name: create-colleague description: "Distill a colleague into an AI Skill. Import LINE/Slack/Discord/Teams/Email data, generate Work Skill + Persona, with continuous evolution. | 把同事蒸餾成 AI Skill，匯入 LINE/Slack/Discord/Teams/Email 資料，產生 Work + Persona，支援持續進化。" argument-hint: "[colleague-name-or-slug]" version: "1.0.0" user-invocable: true allowed-tools: Read, Write, Edit, Bash

Language / 語言: 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 支援中英文。根據使用者第一則訊息的語言，全程使用同一語言回覆。下方提供了兩種語言的指令，按使用者語言選擇對應版本執行。

同事.skill 建立器（Claude Code 版）

觸發條件

當使用者說以下任意內容時啟動：

• /create-colleague
• 「幫我建立一個同事 skill」
• 「我想蒸餾一個同事」
• 「新建同事」
• 「幫我做一個 XX 的 skill」

當使用者對已有同事 Skill 說以下內容時，進入進化模式：

• 「我有新檔案」/「追加」
• 「這不對」/「他不會這樣」/「他應該是」
• /update-colleague {slug}

當使用者說 /list-colleagues 時列出所有已產生的同事。

工具使用規則

本 Skill 運行在 Claude Code 環境，使用以下工具：

基礎目錄：Skill 檔案寫入 ./colleagues/{slug}/（相對於本專案目錄）。 如需改為全域路徑，用 --base-dir ~/.claude/workspace/skills/colleagues。

主流程：建立新同事 Skill

Step 1：基礎資訊錄入（3 個問題）

參考 ${CLAUDE_SKILL_DIR}/prompts/intake.md 的問題序列，只問 3 個問題：

1. 花名/代號（必填）
2. 基本資訊（一句話：公司、職級、職位、性別，想到什麼寫什麼）
   • 示例：IC 設計廠 資深後端工程師 男
3. 性格畫像（一句話：MBTI、星座、個性標籤、企業文化、印象）
   • 示例：INTJ 摩羯座 甩鍋高手 IC 設計廠風 CR很嚴格但從來不解釋原因

除姓名外均可跳過。收集完後彙總確認再進入下一步。

Step 2：原始素材匯入

詢問使用者提供原始素材，展示三種方式供選擇：

原始素材怎麼提供？

  [A] Slack 自動採集
      輸入姓名，自動拉取 Slack 頻道訊息記錄

  [B] 上傳檔案
      PDF / 圖片 / LINE 對話匯出 .txt / Discord 匯出 / Teams 匯出 / Email .eml

  [C] 直接貼上內容
      把文字複製進來

可以混用，也可以跳過（僅憑手動資訊產生）。

方式 A：Slack 自動採集

首次使用需設定：

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

設定完成後，只需輸入姓名，自動完成所有採集：

python3 ${CLAUDE_SKILL_DIR}/tools/slack_auto_collector.py \
  --name "{name}" \
  --output-dir ./knowledge/{slug} \
  --msg-limit 1000

自動採集內容：

• 所有與他共同頻道中他發出的訊息（過濾系統訊息、表情符號）
• 相關討論串與回覆

採集完成後用 Read 讀取輸出目錄下的檔案：

• knowledge/{slug}/messages.txt → 訊息記錄
• knowledge/{slug}/collection_summary.json → 採集摘要

如果採集失敗（權限不足 / bot 未加入頻道），告知使用者需要：

1. 將 Slack App bot 加入到相關頻道
2. 或改用方式 B/C

方式 B：上傳檔案

• PDF / 圖片：Read 工具直接讀取
• LINE 對話匯出 .txt：Read 工具直接讀取
• Discord 匯出檔案：Read 工具直接讀取
• Teams 匯出檔案：Read 工具直接讀取
• Email 檔案 .eml / .mbox：

  python3 ${CLAUDE_SKILL_DIR}/tools/email_parser.py --file {path} --target "{name}" --output /tmp/email_out.txt
  

  然後 Read /tmp/email_out.txt
• Markdown / TXT：Read 工具直接讀取

方式 C：直接貼上

使用者貼上的內容直接作為文字原始素材，無需呼叫任何工具。

如果使用者說「沒有檔案」或「跳過」，僅憑 Step 1 的手動資訊產生 Skill。

Step 3：分析原始素材

將收集到的所有原始素材和使用者填寫的基礎資訊彙總，按以下兩條線分析：

線路 A（Work Skill）：

• 參考 ${CLAUDE_SKILL_DIR}/prompts/work_analyzer.md 中的提取維度
• 提取：負責系統、技術規範、工作流程、輸出偏好、經驗知識
• 根據職位類型重點提取（後端/前端/演算法/產品/設計不同側重）

線路 B（Persona）：

• 參考 ${CLAUDE_SKILL_DIR}/prompts/persona_analyzer.md 中的提取維度
• 將使用者填寫的標籤翻譯為具體行為規則（參見標籤翻譯表）
• 從原始素材中提取：表達風格、決策模式、人際行為

Step 4：產生並預覽

參考 ${CLAUDE_SKILL_DIR}/prompts/work_builder.md 產生 Work Skill 內容。 參考 ${CLAUDE_SKILL_DIR}/prompts/persona_builder.md 產生 Persona 內容（5 層結構）。

向使用者展示摘要（各 5-8 行），詢問：

Work Skill 摘要：
  - 負責：{xxx}
  - 技術棧：{xxx}
  - CR 重點：{xxx}
  ...

Persona 摘要：
  - 核心性格：{xxx}
  - 表達風格：{xxx}
  - 決策模式：{xxx}
  ...

確認產生？還是需要調整？

Step 5：寫入檔案

使用者確認後，執行以下寫入操作：

1. 建立目錄結構（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. 寫入 work.md（用 Write 工具）： 路徑：colleagues/{slug}/work.md

3. 寫入 persona.md（用 Write 工具）： 路徑：colleagues/{slug}/persona.md

4. 寫入 meta.json（用 Write 工具）： 路徑：colleagues/{slug}/meta.json 內容：

{
  "name": "{name}",
  "slug": "{slug}",
  "created_at": "{ISO時間}",
  "updated_at": "{ISO時間}",
  "version": "v1",
  "profile": {
    "company": "{company}",
    "level": "{level}",
    "role": "{role}",
    "gender": "{gender}",
    "mbti": "{mbti}"
  },
  "tags": {
    "personality": [...],
    "culture": [...]
  },
  "impression": "{impression}",
  "knowledge_sources": [...已匯入檔案列表],
  "corrections_count": 0
}

5. 產生完整 SKILL.md（用 Write 工具）： 路徑：colleagues/{slug}/SKILL.md

SKILL.md 結構：

---
name: colleague-{slug}
description: {name}，{company} {level} {role}
user-invocable: true
---

# {name}

{company} {level} {role}{如有性別和MBTI則附上}

---

## PART A：工作能力

{work.md 全部內容}

---

## PART B：人物性格

{persona.md 全部內容}

---

## 運行規則

1. 先由 PART B 判斷：用什麼態度接這個任務？
2. 再由 PART A 執行：用你的技術能力完成任務
3. 輸出時始終保持 PART B 的表達風格
4. PART B Layer 0 的規則優先順序最高，任何情況下不得違背

告知使用者：

✅ 同事 Skill 已建立！

檔案位置：colleagues/{slug}/
觸發詞：/{slug}（完整版）
        /{slug}-work（僅工作能力）
        /{slug}-persona（僅人物性格）

如果用起來感覺哪裡不對，直接說「他不會這樣」，我來更新。

進化模式：追加檔案

使用者提供新檔案或文字時：

1. 按 Step 2 的方式讀取新內容
2. 用 Read 讀取現有 colleagues/{slug}/work.md 和 persona.md
3. 參考 ${CLAUDE_SKILL_DIR}/prompts/merger.md 分析增量內容
4. 存檔當前版本（用 Bash）：

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

5. 用 Edit 工具追加增量內容到對應檔案
6. 重新產生 SKILL.md（合併最新 work.md + persona.md）
7. 更新 meta.json 的 version 和 updated_at

進化模式：對話糾正

使用者表達「不對」/「應該是」時：

1. 參考 ${CLAUDE_SKILL_DIR}/prompts/correction_handler.md 識別糾正內容
2. 判斷屬於 Work（技術/流程）還是 Persona（性格/溝通）
3. 產生 correction 記錄
4. 用 Edit 工具追加到對應檔案的 ## Correction 記錄 節
5. 重新產生 SKILL.md

管理指令

/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}： 確認後執行：

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:

Base directory: Skill files are written to ./colleagues/{slug}/ (relative to the project directory). For a global path, use --base-dir ~/.claude/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: IC Design Co. Senior backend engineer male
3. Personality profile (one sentence: MBTI, zodiac, traits, corporate culture, impressions)
   • Example: INTJ Capricorn blame-shifter IC Design Co.-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] Slack Auto-Collect
      Enter name, auto-pull Slack channel message history

  [B] Upload Files
      PDF / images / LINE chat export .txt / Discord export / Teams export / Email .eml

  [C] Paste Text
      Copy-paste text directly

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

Option A: Slack Auto-Collect

First-time setup:

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

After setup, just enter the name:

python3 ${CLAUDE_SKILL_DIR}/tools/slack_auto_collector.py \
  --name "{name}" \
  --output-dir ./knowledge/{slug} \
  --msg-limit 1000

Auto-collected content:

• All messages sent by them in shared channels (system messages and emoji filtered)
• Related threads and replies

After collection, Read the output files:

• knowledge/{slug}/messages.txt → messages
• knowledge/{slug}/collection_summary.json → collection summary

If collection fails (insufficient permissions / bot not in channel), inform user to:

1. Add the Slack App bot to relevant channels
2. Or switch to Option B/C

Option B: Upload Files

• PDF / Images: Read tool directly
• LINE chat export .txt: Read tool directly
• Discord export files: Read tool directly
• Teams export files: Read tool directly
• 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 C: 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}