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# Architecture Research
**Domain:** Skill-based Workflow Orchestration System
**Researched:** 2026-03-13
**Confidence:** HIGH
## Standard Architecture
### System Overview
技能型工作流编排系统采用分层架构，将处理逻辑、状态管理和执行协调分离。基于当前研究和行业最佳实践，推荐以下四层架构：
```
+---------------------------------------------------------------+
| Orchestration Layer |
| [Workflow Coordinator] [State Manager] [Error Handler] |
+---------------------------------------------------------------+
| Skill Definition Layer |
| [SKILL.md specs] [Schema Definitions] [Trigger Rules] |
+---------------------------------------------------------------+
| Execution Layer |
| [Platform Skills] [Processing Skills] [Analysis Skills] |
+---------------------------------------------------------------+
| Infrastructure Layer |
| [File System] [External APIs] [AI Services] [State Store] |
+---------------------------------------------------------------+
```
### Component Responsibilities
| Component | Responsibility | Typical Implementation |
|-----------|----------------|------------------------|
| Workflow Coordinator | 协调多个技能的执行顺序、依赖管理 | kb-workflow 主控模块 |
| State Manager | 步骤级状态持久化、断点续传支持 | workflow_state.json + 原子写入 |
| Error Handler | 错误分类、降级策略、重试逻辑 | 统一错误处理模块 |
| Skill Registry | 技能发现、能力声明、Schema 注册 | SKILL.md + JSON Schema |
| Platform Skills | 平台特定内容获取（B站/知乎） | kb-bilibili-*, kb-zhihu-* |
| Processing Skills | 内容转换（语音识别/OCR） | kb-whisper, kb-funasr, kb-ocr |
| Analysis Skills | AI 深度分析、报告生成 | kb-deep-analyzer |
## Recommended Project Structure
```
kb-skills/
+-- skills/ # 技能模块目录
| +-- core/ # 核心基础设施
| | +-- state_manager.py # 状态管理
| | +-- schema_registry.py # Schema 注册
| | +-- pipeline.py # 管道执行引擎
| | +-- error_handler.py # 错误处理
| |
| +-- platforms/ # 平台技能
| | +-- bilibili/ # B站技能组
| | | +-- SKILL.md
| | | +-- metadata.py
| | | +-- content.py
| | +-- zhihu/ # 知乎技能组
| | +-- SKILL.md
| | +-- metadata.py
| | +-- content.py
| |
| +-- processing/ # 处理技能
| | +-- whisper/ # 语音识别
| | +-- funasr/ # 中文语音识别
| | +-- ocr/ # OCR 处理
| |
| +-- analysis/ # 分析技能
| +-- deep_analyzer/ # 深度分析
|
+-- schemas/ # JSON Schema 定义
| +-- workflow_state.schema.json
| +-- skill_output.schema.json
| +-- platform_metadata.schema.json
|
+-- workflows/ # 工作流定义
| +-- video_pipeline.yaml # 视频处理流程
| +-- article_pipeline.yaml # 文章处理流程
|
+-- cli.py # 统一 CLI 入口
+-- config.py # 配置管理
```
### Structure Rationale
- **skills/core/:** 核心基础设施，所有技能共享的状态管理、Schema 验证和管道执行
- **skills/platforms/:** 按平台组织，每个平台有自己的元数据提取和内容下载技能
- **skills/processing/:** 内容转换技能，独立于平台
- **skills/analysis/:** AI 分析技能，作为管道的最后阶段
- **schemas/:** 统一的 JSON Schema 定义，确保接口一致性
- **workflows/:** 声明式工作流定义，指定技能组合和执行顺序
## Architectural Patterns
### Pattern 1: Pipeline Orchestration (管道编排)
**What:** 将处理流程建模为一系列串联的步骤，每个步骤接收输入、执行处理、产生输出。
**When to use:** 可预测的、重复性的任务，如内容处理管道。
**Trade-offs:**
- 优点：确定性执行、易于调试、支持断点续传
- 缺点：灵活性有限，不适合需要动态决策的场景
**Example:**
```python
# Pipeline definition
class ContentPipeline:
steps = [
("parse", LinkParserSkill),
("metadata", MetadataSkill), # depends on parse
("download", ContentSkill), # depends on metadata
("transcribe", TranscribeSkill), # conditional: only for video
("analyze", AnalyzeSkill), # depends on download + transcribe
]
def run(self, url: str, state: WorkflowState):
for step_name, skill in self.steps:
if state.is_completed(step_name):
continue # Skip completed steps
result = skill.execute(state.get_step_input(step_name))
state.mark_completed(step_name, result)
if not result.success:
return self.handle_error(result.error)
```
### Pattern 2: Step-Level State Management (步骤级状态管理)
**What:** 每个主要步骤完成后立即持久化状态，支持从任意断点恢复执行。
**When to use:** 长时间运行的批处理任务，可能因网络、API 限制等原因中断。
**Trade-offs:**
- 优点：断点续传、执行追踪、幂等性保证
- 缺点：状态管理开销、需要设计状态 Schema
**Example:**
```python
# workflow_state.json
{
"url": "https://www.bilibili.com/video/BV1xx411c7mD",
"platform": "bilibili",
"content_type": "video",
"created_at": "2026-03-13T10:00:00Z",
"updated_at": "2026-03-13T10:15:00Z",
"steps": {
"parse": {
"status": "completed",
"output": {"video_id": "BV1xx411c7mD"},
"completed_at": "2026-03-13T10:00:05Z"
},
"metadata": {
"status": "completed",
"output": {"title": "...", "duration": 1800},
"completed_at": "2026-03-13T10:00:30Z"
},
"download": {
"status": "in_progress",
"output": null,
"started_at": "2026-03-13T10:01:00Z"
}
}
}
```
### Pattern 3: Schema-First Interface Design (Schema 优先接口设计)
**What:** 先定义输入输出的 JSON Schema，再实现技能逻辑。所有技能遵守统一的接口规范。
**When to use:** 多人协作、需要接口稳定性和可测试性的项目。
**Trade-offs:**
- 优点：接口清晰、易于测试、支持自动验证
- 缺点：前期设计成本、Schema 维护负担
**Example:**
```json
// schemas/skill_output.schema.json
{
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"required": ["success", "data"],
"properties": {
"success": {"type": "boolean"},
"data": {"type": "object"},
"error": {
"type": "object",
"properties": {
"code": {"type": "string"},
"message": {"type": "string"},
"detail": {"type": "string"}
}
},
"metadata": {
"type": "object",
"properties": {
"duration_ms": {"type": "number"},
"tokens_used": {"type": "number"}
}
}
}
}
```
### Pattern 4: Idempotent Execution (幂等执行)
**What:** 相同输入重复执行产生相同结果，不会产生副作用（如重复文件）。
**When to use:** 任何可能需要重试的处理步骤。
**Trade-offs:**
- 优点：安全重试、简化错误恢复
- 缺点：需要设计去重逻辑
**Example:**
```python
class ContentSkill:
def execute(self, input: dict) -> SkillOutput:
# Generate deterministic filename from input
content_hash = hashlib.sha256(input['url'].encode()).hexdigest()[:12]
output_path = self.get_output_dir() / f"{content_hash}.mp4"
# Check if already exists
if output_path.exists():
return SkillOutput(success=True, data={"path": str(output_path), "cached": True})
# Download content
self.download_content(input['url'], output_path)
return SkillOutput(success=True, data={"path": str(output_path), "cached": False})
```
## Data Flow
### Content Processing Pipeline
```
[URL Input]
|
v
+-------------+ +----------------+
| Link Parser | --> | Platform/Type |
+-------------+ | Detection |
+----------------+
|
+---------------+---------------+
| |
v v
+-------------+ +-------------+
| Bilibili | | Zhihu |
| Pipeline | | Pipeline |
+-------------+ +-------------+
| |
+------+------+ +-------+-------+
| | | |
v v v v
[Metadata] [Download] [Metadata] [Download]
| | | |
+------+------+ +-------+-------+
| |
v v
+-------------+ +-------------+
| Video? | | Article |
| - Whisper | | - Direct |
| - FunASR | | Extract |
| - OCR | +-------------+
+-------------+ |
| |
+---------------+--------------+
|
v
+-----------------+
| Deep Analyzer |
| (AI Analysis) |
+-----------------+
|
v
+-----------------+
| Archive to |
| Knowledge Base |
+-----------------+
```
### State Management Flow
```
[Workflow Start]
|
v
+------------------+
| Load/Init State | <-- workflow_state.json
+------------------+
|
v
+------------------+ +------------------+
| For Each Step | --> | Check Status |
+------------------+ | - completed? skip|
| | - failed? retry |
v | - pending? run |
+------------------+ +------------------+
| Execute Step | |
+------------------+ v
| +------------------+
v | Mark Completed |
+------------------+ | Save State |
| Validate Output | -->| (atomic write) |
+------------------+ +------------------+
|
v
[Next Step / Complete]
```
### Key Data Flows
1. **URL to Metadata:** URL -> Link Parser -> (platform, type, id) -> Platform Metadata Skill -> Metadata JSON
2. **Metadata to Content:** Metadata JSON -> Content Skill -> (video file / article text)
3. **Content to Transcript:** Video File -> Transcribe Skill (Whisper/FunASR) -> Transcript JSON
4. **Content + Transcript to Analysis:** Content + Transcript -> Deep Analyzer -> Analysis Report
## Scaling Considerations
| Scale | Architecture Adjustments |
|-------|--------------------------|
| 1-100 items/day | 当前架构足够，单进程执行 |
| 100-1000 items/day | 添加任务队列，并行处理独立任务 |
| 1000+ items/day | 分布式任务调度，API 请求限流，缓存层 |
### Scaling Priorities
1. **First bottleneck:** 外部 API 限流（B站/知乎）。解决方案：请求队列 + 指数退避重试
2. **Second bottleneck:** AI 分析耗时。解决方案：批处理 + 并行分析
## Anti-Patterns
### Anti-Pattern 1: Global State Mutation
**What people do:** 在多个技能间共享可变全局状态
**Why it's wrong:** 难以追踪状态变化、难以测试、难以恢复
**Do this instead:** 每个步骤从 state 读取输入，将输出写回 state，状态变更通过 state manager 集中管理
### Anti-Pattern 2: Implicit Dependencies
**What people do:** 技能之间通过隐式约定传递数据（如固定文件路径）
**Why it's wrong:** 脆弱的耦合，修改一个技能可能破坏其他技能
**Do this instead:** 在 workflow_state.json 中显式声明依赖关系，通过 state manager 传递数据
### Anti-Pattern 3: Silent Failure
**What people do:** 捕获异常后只打印日志，继续执行
**Why it's wrong:** 下游步骤收到无效输入，产生难以调试的错误
**Do this instead:** 明确的失败处理策略 - 要么终止并记录，要么降级并提供默认值
### Anti-Pattern 4: Non-Idempotent Operations
**What people do:** 每次执行都创建新文件（如 `output_1.mp4`, `output_2.mp4`）
**Why it's wrong:** 重试产生垃圾文件，磁盘占用增长
**Do this instead:** 基于输入生成确定性文件名，已存在则跳过
## Integration Points
### External Services
| Service | Integration Pattern | Notes |
|---------|---------------------|-------|
| Bilibili API | HTTP + Cookie Auth | 需要 BILIBILI_COOKIE 环境变量 |
| Zhihu API | HTTP + Cookie Auth | 需要 ZHIHU_COOKIE 环境变量 |
| AI Provider | OpenAI-compatible API | 需要 AI_API_KEY 环境变量 |
| HuggingFace | API + Local Cache | 需要 HF_TOKEN 环境变量 |
| Git | Command Line | 需要 KB_GIT_USER/EMAIL 配置 |
### Internal Boundaries
| Boundary | Communication | Notes |
|----------|---------------|-------|
| Orchestrator <-> Skills | JSON via stdin/stdout | 标准化输入输出格式 |
| Skills <-> State Manager | File I/O | workflow_state.json |
| Skills <-> External APIs | HTTP | 统一错误处理和重试 |
## Build Order Implications
基于组件依赖关系，建议的构建顺序：
### Phase 1: Foundation (基础设施)
1. **State Manager** - 所有后续组件的基础
2. **Schema Registry** - 定义统一的输入输出格式
3. **Error Handler** - 统一错误处理模式
### Phase 2: Core Skills (核心技能)
4. **Link Parser** - 入口点，无依赖
5. **Platform Metadata Skills** - 依赖 Link Parser 输出
6. **Platform Content Skills** - 依赖 Metadata 输出
### Phase 3: Processing Skills (处理技能)
7. **Whisper/FunASR** - 依赖视频文件
8. **OCR Processor** - 依赖视频截图
### Phase 4: Analysis Skills (分析技能)
9. **Deep Analyzer** - 依赖内容和转录文本
### Phase 5: Orchestration (编排层)
10. **Pipeline Engine** - 组合所有技能
11. **Workflow Definitions** - 声明式流程定义
## Sources
- [AI Architecture Patterns 101: Workflows, Agents, MCPs, and A2A Systems](https://aipmguru.substack.com/p/ai-architecture-patterns-101-workflows) - HIGH confidence, comprehensive pattern overview
- [Data Pipeline Architecture: 5 Design Patterns with Examples - Dagster](https://dagster.io/guides/data-pipeline-architecture-5-design-patterns-with-examples) - HIGH confidence, pipeline patterns
- [Agentic Workflows in 2026: The ultimate guide - Vellum](https://www.vellum.ai/blog/agentic-workflows-emerging-architectures-and-design-patterns) - HIGH confidence, agent architecture patterns
- [State of Open Source Workflow Orchestration Systems 2025](https://www.pracdata.io/p/state-of-workflow-orchestration-ecosystem-2025) - MEDIUM confidence, ecosystem overview
- [ML Pipeline Architecture Design Patterns - Neptune.ai](https://neptune.ai/blog/ml-pipeline-architecture-design-patterns) - MEDIUM confidence, ML-specific patterns
---
*Architecture research for: Skill-based Workflow Orchestration System*
*Researched: 2026-03-13*