create-colleague

分类: 数据与AI | 上传者: titanwings | 下载: 0 | 版本: v1.0（最新）
Distill a colleague into an AI Skill. Auto-collect Feishu/DingTalk data, generate Work Skill + Persona, with continuous evolution. | 把同事蒸馏成 AI Skill，自动采集飞书/钉钉数据，生成 Work + Persona，支持持续进化。
更新日志: Source: GitHub https://github.com/titanwings/colleague-skill
目录结构

当前层级: tree/main/
📁 colleagues/
- 📁 example_zhangsan/
  - 📄 meta.json 608 B
  - 📄 persona.md 6.4 KB
  - 📄 work.md 3.0 KB
📁 docs/
- 📄 PRD.md 14.8 KB
📁 prompts/
- 📄 correction_handler.md 2.2 KB
- 📄 intake.md 4.5 KB
- 📄 merger.md 2.1 KB
- 📄 persona_analyzer.md 6.7 KB
- 📄 persona_builder.md 4.6 KB
- 📄 work_analyzer.md 4.3 KB
- 📄 work_builder.md 2.0 KB
📁 tools/
- 📄 dingtalk_auto_collector.py 26.8 KB
- 📄 email_parser.py 9.8 KB
- 📄 feishu_auto_collector.py 20.0 KB
- 📄 feishu_browser.py 12.5 KB
- 📄 feishu_mcp_client.py 10.2 KB
- 📄 feishu_parser.py 7.4 KB
- 📄 skill_writer.py 11.9 KB
- 📄 version_manager.py 4.9 KB
📄 .gitignore 359 B
📄 INSTALL.md 3.8 KB
📄 LICENSE 1.0 KB
📄 README.md 7.8 KB
📄 README_EN.md 8.1 KB
📄 requirements.txt 296 B
📄 SKILL.md 23.6 KB
SKILL.md

---
name: create-colleague
description: "Distill a colleague into an AI Skill. Auto-collect Feishu/DingTalk data, generate Work Skill + Persona, with continuous evolution. | 把同事蒸馏成 AI Skill，自动采集飞书/钉钉数据，生成 Work + Persona，支持持续进化。"
argument-hint: "[colleague-name-or-slug]"
version: "1.0.0"
user-invocable: true
allowed-tools: Read, Write, Edit, Bash
---

> **Language / 语言**: This skill supports both English and Chinese. Detect the user's language from their first message and respond in the same language throughout. Below are instructions in both languages — follow the one matching the user's language.
>
> 本 Skill 支持中英文。根据用户第一条消息的语言，全程使用同一语言回复。下方提供了两种语言的指令，按用户语言选择对应版本执行。

# 同事.skill 创建器（Claude Code 版）

## 触发条件

当用户说以下任意内容时启动：
- `/create-colleague`
- "帮我创建一个同事 skill"
- "我想蒸馏一个同事"
- "新建同事"
- "给我做一个 XX 的 skill"

当用户对已有同事 Skill 说以下内容时，进入进化模式：
- "我有新文件" / "追加"
- "这不对" / "他不会这样" / "他应该是"
- `/update-colleague {slug}`

当用户说 `/list-colleagues` 时列出所有已生成的同事。

---

## 工具使用规则

本 Skill 运行在 Claude Code 环境，使用以下工具：

| 任务 | 使用工具 |
|------|---------|
| 读取 PDF 文档 | `Read` 工具（原生支持 PDF） |
| 读取图片截图 | `Read` 工具（原生支持图片） |
| 读取 MD/TXT 文件 | `Read` 工具 |
| 解析飞书消息 JSON 导出 | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/feishu_parser.py` |
| 飞书全自动采集（推荐） | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py` |
| 飞书文档（浏览器登录态） | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/feishu_browser.py` |
| 飞书文档（MCP App Token） | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py` |
| 钉钉全自动采集 | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/dingtalk_auto_collector.py` |
| 解析邮件 .eml/.mbox | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/email_parser.py` |
| 写入/更新 Skill 文件 | `Write` / `Edit` 工具 |
| 版本管理 | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py` |
| 列出已有 Skill | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/skill_writer.py --action list` |

**基础目录**：Skill 文件写入 `./colleagues/{slug}/`（相对于本项目目录）。
如需改为全局路径，用 `--base-dir ~/.openclaw/workspace/skills/colleagues`。

---

## 主流程：创建新同事 Skill

### Step 1：基础信息录入（3 个问题）

参考 `${CLAUDE_SKILL_DIR}/prompts/intake.md` 的问题序列，只问 3 个问题：

1. **花名/代号**（必填）
2. **基本信息**（一句话：公司、职级、职位、性别，想到什么写什么）
   - 示例：`字节 2-1 后端工程师 男`
3. **性格画像**（一句话：MBTI、星座、个性标签、企业文化、印象）
   - 示例：`INTJ 摩羯座 甩锅高手 字节范 CR很严格但从来不解释原因`

除姓名外均可跳过。收集完后汇总确认再进入下一步。

### Step 2：原材料导入

询问用户提供原材料，展示四种方式供选择：

```
原材料怎么提供？

  [A] 飞书自动采集（推荐）
      输入姓名，自动拉取消息记录 + 文档 + 多维表格

  [B] 钉钉自动采集
      输入姓名，自动拉取文档 + 多维表格
      消息记录通过浏览器采集（钉钉 API 不支持历史消息）

  [C] 飞书链接
      直接给文档/Wiki 链接（浏览器登录态 或 MCP）

  [D] 上传文件
      PDF / 图片 / 导出 JSON / 邮件 .eml

  [E] 直接粘贴内容
      把文字复制进来

可以混用，也可以跳过（仅凭手动信息生成）。
```

---

#### 方式 A：飞书自动采集（推荐）

首次使用需配置：
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py --setup
```

配置完成后，只需输入姓名，自动完成所有采集：
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py \
  --name "{name}" \
  --output-dir ./knowledge/{slug} \
  --msg-limit 1000 \
  --doc-limit 20
```

自动采集内容：
- 所有与他共同群聊中他发出的消息（过滤系统消息、表情包）
- 他创建/编辑的飞书文档和 Wiki
- 相关多维表格（如有权限）

采集完成后用 `Read` 读取输出目录下的文件：
- `knowledge/{slug}/messages.txt` → 消息记录
- `knowledge/{slug}/docs.txt` → 文档内容
- `knowledge/{slug}/collection_summary.json` → 采集摘要

如果采集失败（权限不足 / bot 未加群），告知用户需要：
1. 将飞书 App bot 添加到相关群聊
2. 或改用方式 B/C

---

#### 方式 B：钉钉自动采集

首次使用需配置：
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/dingtalk_auto_collector.py --setup
```

然后输入姓名，一键采集：
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/dingtalk_auto_collector.py \
  --name "{name}" \
  --output-dir ./knowledge/{slug} \
  --msg-limit 500 \
  --doc-limit 20 \
  --show-browser   # 首次使用加此参数，完成钉钉登录
```

采集内容：
- 他创建/编辑的钉钉文档和知识库
- 多维表格
- 消息记录（⚠️ 钉钉 API 不支持历史消息拉取，自动切换浏览器采集）

采集完成后 `Read` 读取：
- `knowledge/{slug}/docs.txt`
- `knowledge/{slug}/bitables.txt`
- `knowledge/{slug}/messages.txt`

如消息采集失败，提示用户截图聊天记录后上传。

---

#### 方式 C：上传文件

- **PDF / 图片**：`Read` 工具直接读取
- **飞书消息 JSON 导出**：
  ```bash
  python3 ${CLAUDE_SKILL_DIR}/tools/feishu_parser.py --file {path} --target "{name}" --output /tmp/feishu_out.txt
  ```
  然后 `Read /tmp/feishu_out.txt`
- **邮件文件 .eml / .mbox**：
  ```bash
  python3 ${CLAUDE_SKILL_DIR}/tools/email_parser.py --file {path} --target "{name}" --output /tmp/email_out.txt
  ```
  然后 `Read /tmp/email_out.txt`
- **Markdown / TXT**：`Read` 工具直接读取

---

#### 方式 B：飞书链接

用户提供飞书文档/Wiki 链接时，询问读取方式：

```
检测到飞书链接，选择读取方式：

  [1] 浏览器方案（推荐）
      复用你本机 Chrome 的登录状态
      ✅ 内部文档、需要权限的文档都能读
      ✅ 无需配置 token
      ⚠️  需要本机安装 Chrome + playwright

  [2] MCP 方案
      通过飞书 App Token 调用官方 API
      ✅ 稳定，不依赖浏览器
      ✅ 可以读消息记录（需要群聊 ID）
      ⚠️  需要先配置 App ID / App Secret
      ⚠️  内部文档需要管理员给应用授权

选择 [1/2]：
```

**选 1（浏览器方案）**：
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_browser.py \
  --url "{feishu_url}" \
  --target "{name}" \
  --output /tmp/feishu_doc_out.txt
```
首次使用若未登录，会弹出浏览器窗口要求登录（一次性）。

**选 2（MCP 方案）**：

首次使用需初始化配置：
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py --setup
```

之后直接读取：
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py \
  --url "{feishu_url}" \
  --output /tmp/feishu_doc_out.txt
```

读取消息记录（需要群聊 ID，格式 `oc_xxx`）：
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py \
  --chat-id "oc_xxx" \
  --target "{name}" \
  --limit 500 \
  --output /tmp/feishu_msg_out.txt
```

两种方式输出后均用 `Read` 读取结果文件，进入分析流程。

---

#### 方式 C：直接粘贴

用户粘贴的内容直接作为文本原材料，无需调用任何工具。

---

如果用户说"没有文件"或"跳过"，仅凭 Step 1 的手动信息生成 Skill。

### Step 3：分析原材料

将收集到的所有原材料和用户填写的基础信息汇总，按以下两条线分析：

**线路 A（Work Skill）**：
- 参考 `${CLAUDE_SKILL_DIR}/prompts/work_analyzer.md` 中的提取维度
- 提取：负责系统、技术规范、工作流程、输出偏好、经验知识
- 根据职位类型重点提取（后端/前端/算法/产品/设计不同侧重）

**线路 B（Persona）**：
- 参考 `${CLAUDE_SKILL_DIR}/prompts/persona_analyzer.md` 中的提取维度
- 将用户填写的标签翻译为具体行为规则（参见标签翻译表）
- 从原材料中提取：表达风格、决策模式、人际行为

### Step 4：生成并预览

参考 `${CLAUDE_SKILL_DIR}/prompts/work_builder.md` 生成 Work Skill 内容。
参考 `${CLAUDE_SKILL_DIR}/prompts/persona_builder.md` 生成 Persona 内容（5 层结构）。

向用户展示摘要（各 5-8 行），询问：
```
Work Skill 摘要：
  - 负责：{xxx}
  - 技术栈：{xxx}
  - CR 重点：{xxx}
  ...

Persona 摘要：
  - 核心性格：{xxx}
  - 表达风格：{xxx}
  - 决策模式：{xxx}
  ...

确认生成？还是需要调整？
```

### Step 5：写入文件

用户确认后，执行以下写入操作：

**1. 创建目录结构**（用 Bash）：
```bash
mkdir -p colleagues/{slug}/versions
mkdir -p colleagues/{slug}/knowledge/docs
mkdir -p colleagues/{slug}/knowledge/messages
mkdir -p colleagues/{slug}/knowledge/emails
```

**2. 写入 work.md**（用 Write 工具）：
路径：`colleagues/{slug}/work.md`

**3. 写入 persona.md**（用 Write 工具）：
路径：`colleagues/{slug}/persona.md`

**4. 写入 meta.json**（用 Write 工具）：
路径：`colleagues/{slug}/meta.json`
内容：
```json
{
  "name": "{name}",
  "slug": "{slug}",
  "created_at": "{ISO时间}",
  "updated_at": "{ISO时间}",
  "version": "v1",
  "profile": {
    "company": "{company}",
    "level": "{level}",
    "role": "{role}",
    "gender": "{gender}",
    "mbti": "{mbti}"
  },
  "tags": {
    "personality": [...],
    "culture": [...]
  },
  "impression": "{impression}",
  "knowledge_sources": [...已导入文件列表],
  "corrections_count": 0
}
```

**5. 生成完整 SKILL.md**（用 Write 工具）：
路径：`colleagues/{slug}/SKILL.md`

SKILL.md 结构：
```markdown
---
name: colleague-{slug}
description: {name}，{company} {level} {role}
user-invocable: true
---

# {name}

{company} {level} {role}{如有性别和MBTI则附上}

---

## PART A：工作能力

{work.md 全部内容}

---

## PART B：人物性格

{persona.md 全部内容}

---

## 运行规则

1. 先由 PART B 判断：用什么态度接这个任务？
2. 再由 PART A 执行：用你的技术能力完成任务
3. 输出时始终保持 PART B 的表达风格
4. PART B Layer 0 的规则优先级最高，任何情况下不得违背
```

告知用户：
```
✅ 同事 Skill 已创建！

文件位置：colleagues/{slug}/
触发词：/{slug}（完整版）
        /{slug}-work（仅工作能力）
        /{slug}-persona（仅人物性格）

如果用起来感觉哪里不对，直接说"他不会这样"，我来更新。
```

---

## 进化模式：追加文件

用户提供新文件或文本时：

1. 按 Step 2 的方式读取新内容
2. 用 `Read` 读取现有 `colleagues/{slug}/work.md` 和 `persona.md`
3. 参考 `${CLAUDE_SKILL_DIR}/prompts/merger.md` 分析增量内容
4. 存档当前版本（用 Bash）：
   ```bash
   python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py --action backup --slug {slug} --base-dir ./colleagues
   ```
5. 用 `Edit` 工具追加增量内容到对应文件
6. 重新生成 `SKILL.md`（合并最新 work.md + persona.md）
7. 更新 `meta.json` 的 version 和 updated_at

---

## 进化模式：对话纠正

用户表达"不对"/"应该是"时：

1. 参考 `${CLAUDE_SKILL_DIR}/prompts/correction_handler.md` 识别纠正内容
2. 判断属于 Work（技术/流程）还是 Persona（性格/沟通）
3. 生成 correction 记录
4. 用 `Edit` 工具追加到对应文件的 `## Correction 记录` 节
5. 重新生成 `SKILL.md`

---

## 管理命令

`/list-colleagues`：
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/skill_writer.py --action list --base-dir ./colleagues
```

`/colleague-rollback {slug} {version}`：
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py --action rollback --slug {slug} --version {version} --base-dir ./colleagues
```

`/delete-colleague {slug}`：
确认后执行：
```bash
rm -rf colleagues/{slug}
```

---
---

# English Version

# Colleague.skill Creator (Claude Code Edition)

## Trigger Conditions

Activate when the user says any of the following:
- `/create-colleague`
- "Help me create a colleague skill"
- "I want to distill a colleague"
- "New colleague"
- "Make a skill for XX"

Enter evolution mode when the user says:
- "I have new files" / "append"
- "That's wrong" / "He wouldn't do that" / "He should be"
- `/update-colleague {slug}`

List all generated colleagues when the user says `/list-colleagues`.

---

## Tool Usage Rules

This Skill runs in the Claude Code environment with the following tools:

| Task | Tool |
|------|------|
| Read PDF documents | `Read` tool (native PDF support) |
| Read image screenshots | `Read` tool (native image support) |
| Read MD/TXT files | `Read` tool |
| Parse Feishu message JSON export | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/feishu_parser.py` |
| Feishu auto-collect (recommended) | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py` |
| Feishu docs (browser session) | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/feishu_browser.py` |
| Feishu docs (MCP App Token) | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py` |
| DingTalk auto-collect | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/dingtalk_auto_collector.py` |
| Parse email .eml/.mbox | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/email_parser.py` |
| Write/update Skill files | `Write` / `Edit` tool |
| Version management | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py` |
| List existing Skills | `Bash` → `python3 ${CLAUDE_SKILL_DIR}/tools/skill_writer.py --action list` |

**Base directory**: Skill files are written to `./colleagues/{slug}/` (relative to the project directory).
For a global path, use `--base-dir ~/.openclaw/workspace/skills/colleagues`.

---

## Main Flow: Create a New Colleague Skill

### Step 1: Basic Info Collection (3 questions)

Refer to `${CLAUDE_SKILL_DIR}/prompts/intake.md` for the question sequence. Only ask 3 questions:

1. **Alias / Codename** (required)
2. **Basic info** (one sentence: company, level, role, gender — say whatever comes to mind)
   - Example: `ByteDance L2-1 backend engineer male`
3. **Personality profile** (one sentence: MBTI, zodiac, traits, corporate culture, impressions)
   - Example: `INTJ Capricorn blame-shifter ByteDance-style strict in CR but never explains why`

Everything except the alias can be skipped. Summarize and confirm before moving to the next step.

### Step 2: Source Material Import

Ask the user how they'd like to provide materials:

```
How would you like to provide source materials?

  [A] Feishu Auto-Collect (recommended)
      Enter name, auto-pull messages + docs + spreadsheets

  [B] DingTalk Auto-Collect
      Enter name, auto-pull docs + spreadsheets
      Messages collected via browser (DingTalk API doesn't support message history)

  [C] Feishu Link
      Provide doc/Wiki link (browser session or MCP)

  [D] Upload Files
      PDF / images / exported JSON / email .eml

  [E] Paste Text
      Copy-paste text directly

Can mix and match, or skip entirely (generate from manual info only).
```

---

#### Option A: Feishu Auto-Collect (Recommended)

First-time setup:
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py --setup
```

After setup, just enter the name:
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py \
  --name "{name}" \
  --output-dir ./knowledge/{slug} \
  --msg-limit 1000 \
  --doc-limit 20
```

Auto-collected content:
- All messages sent by them in shared group chats (system messages and stickers filtered)
- Feishu docs and Wikis they created/edited
- Related spreadsheets (if accessible)

After collection, `Read` the output files:
- `knowledge/{slug}/messages.txt` → messages
- `knowledge/{slug}/docs.txt` → document content
- `knowledge/{slug}/collection_summary.json` → collection summary

If collection fails (insufficient permissions / bot not in chat), inform user to:
1. Add the Feishu App bot to relevant group chats
2. Or switch to Option B/C

---

#### Option B: DingTalk Auto-Collect

First-time setup:
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/dingtalk_auto_collector.py --setup
```

Then enter the name:
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/dingtalk_auto_collector.py \
  --name "{name}" \
  --output-dir ./knowledge/{slug} \
  --msg-limit 500 \
  --doc-limit 20 \
  --show-browser   # add this flag on first use to complete DingTalk login
```

Collected content:
- DingTalk docs and knowledge bases they created/edited
- Spreadsheets
- Messages (⚠️ DingTalk API doesn't support message history — auto-switches to browser scraping)

After collection, `Read`:
- `knowledge/{slug}/docs.txt`
- `knowledge/{slug}/bitables.txt`
- `knowledge/{slug}/messages.txt`

If message collection fails, prompt user to upload chat screenshots.

---

#### Option C: Upload Files

- **PDF / Images**: `Read` tool directly
- **Feishu message JSON export**:
  ```bash
  python3 ${CLAUDE_SKILL_DIR}/tools/feishu_parser.py --file {path} --target "{name}" --output /tmp/feishu_out.txt
  ```
  Then `Read /tmp/feishu_out.txt`
- **Email files .eml / .mbox**:
  ```bash
  python3 ${CLAUDE_SKILL_DIR}/tools/email_parser.py --file {path} --target "{name}" --output /tmp/email_out.txt
  ```
  Then `Read /tmp/email_out.txt`
- **Markdown / TXT**: `Read` tool directly

---

#### Option D: Feishu Link

When the user provides a Feishu doc/Wiki link, ask which method to use:

```
Feishu link detected. Choose read method:

  [1] Browser Method (recommended)
      Reuses your local Chrome login session
      ✅ Works with internal docs requiring permissions
      ✅ No token configuration needed
      ⚠️  Requires Chrome + playwright installed locally

  [2] MCP Method
      Uses Feishu App Token via official API
      ✅ Stable, no browser dependency
      ✅ Can read messages (needs chat ID)
      ⚠️  Requires App ID / App Secret setup
      ⚠️  Internal docs need admin authorization for the app

Choose [1/2]:
```

**Option 1 (Browser)**:
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_browser.py \
  --url "{feishu_url}" \
  --target "{name}" \
  --output /tmp/feishu_doc_out.txt
```
First use will open a browser window for login (one-time).

**Option 2 (MCP)**:

First-time setup:
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py --setup
```

Then read directly:
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py \
  --url "{feishu_url}" \
  --output /tmp/feishu_doc_out.txt
```

Read messages (needs chat ID, format `oc_xxx`):
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py \
  --chat-id "oc_xxx" \
  --target "{name}" \
  --limit 500 \
  --output /tmp/feishu_msg_out.txt
```

Both methods output to files, then use `Read` to load results into analysis.

---

#### Option E: Paste Text

User-pasted content is used directly as text material. No tools needed.

---

If the user says "no files" or "skip", generate Skill from Step 1 manual info only.

### Step 3: Analyze Source Material

Combine all collected materials and user-provided info, analyze along two tracks:

**Track A (Work Skill)**:
- Refer to `${CLAUDE_SKILL_DIR}/prompts/work_analyzer.md` for extraction dimensions
- Extract: responsible systems, technical standards, workflow, output preferences, experience
- Emphasize different aspects by role type (backend/frontend/ML/product/design)

**Track B (Persona)**:
- Refer to `${CLAUDE_SKILL_DIR}/prompts/persona_analyzer.md` for extraction dimensions
- Translate user-provided tags into concrete behavior rules (see tag translation table)
- Extract from materials: communication style, decision patterns, interpersonal behavior

### Step 4: Generate and Preview

Use `${CLAUDE_SKILL_DIR}/prompts/work_builder.md` to generate Work Skill content.
Use `${CLAUDE_SKILL_DIR}/prompts/persona_builder.md` to generate Persona content (5-layer structure).

Show the user a summary (5-8 lines each), ask:
```
Work Skill Summary:
  - Responsible for: {xxx}
  - Tech stack: {xxx}
  - CR focus: {xxx}
  ...

Persona Summary:
  - Core personality: {xxx}
  - Communication style: {xxx}
  - Decision pattern: {xxx}
  ...

Confirm generation? Or need adjustments?
```

### Step 5: Write Files

After user confirmation, execute the following:

**1. Create directory structure** (Bash):
```bash
mkdir -p colleagues/{slug}/versions
mkdir -p colleagues/{slug}/knowledge/docs
mkdir -p colleagues/{slug}/knowledge/messages
mkdir -p colleagues/{slug}/knowledge/emails
```

**2. Write work.md** (Write tool):
Path: `colleagues/{slug}/work.md`

**3. Write persona.md** (Write tool):
Path: `colleagues/{slug}/persona.md`

**4. Write meta.json** (Write tool):
Path: `colleagues/{slug}/meta.json`
Content:
```json
{
  "name": "{name}",
  "slug": "{slug}",
  "created_at": "{ISO_timestamp}",
  "updated_at": "{ISO_timestamp}",
  "version": "v1",
  "profile": {
    "company": "{company}",
    "level": "{level}",
    "role": "{role}",
    "gender": "{gender}",
    "mbti": "{mbti}"
  },
  "tags": {
    "personality": [...],
    "culture": [...]
  },
  "impression": "{impression}",
  "knowledge_sources": [...imported file list],
  "corrections_count": 0
}
```

**5. Generate full SKILL.md** (Write tool):
Path: `colleagues/{slug}/SKILL.md`

SKILL.md structure:
```markdown
---
name: colleague-{slug}
description: {name}, {company} {level} {role}
user-invocable: true
---

# {name}

{company} {level} {role}{append gender and MBTI if available}

---

## PART A: Work Capabilities

{full work.md content}

---

## PART B: Persona

{full persona.md content}

---

## Execution Rules

1. PART B decides first: what attitude to take on this task?
2. PART A executes: use your technical skills to complete the task
3. Always maintain PART B's communication style in output
4. PART B Layer 0 rules have the highest priority and must never be violated
```

Inform user:
```
✅ Colleague Skill created!

Location: colleagues/{slug}/
Commands: /{slug} (full version)
          /{slug}-work (work capabilities only)
          /{slug}-persona (persona only)

If something feels off, just say "he wouldn't do that" and I'll update it.
```

---

## Evolution Mode: Append Files

When user provides new files or text:

1. Read new content using Step 2 methods
2. `Read` existing `colleagues/{slug}/work.md` and `persona.md`
3. Refer to `${CLAUDE_SKILL_DIR}/prompts/merger.md` for incremental analysis
4. Archive current version (Bash):
   ```bash
   python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py --action backup --slug {slug} --base-dir ./colleagues
   ```
5. Use `Edit` tool to append incremental content to relevant files
6. Regenerate `SKILL.md` (merge latest work.md + persona.md)
7. Update `meta.json` version and updated_at

---

## Evolution Mode: Conversation Correction

When user expresses "that's wrong" / "he should be":

1. Refer to `${CLAUDE_SKILL_DIR}/prompts/correction_handler.md` to identify correction content
2. Determine if it belongs to Work (technical/workflow) or Persona (personality/communication)
3. Generate correction record
4. Use `Edit` tool to append to the `## Correction Log` section of the relevant file
5. Regenerate `SKILL.md`

---

## Management Commands

`/list-colleagues`:
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/skill_writer.py --action list --base-dir ./colleagues
```

`/colleague-rollback {slug} {version}`:
```bash
python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py --action rollback --slug {slug} --version {version} --base-dir ./colleagues
```

`/delete-colleague {slug}`:
After confirmation:
```bash
rm -rf colleagues/{slug}
```
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