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：

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：

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：

python3 ${CLAUDE_SKILL_DIR}/tools/dingtalk_auto_collector.py --setup

然后输入姓名，一键采集：

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：

  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：

  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）：

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：

python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py --setup

之后直接读取：

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）：

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）：

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：

{
  "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：

---
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）：

   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：

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

/colleague-rollback {slug} {version}：

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：

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:

python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py --setup

After setup, just enter the name:

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:

python3 ${CLAUDE_SKILL_DIR}/tools/dingtalk_auto_collector.py --setup

Then enter the name:

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:

  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:

  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):

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:

python3 ${CLAUDE_SKILL_DIR}/tools/feishu_mcp_client.py --setup

Then read directly:

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):

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):

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:

{
  "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:

---
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):

   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:

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

/colleague-rollback {slug} {version}:

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

/delete-colleague {slug}: After confirmation:

rm -rf colleagues/{slug}