |
+| `KB_GIT_ROOT` | Git仓库根目录 | `~/work/zq` |
+| `HF_TOKEN` | HuggingFace Token（说话者分离） | `hf_xxx` |

-## 步骤详解
+### 检查命令

-### Step 0: 环境变量检查
-
-**前置条件：** 无
-
-**执行命令：**
 ```bash
-# 加载环境变量
-if [ -f ~/.kb_env ]; then source ~/.kb_env; fi
+# 从 ~/.kb_env 加载环境变量
+if [ -f ~/.kb_env ]; then
+    source ~/.kb_env
+fi
+
+# 检查必须的环境变量
+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 ~/.kb_env 文件"
+    exit 1
+fi

-# 检查必需变量
-for var in AI_API_KEY AI_PROVIDER AI_MODEL AI_BASE_URL; do
-    if [ -z "${!var}" ]; then
-        echo "❌ 错误：$var 未设置"
-        exit 1
-    fi
-done
 echo "✅ 环境变量检查通过"
 ```

-**成功标准：**
- 所有必需变量已设置
- 输出 "✅ 环境变量检查通过"
+## Workflow 执行方式

-**失败处理：**
- 缺少任何必需变量 → 报错退出，提示用户配置
+### 方式一：一键处理（推荐）

-**输出示例：**
-```
-✅ 环境变量检查通过
-   AI_PROVIDER: zhipu
-   AI_MODEL: glm-5
-```
+使用管道 CLI 一键完成所有步骤：

-**错误示例：**
-```
-❌ 错误：AI_API_KEY 未设置
-请配置以下环境变量：
-  - AI_API_KEY
-  - AI_PROVIDER
-  - AI_MODEL
-  - AI_BASE_URL
-```
-
---
-
-### Step 1: 链接解析
-
-**前置条件：**
- 环境变量检查通过
- URL 格式有效
-
-**执行命令：**
 ```bash
-# 获取目录路径
-python3 ~/.openclaw/skills/kb-workflow/kb_workflow.py --pending
-
-# 解析链接
-python3 ~/.openclaw/skills/kb-link-parser/link-parser.py --url "[URL]"
+python3 kb-workflow/kb_workflow.py process "https://www.bilibili.com/video/BVxxx"
 ```

-**成功标准：**
- 返回 JSON 包含 `success: true`
- 包含字段：`platform`, `content_type`, `content_id`
- 生成文件：`pending/{platform}_{id}.link.json`
+**该命令自动执行：**
+1. URL 解析 → 识别平台和内容 ID
+2. 元数据提取 → 获取标题、作者等信息
+3. 内容下载 → 字幕/视频/文章内容
+4. 语音识别 → Whisper/FunASR（如需要）
+5. 深度分析 → AI 生成分析报告
+6. 归档 → 移动到归档目录

-**失败处理：**
- 不支持的平台 → 报错退出，告知用户支持的平台
- URL 无效 → 报错退出，请用户提供正确链接
-
-**输出示例：**
-```json
-{
-  "success": true,
-  "platform": "bilibili",
-  "content_type": "video",
-  "content_id": "BV1xx411c7mD",
-  "output_file": "pending/bilibili_BV1xx411c7mD.link.json"
-}
-```
-
-**错误示例：**
-```json
-{
-  "success": false,
-  "error": "不支持的平台: example.com",
-  "supported_platforms": ["bilibili.com", "zhihu.com", "youtube.com"]
-}
-```
-
---
-
-### Step 2: 元数据提取
-
-**前置条件：**
- Step 1 完成
- `platform`, `content_type`, `content_id` 已知
-
-**执行命令：**
+**命令参数：**
 ```bash
-# 获取目录路径
-python3 ~/.openclaw/skills/kb-workflow/kb_workflow.py --metadata
+# 指定输出目录
+python3 kb-workflow/kb_workflow.py process "https://..." -o /path/to/output

-# 根据平台选择命令
-# B站视频
-python3 ~/.openclaw/skills/kb-bilibili-metadata/bilibili_metadata.py \
-    --id "[BV号]" --type video \
-    --output "metadata/bilibili_[BV号].meta.json"
-
-# B站专栏
-python3 ~/.openclaw/skills/kb-bilibili-metadata/bilibili_metadata.py \
-    --id "[cv号]" --type article \
-    --output "metadata/bilibili_cv[cv号].meta.json"
-
-# 知乎问答
-python3 ~/.openclaw/skills/kb-zhihu-metadata/zhihu_metadata.py \
-    --id "[问题ID]" --type question \
-    --output "metadata/zhihu_[ID].meta.json"
-
-# 知乎视频
-python3 ~/.openclaw/skills/kb-zhihu-metadata/zhihu_metadata.py \
-    --id "[视频ID]" --type zvideo \
-    --output "metadata/zhihu_[ID].meta.json"
+# 强制重新执行（忽略已完成状态）
+python3 kb-workflow/kb_workflow.py process "https://..." --no-skip
 ```

-**成功标准：**
- 返回 JSON 包含 `success: true`
- 包含字段：`platform`, `content_id`, `title`
- 生成文件：`metadata/{platform}_{id}.meta.json`
+### 方式二：手动分步执行（高级）

-**失败处理：**
- 网络错误 → 重试1次
- 内容不存在 → 报错退出
- 需要登录 → 提示配置 Cookie
+如需更细粒度控制，可手动调用各子技能：

-**输出示例：**
-```json
-{
-  "success": true,
-  "platform": "bilibili",
-  "content_id": "BV1xx411c7mD",
-  "content_type": "video",
-  "title": "视频标题",
-  "author": "UP主名字",
-  "duration": "10:30",
-  "output_file": "metadata/bilibili_BV1xx411c7mD.meta.json"
-}
-```
-
-**错误示例：**
-```json
-{
-  "success": false,
-  "error": "视频不存在或已被删除",
-  "content_id": "BV1xx411c7mD"
-}
-```
-
---
-
-### Step 3a: 视频内容下载
-
-**前置条件：**
- Step 2 完成
- `content_type` 为 `video` 或 `zvideo`
-
-**执行命令：**
 ```bash
-# 获取目录路径
-python3 ~/.openclaw/skills/kb-workflow/kb_workflow.py --content_downloads
+# 1. 查重
+python3 kb-workflow/kb_workflow.py --check --url "https://..."

-# B站视频 - 优先尝试字幕
-python3 ~/.openclaw/skills/kb-bilibili-content/bilibili_content.py \
-    --id "[BV号]" --type subtitle-download \
-    --output "content/downloads/"
+# 2. 一键处理
+python3 kb-workflow/kb_workflow.py process "https://..."

-# 检查是否有官方字幕
-# 如果 has_subtitle=true → 进入 Step 5
-# 如果 has_subtitle=false → 下载视频，进入 Step 4
+# 3. 归档
+python3 kb-workflow/kb_workflow.py --archive-file "/path/to/report.md"
 ```

-**成功标准：**
- 返回 JSON 包含 `success: true`
- `has_subtitle=true`: 有字幕文件
- `has_subtitle=false`: 视频文件下载成功
+## 支持的平台和内容类型

-**失败处理：**
- 403 禁止访问 → 检查 BILIBILI_COOKIE 配置
- 网络超时 → 重试1次
- 会员视频 → 提示需要配置 Cookie
+| 平台 | 内容类型 | URL 示例 |
+|------|----------|----------|
+| B站 | 视频 | `bilibili.com/video/BVxxx` |
+| B站 | 专栏 | `bilibili.com/read/cvxxx` |
+| 知乎 | 问答 | `zhihu.com/question/xxx` |
+| 知乎 | 专栏 | `zhuanlan.zhihu.com/p/xxx` |
+| 知乎 | 视频 | `zhihu.com/zvideo/xxx` |
+
+## 处理流程

-**输出示例（有字幕）：**
-```json
-{
-  "success": true,
-  "type": "subtitle",
-  "has_subtitle": true,
-  "subtitle_file": "content/downloads/bilibili_BV1xx411c7mD.srt",
-  "message": "官方字幕下载成功"
-}
 ```
-
-**输出示例（无字幕）：**
-```json
-{
-  "success": true,
-  "type": "subtitle",
-  "has_subtitle": false,
-  "message": "无官方字幕，需要下载视频进行语音识别"
-}
+┌─────────────────────────────────────────────────────────────┐
+│                    kb-workflow process                       │
+├─────────────────────────────────────────────────────────────┤
+│  1. Parse     → LinkParser 解析 URL                         │
+│  2. Download  → 平台适配器获取元数据和内容                    │
+│  3. ASR       → 语音识别（视频无字幕时）                      │
+│  4. Analyze   → DeepAnalyzer 生成分析报告                    │
+│  5. Archive   → 归档到 KB_ARCHIVE_PATH                      │
+└─────────────────────────────────────────────────────────────┘
 ```

-**错误示例：**
-```json
-{
-  "success": false,
-  "error": "403 Forbidden - 可能需要会员权限",
-  "suggestion": "请配置 BILIBILI_COOKIE 环境变量"
-}
-```
-
---
-
-### Step 3b: 文章内容提取
-
-**前置条件：**
- Step 2 完成
- `content_type` 为 `article`、`zhuanlan` 或 `question`
-
-**执行命令：**
-```bash
-# B站专栏
-python3 ~/.openclaw/skills/kb-bilibili-content/bilibili_content.py \
-    --id "[cv号]" --type article \
-    --output "content/"
-
-# 知乎专栏
-python3 ~/.openclaw/skills/kb-zhihu-content/zhihu_content.py \
-    --type article-from-media \
-    --output "content/"
-```
-
-**成功标准：**
- 返回 JSON 包含 `success: true`
- 包含字段：`filepath`, `content_length`
- 跳过 Step 4，直接进入 Step 5
-
-**输出示例：**
-```json
-{
-  "success": true,
-  "type": "article",
-  "filepath": "content/bilibili_cv43780186.txt",
-  "content_length": 5234,
-  "message": "文章内容提取成功"
-}
-```
-
---
-
-### Step 4: Whisper 语音识别
-
-**前置条件：**
- Step 3a 完成
- `has_subtitle=false`
- 视频文件已下载
-
-**执行命令：**
-```bash
-# 获取目录路径
-python3 ~/.openclaw/skills/kb-workflow/kb_workflow.py --content
-
-# 普通识别
-python3 ~/.openclaw/skills/kb-whisper/whisper.py \
-    --input "content/downloads/bilibili_BV1xx411c7mD.mp4" \
-    --model base \
-    --output "content/"
-
-# 带说话者分离（需要 HF_TOKEN）
-if [ -n "$HF_TOKEN" ]; then
-    python3 ~/.openclaw/skills/kb-whisper-diarization/whisper_diarization.py \
-        --input "content/downloads/bilibili_BV1xx411c7mD.mp4" \
-        --model base --diarize \
-        --output "content/"
-fi
-```
-
-**成功标准：**
- 返回 JSON 包含 `success: true`
- 生成文件：`.json`, `.txt`, `.srt`
-
-**失败处理：**
- 模型未下载 → 提示下载模型
- 内存不足 → 尝试更小的模型
- Whisper 失败 → 尝试 OCR 降级
-
-**输出示例：**
-```json
-{
-  "success": true,
-  "input_file": "content/downloads/bilibili_BV1xx411c7mD.mp4",
-  "model": "base",
-  "output_files": {
-    "json": "content/bilibili_BV1xx411c7mD.json",
-    "txt": "content/bilibili_BV1xx411c7mD.txt",
-    "srt": "content/bilibili_BV1xx411c7mD.srt"
-  },
-  "duration_seconds": 630
-}
-```
-
---
-
-### Step 5: 深度分析
-
-**前置条件：**
- 有文本内容（字幕/识别结果/文章）
- 元数据文件存在
-
-**执行命令：**
-```bash
-# 获取目录路径
-python3 ~/.openclaw/skills/kb-workflow/kb_workflow.py --analyzed
-
-# 分析视频
-AI_API_KEY="$AI_API_KEY" AI_PROVIDER="zhipu" AI_MODEL="glm-5" \
-python3 -u ~/.openclaw/skills/kb-deep-analyzer/deep_analyzer.py \
-    --metadata "metadata/bilibili_BV1xx411c7mD.meta.json" \
-    --content "content/bilibili_BV1xx411c7mD.json" \
-    --output "analyzed/bilibili_BV1xx411c7mD.md" \
-    --type video \
-    --provider zhipu --model glm-5
-
-# 分析文章
-AI_API_KEY="$AI_API_KEY" AI_PROVIDER="zhipu" AI_MODEL="glm-5" \
-python3 -u ~/.openclaw/skills/kb-deep-analyzer/deep_analyzer.py \
-    --metadata "metadata/xxx.meta.json" \
-    --content "content/xxx.txt" \
-    --output "analyzed/xxx.md" \
-    --type article \
-    --provider zhipu --model glm-5
-```
-
-**成功标准：**
- 返回 JSON 包含 `success: true`
- 生成分析报告文件
-
-**失败处理：**
- ⚠️ **深度分析失败 = 整个流程失败**
- API 调用失败 → 记录错误，**终止流程**
- **禁止**使用预设模板或降级内容
-
-**输出示例：**
-```json
-{
-  "success": true,
-  "output_file": "analyzed/bilibili_BV1xx411c7mD.md",
-  "model": "glm-5",
-  "provider": "zhipu"
-}
-```
-
-**错误示例：**
-```json
-{
-  "success": false,
-  "error": "AI API 调用失败: rate limit exceeded",
-  "message": "深度分析失败，整个流程终止"
-}
-```
-
---
-
-### Step 6: 归档
-
-**前置条件：**
- Step 5 完成
- 分析报告文件存在
-
-**执行命令：**
-```bash
-# 归档
-python3 ~/.openclaw/skills/kb-workflow/kb_workflow.py \
-    --archive-file "analyzed/bilibili_BV1xx411c7mD.md"
-
-# 如果配置了 Git，会自动提交
-```
-
-**成功标准：**
- 文件复制到归档目录
- Git 提交成功（如果配置）
-
-**失败处理：**
- 归档失败 → 记录错误，文件仍可用
- Git 失败 → 不影响主流程
-
-**输出示例：**
-```json
-{
-  "success": true,
-  "archive_path": "~/work/zq/30.资源/AI分析/2026-03-13/bilibili_BV1xx411c7mD.md",
-  "git_commit": "abc1234"
-}
-```
-
---
-
 ## 目录结构

+**工作目录（默认 ~/work/knowledge-base/）：**
+```
+~/work/knowledge-base/
+├── pending/           # 待处理链接
+├── metadata/          # 元数据 JSON
+├── content/           # 内容文件
+│   └── downloads/     # 下载的视频/字幕
+└── analyzed/          # 分析报告
 ```
-~/work/knowledge-base/           # 知识库根目录
-├── pending/                     # 待处理链接
-│   └── {platform}_{id}.link.json
-├── metadata/                    # 元数据
-│   └── {platform}_{id}.meta.json
-├── content/                     # 内容
-│   ├── downloads/               # 视频下载
-│   │   └── {platform}_{id}.mp4
-│   ├── {platform}_{id}.txt      # 文本内容
-│   └── {platform}_{id}.json     # Whisper 结果
-└── analyzed/                    # 深度分析报告
-    └── {platform}_{id}.md

-~/work/zq/30.资源/AI分析/        # 归档目录
+**归档目录（默认 ~/work/zq/30.资源/AI分析/）：**
+```
+~/work/zq/30.资源/AI分析/
 └── YYYY-MM-DD/
-    └── {platform}_{id}.md
+    └── {platform}_{id}.md  # 深度分析报告
 ```

---
-
-## 子技能清单
-
-| 步骤 | 子技能 | 命令 |
-|------|--------|------|
-| 1 | kb-link-parser | `--url "[URL]"` |
-| 2 | kb-bilibili-metadata | `--id "[ID]" --type video --output "[PATH]"` |
-| 2 | kb-zhihu-metadata | `--id "[ID]" --type question --output "[PATH]"` |
-| 3a | kb-bilibili-content | `--id "[ID]" --type subtitle-download --output "[PATH]"` |
-| 3a | kb-bilibili-content | `--id "[ID]" --type video --output "[PATH]"` |
-| 3b | kb-bilibili-content | `--id "[ID]" --type article --output "[PATH]"` |
-| 3b | kb-zhihu-content | `--type article-from-media --output "[PATH]"` |
-| 4 | kb-whisper | `--input "[VIDEO]" --model base --output "[PATH]"` |
-| 4 | kb-whisper-diarization | `--input "[VIDEO]" --model base --diarize --output "[PATH]"` |
-| 5 | kb-deep-analyzer | `--metadata "[META]" --content "[CONTENT]" --output "[OUT]" --type video` |
-
---
-
-## 端到端示例
-
-### 示例 1: B站视频（有官方字幕）
-
-**输入：** `添加到知识库 https://www.bilibili.com/video/BV1xx411c7mD`
-
-```
-Step 0: ✅ 环境变量检查通过
-Step 1: ✅ 链接解析 → bilibili, video, BV1xx411c7mD
-Step 2: ✅ 元数据提取 → 标题: "xxx", 作者: "xxx"
-Step 3a: ✅ 字幕下载 → has_subtitle=true, bilibili_BV1xx411c7mD.srt
-Step 4: ⏭️ 跳过（有官方字幕）
-Step 5: ✅ 深度分析 → bilibili_BV1xx411c7mD.md
-Step 6: ✅ 归档 → ~/work/zq/30.资源/AI分析/2026-03-13/
-```
-
-### 示例 2: B站视频（无官方字幕）
-
-**输入：** `添加到知识库 https://www.bilibili.com/video/BV1yy411c7mE`
-
-```
-Step 0: ✅ 环境变量检查通过
-Step 1: ✅ 链接解析 → bilibili, video, BV1yy411c7mE
-Step 2: ✅ 元数据提取 → 标题: "xxx", 时长: "15:30"
-Step 3a: ⚠️ 无官方字幕 → 下载视频
-Step 3a: ✅ 视频下载 → bilibili_BV1yy411c7mE.mp4
-Step 4: ✅ Whisper 识别 → bilibili_BV1yy411c7mE.json/txt/srt
-Step 5: ✅ 深度分析 → bilibili_BV1yy411c7mE.md
-Step 6: ✅ 归档
-```
-
-### 示例 3: 403 下载失败
-
-**输入：** `添加到知识库 https://www.bilibili.com/video/BV1zz411c7mF`
-
-```
-Step 0: ✅ 环境变量检查通过
-Step 1: ✅ 链接解析
-Step 2: ✅ 元数据提取
-Step 3a: ❌ 403 Forbidden
-         → 可能需要会员权限
-         → 请配置 BILIBILI_COOKIE 环境变量
-         → 流程暂停，等待用户处理
-```
-
---
-
-## 常见错误处理
-
-### 错误 1: 环境变量未设置
-
-**症状：** `❌ 错误：AI_API_KEY 未设置`
-
-**解决：**
-```bash
-echo 'export AI_API_KEY="your-key"' >> ~/.kb_env
-source ~/.kb_env
-```
-
-### 错误 2: 403 下载失败
-
-**症状：** B站视频下载返回 403
-
-**解决：**
-1. 获取 B站 Cookie（F12 → Network → Copy Cookie）
-2. 配置环境变量：
-```bash
-echo 'export BILIBILI_COOKIE="SESSDATA=xxx; bili_jct=xxx"' >> ~/.kb_env
-source ~/.kb_env
-```
-
-### 错误 3: 深度分析失败
-
-**症状：** `success: false` from kb-deep-analyzer
-
-**处理：**
- **整个流程终止**
- 检查 AI_API_KEY 是否有效
- 检查 AI_BASE_URL 是否正确
- 检查模型配额是否用尽
-
-### 错误 4: Whisper 模型未下载
-
-**症状：** `Model file not found`
-
-**解决：**
-```bash
-cd ~/whisper.cpp
-./models/download-ggml-model.sh base
-```
-
---
-
-## 状态管理（断点续传）
-
-> ⚠️ 此功能需要完成 P3 实现
-
-```bash
-# 初始化状态
-python3 kb_workflow.py --init-state "[URL]"
-
-# 查询当前状态
-python3 kb_workflow.py --get-state "[URL]"
-
-# 推进到下一步
-python3 kb_workflow.py --advance-step "[URL]" "metadata"
-
-# 记录错误
-python3 kb_workflow.py --record-error "[URL]" "403 Forbidden"
-
-# 获取恢复指令
-python3 kb_workflow.py --resume "[URL]"
-```
-
---
-
 ## ⚠️ 严格失败处理原则

-1. **深度分析不可跳过**：kb-deep-analyzer 是必须成功的步骤
-2. **失败即终止**：如果深度分析返回 `success: false`，必须立即停止
-3. **禁止降级处理**：不能使用预设模板或简单摘要"假装成功"
-4. **每次执行都要使用 AI**：必须有真实的 AI API 调用
+> **深度分析步骤必须成功，否则整个流程失败**
+
+1. **深度分析不可跳过**：AI 分析是必须成功执行的步骤
+2. **失败即终止**：如果分析返回失败，必须立即停止并汇报
+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
+
+## 辅助命令
+
+```bash
+# 查重
+python3 kb-workflow/kb_workflow.py --check --url "https://..."
+
+# 获取目录路径
+python3 kb-workflow/kb_workflow.py --base-path
+python3 kb-workflow/kb_workflow.py --metadata
+python3 kb-workflow/kb_workflow.py --content
+python3 kb-workflow/kb_workflow.py --analyzed
+
+# 归档分析报告
+python3 kb-workflow/kb_workflow.py --archive-file "/path/to/report.md"
+```
+
+## 示例
+
+```
+用户: 添加到知识库 https://www.bilibili.com/video/BV1xx
+
+Claude:
+✅ 环境变量检查通过
+
+◆ 处理中...
+  → 解析 URL: bilibili 视频 BV1xx
+  → 获取元数据...
+  → 下载字幕...
+  → 深度分析...
+
+✅ 完成！
+
+标题: 【教程】如何使用 Python 进行数据分析
+处理阶段: 全部完成
+最终文件: ~/work/zq/30.资源/AI分析/2026-03-15/bilibili_BV1xx.md
+```
+
+## 技术架构
+
+```
+kb-workflow/kb_workflow.py (CLI)
+    └── kb_pipeline/ (核心管道包)
+        ├── pipeline.py      — Pipeline 编排引擎
+        ├── processors/      — 处理器实现
+        │   ├── parse.py     — URL 解析
+        │   ├── download.py  — 内容下载
+        │   ├── asr.py       — 语音识别
+        │   └── analyze.py   — 深度分析
+        └── schemas.py       — 数据模型
+```
@@ -5,7 +5,7 @@ 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、状态管理等工具函数，
+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。
 """

@@ -18,46 +18,7 @@ import shutil
 import re
 from datetime import datetime
 from pathlib import Path
-from typing import Dict, List, Optional, Any, Tuple
-
-
-# ==================== 状态机定义 ====================
-
-# 工作流步骤顺序
-WORKFLOW_STEPS = [
-    "init",
-    "link_parse",
-    "metadata",
-    "content",
-    "whisper",      # 可选步骤
-    "analyze",
-    "archive",
-    "done"
-]
-
-# 每个步骤的必需输出字段
-STEP_OUTPUT_FIELDS = {
-    "init": [],
-    "link_parse": ["platform", "content_type", "content_id"],
-    "metadata": ["title"],
-    "content": ["filepath"],
-    "whisper": ["output_files"],
-    "analyze": ["output_file"],
-    "archive": ["archive_path"],
-    "done": []
-}
-
-# 步骤描述（用于汇报）
-STEP_DESCRIPTIONS = {
-    "init": "初始化",
-    "link_parse": "链接解析",
-    "metadata": "元数据提取",
-    "content": "内容下载",
-    "whisper": "语音识别",
-    "analyze": "深度分析",
-    "archive": "归档",
-    "done": "完成"
-}
+from typing import Dict, List, Optional, Any


 # ==================== 子技能映射 ====================
@@ -332,249 +293,6 @@ def archive_and_git(
    return result


-# ==================== 状态管理 ====================
-
-def get_state_file_path(url: str, base_path: Path = None) -> Path:
-    """获取状态文件路径"""
-    if base_path is None:
-        base_path = get_knowledge_base_path()
-    pending_dir = base_path / "pending"
-    pending_dir.mkdir(parents=True, exist_ok=True)
-    
-    # 从 URL 提取标识符
-    # 尝试提取 BV 号或其他 ID
-    bv_match = re.search(r'BV[a-zA-Z0-9]+', url)
-    if bv_match:
-        content_id = bv_match.group()
-        platform = "bilibili"
-    else:
-        # 尝试提取知乎 ID
-        zhihu_match = re.search(r'zhihu\.com/(?:question|zvideo|p)/(\d+)', url)
-        if zhihu_match:
-            content_id = zhihu_match.group(1)
-            platform = "zhihu"
-        else:
-            # 使用 URL hash 作为标识
-            import hashlib
-            content_id = hashlib.md5(url.encode()).hexdigest()[:12]
-            platform = "unknown"
-    
-    return pending_dir / f"{platform}_{content_id}.state.json"
-
-
-def init_state(url: str, base_path: Path = None) -> Dict[str, Any]:
-    """初始化工作流状态"""
-    state_file = get_state_file_path(url, base_path)
-    
-    # 如果状态文件已存在，返回现有状态
-    if state_file.exists():
-        with open(state_file, 'r', encoding='utf-8') as f:
-            return json.load(f)
-    
-    # 创建新状态
-    now = datetime.now().isoformat()
-    state = {
-        "url": url,
-        "platform": None,
-        "content_id": None,
-        "content_type": None,
-        "current_step": "init",
-        "completed_steps": [],
-        "step_results": {},
-        "errors": [],
-        "created_at": now,
-        "updated_at": now
-    }
-    
-    with open(state_file, 'w', encoding='utf-8') as f:
-        json.dump(state, f, ensure_ascii=False, indent=2)
-    
-    return state
-
-
-def get_state(url: str, base_path: Path = None) -> Dict[str, Any]:
-    """获取当前工作流状态"""
-    state_file = get_state_file_path(url, base_path)
-    
-    if not state_file.exists():
-        return {
-            "success": False,
-            "error": "状态文件不存在",
-            "url": url,
-            "state_file": str(state_file)
-        }
-    
-    with open(state_file, 'r', encoding='utf-8') as f:
-        state = json.load(f)
-    
-    return {
-        "success": True,
-        "state": state,
-        "state_file": str(state_file)
-    }
-
-
-def advance_step(
-    url: str,
-    step_name: str,
-    result: Dict[str, Any] = None,
-    base_path: Path = None
-) -> Dict[str, Any]:
-    """推进到下一步并记录结果"""
-    state_file = get_state_file_path(url, base_path)
-    
-    if not state_file.exists():
-        return {
-            "success": False,
-            "error": "状态文件不存在，请先执行 --init-state"
-        }
-    
-    with open(state_file, 'r', encoding='utf-8') as f:
-        state = json.load(f)
-    
-    # 验证步骤名称
-    if step_name not in WORKFLOW_STEPS:
-        return {
-            "success": False,
-            "error": f"无效的步骤名称: {step_name}",
-            "valid_steps": WORKFLOW_STEPS
-        }
-    
-    # 获取当前步骤索引
-    try:
-        current_idx = WORKFLOW_STEPS.index(state["current_step"])
-        new_idx = WORKFLOW_STEPS.index(step_name)
-    except ValueError:
-        return {
-            "success": False,
-            "error": f"步骤索引错误"
-        }
-    
-    # 更新状态
-    if step_name != state["current_step"]:
-        # 标记当前步骤为完成
-        if state["current_step"] not in state["completed_steps"]:
-            state["completed_steps"].append(state["current_step"])
-    
-    state["current_step"] = step_name
-    state["updated_at"] = datetime.now().isoformat()
-    
-    # 记录步骤结果
-    if result:
-        state["step_results"][step_name] = result
-        
-        # 提取平台和 ID 信息（从 link_parse 步骤）
-        if step_name == "link_parse" and result.get("success"):
-            state["platform"] = result.get("platform")
-            state["content_id"] = result.get("content_id")
-            state["content_type"] = result.get("type")
-    
-    # 保存状态
-    with open(state_file, 'w', encoding='utf-8') as f:
-        json.dump(state, f, ensure_ascii=False, indent=2)
-    
-    return {
-        "success": True,
-        "current_step": step_name,
-        "completed_steps": state["completed_steps"],
-        "state_file": str(state_file)
-    }
-
-
-def record_error(
-    url: str,
-    error_msg: str,
-    step: str = None,
-    base_path: Path = None
-) -> Dict[str, Any]:
-    """记录错误"""
-    state_file = get_state_file_path(url, base_path)
-    
-    if not state_file.exists():
-        return {
-            "success": False,
-            "error": "状态文件不存在，请先执行 --init-state"
-        }
-    
-    with open(state_file, 'r', encoding='utf-8') as f:
-        state = json.load(f)
-    
-    # 记录错误
-    error_entry = {
-        "step": step or state["current_step"],
-        "error": error_msg,
-        "timestamp": datetime.now().isoformat()
-    }
-    state["errors"].append(error_entry)
-    state["updated_at"] = datetime.now().isoformat()
-    
-    # 保存状态
-    with open(state_file, 'w', encoding='utf-8') as f:
-        json.dump(state, f, ensure_ascii=False, indent=2)
-    
-    return {
-        "success": True,
-        "error_recorded": error_entry,
-        "total_errors": len(state["errors"])
-    }
-
-
-def get_resume_info(url: str, base_path: Path = None) -> Dict[str, Any]:
-    """获取恢复执行的指令"""
-    state_file = get_state_file_path(url, base_path)
-    
-    if not state_file.exists():
-        return {
-            "success": False,
-            "error": "状态文件不存在",
-            "suggestion": "请使用 --init-state 初始化工作流"
-        }
-    
-    with open(state_file, 'r', encoding='utf-8') as f:
-        state = json.load(f)
-    
-    current_step = state["current_step"]
-    step_desc = STEP_DESCRIPTIONS.get(current_step, current_step)
-    
-    # 检查是否有错误
-    if state["errors"]:
-        last_error = state["errors"][-1]
-        return {
-            "success": True,
-            "status": "error",
-            "current_step": current_step,
-            "step_description": step_desc,
-            "last_error": last_error,
-            "completed_steps": state["completed_steps"],
-            "suggestion": f"请先解决 {last_error['step']} 步骤的错误: {last_error['error']}"
-        }
-    
-    # 检查是否已完成
-    if current_step == "done":
-        return {
-            "success": True,
-            "status": "completed",
-            "current_step": "done",
-            "step_description": "已完成",
-            "completed_steps": state["completed_steps"],
-            "step_results": state["step_results"]
-        }
-    
-    # 返回下一步指令
-    return {
-        "success": True,
-        "status": "in_progress",
-        "current_step": current_step,
-        "step_description": step_desc,
-        "completed_steps": state["completed_steps"],
-        "next_action": f"继续执行步骤: {step_desc}",
-        "platform": state.get("platform"),
-        "content_id": state.get("content_id"),
-        "content_type": state.get("content_type"),
-        "step_results": state.get("step_results", {})
-    }
-
-
 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）
@@ -607,6 +325,83 @@ def generate_summary(url: str, stages: List[Dict], final_file: str, archive_resu
    return summary


+# ==================== CLI Process Command ====================
+
+def process_command(
+    url: str,
+    output_dir: Optional[str] = None,
+    skip_completed: bool = True
+) -> Dict[str, Any]:
+    """Execute full pipeline for a URL
+
+    Args:
+        url: URL to process
+        output_dir: Optional output directory for content
+        skip_completed: Skip already completed steps (default True)
+
+    Returns:
+        Dictionary with success status, content_dir, and results
+    """
+    import asyncio
+
+    # Add project root to path for kb_pipeline imports
+    project_root = Path(__file__).parent.parent
+    if str(project_root) not in sys.path:
+        sys.path.insert(0, str(project_root))
+
+    try:
+        from kb_pipeline.pipeline import Pipeline
+        from kb_pipeline.processors import ParseProcessor, DownloadProcessor, ASRProcessor, AnalyzeProcessor
+        from kb_pipeline.schemas import PipelineConfig
+
+        async def run_pipeline():
+            # Determine output directory
+            out_dir = Path(output_dir) if output_dir else Path.cwd()
+
+            # Create processors
+            processors = [
+                ParseProcessor(),
+                DownloadProcessor(),
+                ASRProcessor(),
+                AnalyzeProcessor(),
+            ]
+
+            # Create pipeline config
+            config = PipelineConfig(
+                steps=["parse", "download", "asr", "analyze"],
+                output_dir=out_dir,
+                skip_completed=skip_completed,
+            )
+
+            # Create and run pipeline
+            pipeline = Pipeline(
+                processors=processors,
+                config=config,
+                output_dir=out_dir,
+            )
+
+            result = await pipeline.run(url)
+            return result
+
+        # Run async pipeline
+        result = asyncio.run(run_pipeline())
+
+        return {
+            "success": result.success,
+            "error": result.error_message,
+            "content_dir": str(result.content_dir) if result.content_dir else None,
+            "results": result.results,
+        }
+
+    except Exception as e:
+        return {
+            "success": False,
+            "error": str(e),
+            "content_dir": None,
+            "results": {},
+        }
+
+
 # ==================== CLI ====================

 def main():
@@ -620,115 +415,94 @@ def main():
  python kb_workflow.py --check --url "https://www.bilibili.com/video/BVxxx"

  # 归档
-  python kb_workflow.py --archive-file "/path/to/analyzed.md"
+  python kb_workflow.py --archive "/path/to/analyzed.md"

-  # 状态管理
-  python kb_workflow.py --init-state --url "https://..."
-  python kb_workflow.py --get-state --url "https://..."
-  python kb_workflow.py --advance-step --url "https://..." --step "metadata" --result '{"success":true}'
-  python kb_workflow.py --record-error --url "https://..." --error-msg "403 Forbidden"
-  python kb_workflow.py --resume --url "https://..."
+  # 处理URL (通过完整管道)
+  python kb_workflow.py process "https://www.bilibili.com/video/BVxxx"
        """
    )

-    # 基础参数
-    parser.add_argument('--url', type=str, help='URL 地址')
-    parser.add_argument('--base-path', type=str, help='知识库根目录')
+    # Create subparsers for commands
+    subparsers = parser.add_subparsers(dest='command', help='Available commands')

-    # 查重和归档
+    # Process subcommand
+    process_parser = subparsers.add_parser('process', help='Process URL through full pipeline')
+    process_parser.add_argument('url', type=str, help='URL to process')
+    process_parser.add_argument('--output-dir', '-o', type=str, help='Output directory for content')
+    process_parser.add_argument('--skip-completed', action='store_true', default=True,
+                                 help='Skip completed steps (default: True)')
+    process_parser.add_argument('--no-skip', action='store_false', dest='skip_completed',
+                                 help='Re-run all steps')
+
+    # Legacy arguments (for backward compatibility)
    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')

-    # 目录路径查询
-    parser.add_argument('--pending', action='store_true', help='获取 URL 解析目录')
+    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='获取内容下载目录')
+    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='从文件名提取平台名称')
-    
-    # 状态管理
-    parser.add_argument('--init-state', action='store_true', help='初始化工作流状态')
-    parser.add_argument('--get-state', action='store_true', help='获取当前状态')
-    parser.add_argument('--advance-step', action='store_true', help='推进到下一步')
-    parser.add_argument('--step', type=str, help='步骤名称')
-    parser.add_argument('--result', type=str, help='步骤结果（JSON 字符串）')
-    parser.add_argument('--record-error', action='store_true', help='记录错误')
-    parser.add_argument('--error-msg', type=str, help='错误信息')
-    parser.add_argument('--resume', action='store_true', help='获取恢复执行信息')
-    
+    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)

-    # 查重
+    # Handle process subcommand
+    if args.command == 'process':
+        result = process_command(
+            url=args.url,
+            output_dir=args.output_dir,
+            skip_completed=args.skip_completed
+        )
+        print(json.dumps(result, ensure_ascii=False, indent=2))
+        return
+
+    # Legacy argument handling
    if args.check and args.url:
        result = check_duplicate(base_path, args.url)
        print(json.dumps(result, ensure_ascii=False, indent=2))

-    # 归档
    elif args.archive_file:
        result = archive_and_git(args.archive_file, base_path)
        print(json.dumps(result, ensure_ascii=False, indent=2))

-    # 目录路径查询
    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(get_archive_path())}, 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))

-    # 状态管理
-    elif args.init_state and args.url:
-        result = init_state(args.url, base_path)
-        print(json.dumps({"success": True, "state": result}, ensure_ascii=False, indent=2))
-    
-    elif args.get_state and args.url:
-        result = get_state(args.url, base_path)
-        print(json.dumps(result, ensure_ascii=False, indent=2))
-    
-    elif args.advance_step and args.url and args.step:
-        result_data = None
-        if args.result:
-            try:
-                result_data = json.loads(args.result)
-            except json.JSONDecodeError:
-                result_data = {"raw": args.result}
-        result = advance_step(args.url, args.step, result_data, base_path)
-        print(json.dumps(result, ensure_ascii=False, indent=2))
-    
-    elif args.record_error and args.url and args.error_msg:
-        result = record_error(args.url, args.error_msg, args.step, base_path)
-        print(json.dumps(result, ensure_ascii=False, indent=2))
-    
-    elif args.resume and args.url:
-        result = get_resume_info(args.url, base_path)
-        print(json.dumps(result, ensure_ascii=False, indent=2))
-    
    else:
        parser.print_help()


 if __name__ == '__main__':
    main()
-
-
-if __name__ == '__main__':
-    main()
@@ -1,154 +1,86 @@
---
-name: kb-zhihu-content
-description: 知乎内容下载 - 下载视频、提取文章内容
---
-
 # KB Zhihu Content Skill

-**kb-zhihu-content** - 下载知乎视频、提取文章内容。
+**kb-zhihu-content** - Download Zhihu videos and extract content.

-## 什么时候使用
+## Features

- kb-workflow 步骤 3a：下载知乎视频
- kb-workflow 步骤 3b：提取知乎专栏文章内容
+- Video download using **yt-dlp**
+- Subtitle extraction (returns no subtitle, triggers Whisper)
+- **Read article content from MediaCrawler data** (NEW!)
+- Supports `ZHIHU_COOKIE` environment variable

-## 功能
+## Environment Variables

- 视频下载（使用 yt-dlp）
- 从 MediaCrawler 数据读取文章内容
- 支持 `ZHIHU_COOKIE` 环境变量
+| Variable | Required | Description |
+|----------|----------|-------------|
+| `ZHIHU_COOKIE` | No | 知乎登录 Cookie (for member-only videos) |

-## 环境变量
-
-| 变量名 | 必需 | 说明 |
-|--------|------|------|
-| `ZHIHU_COOKIE` | 否 | 知乎登录 Cookie |
-
-## 命令
+## Commands

 ```bash
-# 下载视频
-python3 zhihu_content.py --id "12345678" --type video --output "/path/to/downloads/"
+# Download video
+python zhihu_content.py --id "12345678" --type video --output "/path/to/downloads/"

-# 从 MediaCrawler 读取文章内容
-python3 zhihu_content.py --type article-from-media \
+# Extract subtitle (returns no subtitle, triggers Whisper)
+python zhihu_content.py --id "12345678" --type subtitle --output "/path/to/subtitles/"
+
+# 从 MediaCrawler 读取文章内容 (⚡ NEW!)
+python zhihu_content.py --type article-from-media --input "~/work/MediaCrawler/data/zhihu/json/detail_contents_2026-02-27.json" --output "~/work/knowledge-base/data/zhihu/"
+```
+
+## MediaCrawler 数据读取
+
+**新功能**：从 MediaCrawler 爬取的数据中提取知乎文章内容
+
+### 使用方法
+
+```bash
+# 从 MediaCrawler JSON 文件读取文章内容
+python zhihu_content.py --type article-from-media \
    --input "~/work/MediaCrawler/data/zhihu/json/detail_contents_2026-02-27.json" \
    --output "~/work/knowledge-base/data/zhihu/"

-# 自动查找最新 MediaCrawler 数据
-python3 zhihu_content.py --type article-from-media \
+# 查找最新的爬取数据
+python zhihu_content.py --type article-from-media \
    --output "~/work/knowledge-base/data/zhihu/"
 ```

-## 输出格式 Schema
+### 参数说明

-### 必需字段
+| 参数 | 说明 |
+|------|------|
+| `--type article-from-media` | 从 MediaCrawler 数据读取 |
+| `--input` | MediaCrawler 输出文件路径（可选，不指定则自动查找最新） |
+| `--output` | 输出目录 |

-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `success` | boolean | 是否成功 |
-| `type` | string | 操作类型（video/article） |
-
-### 视频下载特有字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `filepath` | string | 视频文件路径 |
-| `filename` | string | 视频文件名 |
-
-### 文章提取特有字段
-
-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `filepath` | string | 文章文件路径 |
-| `filename` | string | 文章文件名 |
-| `content_length` | number | 内容长度 |
-
-## 成功示例
-
-### 视频下载
-
-```json
-{
-  "success": true,
-  "type": "video",
-  "filename": "zhihu_12345678.mp4",
-  "filepath": "content/downloads/zhihu_12345678.mp4",
-  "message": "下载完成"
-}
-```
-
-### 文章内容提取
+### 输出格式

 ```json
 {
  "success": true,
  "type": "article",
  "filename": "zhihu_article_如何高效的爬取微信公众号.md",
-  "filepath": "~/work/knowledge-base/data/zhihu/zhihu_article_如何高效的爬取微信公众号.md",
-  "content_length": 4563,
+  "filepath": "/home/xxx/work/knowledge-base/data/zhihu/zhihu_article_如何高效的爬取微信公众号.md",
  "message": "文章内容已导出"
 }
 ```

-## 错误示例
+## 原有功能

-### 视频下载失败
+### Video Download

-```json
-{
-  "success": false,
-  "type": "video",
-  "error": "视频不存在或无法访问",
-  "suggestion": "请检查视频 ID 是否正确"
-}
-```
+Video download uses **yt-dlp**:
+1. Parses `ZHIHU_COOKIE` environment variable (optional)
+2. Creates temporary Netscape-format cookie file
+3. Runs `yt-dlp -f best -o <output> <url>`
+4. Cleans up temporary file

-### MediaCrawler 数据未找到
+**Note**: 知乎视频通常没有官方字幕，将自动触发 Whisper 语音识别

-```json
-{
-  "success": false,
-  "type": "article-from-media",
-  "error": "未找到 MediaCrawler 数据文件",
-  "suggestion": "请先运行 MediaCrawler 爬取文章"
-}
-```
-
-### 网络错误
-
-```json
-{
-  "success": false,
-  "error": "网络请求失败: Connection timeout"
-}
-```
-
-## 安装
+## Installation

 ```bash
-cp -r kb-zhihu-content/ ~/.openclaw/skills/
+# Already included in kb-skills repo
+# Ensure yt-dlp is installed
 pip install yt-dlp aiohttp
 ```
-
-## 在 kb-workflow 中的使用
-
-```bash
-# 步骤 3a：下载知乎视频
-python3 ~/.openclaw/skills/kb-zhihu-content/zhihu_content.py \
-    --id "12345678" --type video \
-    --output "content/downloads/"
-
-# 注意：知乎视频没有官方字幕，下载后需要进入 Whisper 步骤
-
-# 步骤 3b：提取专栏文章（如果有 MediaCrawler 数据）
-python3 ~/.openclaw/skills/kb-zhihu-content/zhihu_content.py \
-    --type article-from-media \
-    --output "content/"
-```
-
-## 注意事项
-
- **知乎视频没有官方字幕**：下载后必须进入 Whisper 步骤
- MediaCrawler 需要单独安装和运行
- 部分视频可能需要登录才能下载
@@ -1,207 +1,101 @@
---
-name: kb-zhihu-metadata
-description: 知乎元数据提取 - 获取问题/回答/专栏/视频的详细信息
---
-
 # KB Zhihu Metadata Skill

-**kb-zhihu-metadata** - 提取知乎问题、回答、用户、专栏的元数据。
+**kb-zhihu-metadata** - Extract Zhihu question/answer/user metadata and crawl articles.

-## 什么时候使用
+## Features

- kb-workflow 步骤 2：获取知乎内容的元数据
- 需要问题标题、回答者、文章标题等信息
-
-## 功能
-
- 问题元数据提取
- 回答元数据提取
- 用户信息提取
- 专栏文章爬取（使用 MediaCrawler）
- 支持 `ZHIHU_COOKIE` 环境变量
+- Question metadata extraction
+- Answer metadata extraction
+- User information extraction
+- **Article crawling using MediaCrawler** (NEW!)
+- JSON output format
+- Markdown conversion support
+- **Cookie 支持**：可配置 ZHIHU_COOKIE 访问需要登录的内容

 ## 环境变量

-| 变量名 | 必需 | 说明 |
+| 变量名 | 用途 | 必需 |
 |--------|------|------|
-| `ZHIHU_COOKIE` | 推荐 | 知乎登录 Cookie（访问专栏等需要） |
+| `ZHIHU_COOKIE` | 知乎登录 Cookie | 推荐（访问专栏等需要） |

-**获取 Cookie：**
+**获取 ZHIHU_COOKIE**：
 1. 登录知乎网页版
-2. F12 → Application → Cookies → zhihu.com
+2. 打开开发者工具 (F12) → Application → Cookies → zhihu.com
 3. 复制 Cookie 字符串

 ```bash
-export ZHIHU_COOKIE="你的知乎Cookie"
+# 添加到 ~/.kb_env
+echo 'export ZHIHU_COOKIE="你的知乎Cookie"' >> ~/.kb_env
+source ~/.kb_env
 ```

-## 命令
+## Commands

 ```bash
-# 问题元数据
-python3 zhihu_metadata.py --id "266309957" --type question
+# Question metadata
+python zhihu_metadata.py --id "266309957" --type question

-# 回答元数据
-python3 zhihu_metadata.py --id "2003427891925509507" --type answer
+# Answer metadata
+python zhihu_metadata.py --id "2003427891925509507" --type answer

-# 知乎视频
-python3 zhihu_metadata.py --id "123456789" --type zvideo
-
-# 专栏文章爬取（使用 MediaCrawler）
-python3 zhihu_article_crawler.py --url "https://zhuanlan.zhihu.com/p/1894054073625461813" --output "~/work/knowledge-base/data/zhihu/"
+# 专栏文章爬取 (使用 MediaCrawler) ⚡ NEW!
+python zhihu_article_crawler.py --url "https://zhuanlan.zhihu.com/p/1894054073625461813" --output "~/work/knowledge-base/data/zhihu/"
 ```

-## 输出格式 Schema
+## MediaCrawler 爬取文章

-### 必需字段
+**新功能**：使用 MediaCrawler 爬取知乎专栏文章

-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `success` | boolean | 是否提取成功 |
-| `platform` | string | 固定为 `zhihu` |
-| `content_id` | string | 内容 ID |
-| `content_type` | string | 内容类型 |
-| `title` | string | 标题 |
+### 环境要求

-### 可选字段
+MediaCrawler 已安装在 `~/work/MediaCrawler`

-| 字段 | 类型 | 说明 |
-|------|------|------|
-| `author` | string | 作者名称 |
-| `excerpt` | string | 摘要 |
-| `stats` | object | 统计数据 |
-| `output_file` | string | 输出文件路径 |
-| `error` | string | 错误信息 |
+### 使用方法

-## 成功示例
+```bash
+# 爬取单篇文章
+python zhihu_article_crawler.py --url "https://zhuanlan.zhihu.com/p/XXXXX"

-### 问题元数据
+# 指定输出目录
+python zhihu_article_crawler.py --url "https://zhuanlan.zhihu.com/p/XXXXX" --output "/path/to/output/"
+
+# 输出示例
+{
+  "success": true,
+  "type": "article",
+  "title": "如何高效的爬取微信公众号",
+  "filepath": "/home/xxx/work/knowledge-base/data/zhihu/zhihu_article_如何高效的爬取微信公众号.md",
+  "content_length": 4563,
+  "message": "爬取成功: 如何高效的爬取微信公众号"
+}
+```
+
+### 工作原理
+
+1. 调用 MediaCrawler 爬取文章
+2. 从 JSON 输出中提取内容
+3. 转换为 Markdown 格式（带 frontmatter）
+4. 保存到指定目录
+
+## Output

 ```json
 {
  "success": true,
-  "platform": "zhihu",
-  "content_type": "question",
-  "content_id": "266309957",
-  "title": "问题标题",
-  "excerpt": "问题描述...",
-  "stats": {
+  "data": {
+    "platform": "zhihu",
+    "type": "question",
+    "id": "266309957",
+    "title": "问题标题",
+    "author": "作者名字",
    "answer_count": 50,
-    "follower_count": 1000,
-    "view_count": 50000
-  },
-  "output_file": "metadata/zhihu_266309957.meta.json"
+    "followers": 1000
+  }
 }
 ```

-### 回答元数据
-
-```json
-{
-  "success": true,
-  "platform": "zhihu",
-  "content_type": "answer",
-  "content_id": "2003427891925509507",
-  "title": "问题标题（回答所属问题）",
-  "author": "回答者名字",
-  "excerpt": "回答摘要...",
-  "stats": {
-    "voteup_count": 1000,
-    "comment_count": 50
-  },
-  "output_file": "metadata/zhihu_2003427891925509507.meta.json"
-}
-```
-
-### 专栏文章
-
-```json
-{
-  "success": true,
-  "platform": "zhihu",
-  "content_type": "zhuanlan",
-  "content_id": "1894054073625461813",
-  "title": "文章标题",
-  "author": "作者名字",
-  "excerpt": "文章摘要...",
-  "output_file": "metadata/zhihu_1894054073625461813.meta.json"
-}
-```
-
-### 知乎视频
-
-```json
-{
-  "success": true,
-  "platform": "zhihu",
-  "content_type": "zvideo",
-  "content_id": "123456789",
-  "title": "视频标题",
-  "author": "作者名字",
-  "duration": "10:30",
-  "output_file": "metadata/zhihu_123456789.meta.json"
-}
-```
-
-## 错误示例
-
-### 内容不存在
-
-```json
-{
-  "success": false,
-  "platform": "zhihu",
-  "content_id": "invalid_id",
-  "content_type": "question",
-  "error": "问题不存在或已被删除"
-}
-```
-
-### 需要登录
-
-```json
-{
-  "success": false,
-  "platform": "zhihu",
-  "content_id": "1894054073625461813",
-  "content_type": "zhuanlan",
-  "error": "需要登录才能访问",
-  "suggestion": "请配置 ZHIHU_COOKIE 环境变量"
-}
-```
-
-### 网络错误
-
-```json
-{
-  "success": false,
-  "error": "网络请求失败: Connection timeout"
-}
-```
-
-## 安装
+## Installation

 ```bash
-cp -r kb-zhihu-metadata/ ~/.openclaw/skills/
-pip install httpx
+cp -r kb-zhihu-metadata/ ~/skills/
 ```
-
-## 在 kb-workflow 中的使用
-
-```bash
-# 步骤 2：元数据提取（知乎问题）
-python3 ~/.openclaw/skills/kb-zhihu-metadata/zhihu_metadata.py \
-    --id "266309957" --type question \
-    --output "metadata/zhihu_266309957.meta.json"
-
-# 步骤 2：元数据提取（知乎视频）
-python3 ~/.openclaw/skills/kb-zhihu-metadata/zhihu_metadata.py \
-    --id "123456789" --type zvideo \
-    --output "metadata/zhihu_123456789.meta.json"
-```
-
-## 注意事项
-
- 专栏文章可能需要登录才能访问
- MediaCrawler 需要单独安装：`~/work/MediaCrawler`
- 部分内容可能有访问限制