docs: make workflow examples beginner-friendly
- Replace raw YAML/JSON with plain language explanations - Add "What happens" and "You get" for each phase - More intuitive for newcomers 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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@@ -37,50 +37,31 @@ cp agents/web-search-agent.md ~/.claude/agents/
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## Workflow & Example
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## Workflow & Example
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> Example: Researching "AI Agent Demo 2025"
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> **Example**: Researching "AI Agent Demo 2025"
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### Phase 1: Generate Outline
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### Phase 1: Generate Outline
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```
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```
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run /research AI Agent Demo 2025
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run /research AI Agent Demo 2025
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```
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```
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**Output:** `ai-agent-demo/outline.yaml`
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💡 **What happens**: Tell it your topic → It creates a research list for you
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```yaml
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topic: "AI Agent Demo & Review (2025.9-2025.12)"
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**You get**: A list of 17 AI Agents to research (ChatGPT Agent, Claude Computer Use, Cursor, etc.) + what info to collect for each
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items:
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- name: "ChatGPT Agent"
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category: "Browser Agent"
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brief: "OpenAI unified Agent, released July 2025"
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- name: "Claude Computer Use"
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category: "Desktop Agent"
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brief: "Anthropic desktop control Agent"
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# ... 15 more items
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```
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### Phase 2: Deep Research
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### Phase 2: Deep Research
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```
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```
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run /research/deep
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run /research/deep
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```
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```
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**Output:** `ai-agent-demo/results/ChatGPT_Agent.json`
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💡 **What happens**: AI automatically searches the web for each item, one by one
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```json
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{
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**You get**: Detailed info for each Agent (company, release date, pricing, tech specs, reviews...)
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"basic_info": {
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"name": "ChatGPT Agent",
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"company": "OpenAI",
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"release_date": "2025-07-17",
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"pricing": "Pro $200/mo, Plus $20/mo"
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},
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"tech_specs": {
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"underlying_model": "GPT-5 series",
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"agent_type": "Unified autonomous Agent"
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}
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}
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```
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### Phase 3: Generate Report
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### Phase 3: Generate Report
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```
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```
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run /research/report
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run /research/report
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```
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```
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**Output:** `ai-agent-demo/report.md` - Markdown report with TOC and all items
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💡 **What happens**: All data → One organized report
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**You get**: `report.md` - A complete markdown report with table of contents, ready to read or share
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## References
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## References
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+10
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@@ -37,50 +37,31 @@ cp agents/web-search-agent.md ~/.claude/agents/
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## 工作流 & 示例
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## 工作流 & 示例
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> 示例:调研 "AI Agent Demo 2025"
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> **示例**:调研 "AI Agent Demo 2025"
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### 阶段1:生成Outline
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### 阶段1:生成Outline
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```
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```
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run /research AI Agent Demo 2025
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run /research AI Agent Demo 2025
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```
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```
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**输出:** `ai-agent-demo/outline.yaml`
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💡 **发生了什么**:告诉它你要研究什么 → 它帮你列出调研清单
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```yaml
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topic: "AI Agent Demo & 测评 (2025.9-2025.12)"
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**你会得到**:17个待调研的AI Agent清单(ChatGPT Agent、Claude Computer Use、Cursor等)+ 每个要收集哪些信息
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items:
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- name: "ChatGPT Agent"
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category: "浏览器Agent"
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brief: "OpenAI统一Agent,2025年7月发布"
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- name: "Claude Computer Use"
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category: "桌面Agent"
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brief: "Anthropic桌面操控Agent"
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# ... 另外15个items
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```
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### 阶段2:深度调研
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### 阶段2:深度调研
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```
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```
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run /research/deep
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run /research/deep
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```
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```
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**输出:** `ai-agent-demo/results/ChatGPT_Agent.json`
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💡 **发生了什么**:AI自动上网搜索每个item的详细信息,逐个完成
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```json
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{
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**你会得到**:每个Agent的详细资料(公司、发布日期、定价、技术规格、用户评价...)
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"basic_info": {
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"name": "ChatGPT Agent",
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"company": "OpenAI",
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"release_date": "2025-07-17",
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"pricing": "Pro $200/月, Plus $20/月"
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},
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"tech_specs": {
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"underlying_model": "GPT-5系列",
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"agent_type": "统一型自主Agent"
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}
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}
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```
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### 阶段3:生成报告
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### 阶段3:生成报告
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```
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```
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run /research/report
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run /research/report
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```
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```
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**输出:** `ai-agent-demo/report.md` - 带目录和所有item的Markdown报告
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💡 **发生了什么**:所有数据 → 一份整理好的报告
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**你会得到**:`report.md` - 带目录的完整Markdown报告,可直接阅读或分享
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## 参考文献
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## 参考文献
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