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：

花名/代号（必填）
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
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：

将飞书 App bot 添加到相关群聊
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：

按 Step 2 的方式读取新内容
用 Read 读取现有 colleagues/{slug}/work.md 和 persona.md
参考 ${CLAUDE_SKILL_DIR}/prompts/merger.md 分析增量内容

存档当前版本（用 Bash）：

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

用 Edit 工具追加增量内容到对应文件
重新生成 SKILL.md（合并最新 work.md + persona.md）
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：

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

Alias / Codename (required)
Basic info (one sentence: company, level, role, gender — say whatever comes to mind)
- Example: ByteDance L2-1 backend engineer male
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:

Add the Feishu App bot to relevant group chats
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:

Read new content using Step 2 methods
Read existing colleagues/{slug}/work.md and persona.md
Refer to ${CLAUDE_SKILL_DIR}/prompts/merger.md for incremental analysis

Archive current version (Bash):

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

Use Edit tool to append incremental content to relevant files
Regenerate SKILL.md (merge latest work.md + persona.md)
Update meta.json version and updated_at

Evolution Mode: Conversation Correction

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

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

create-colleague

同事.skill 创建器（Claude Code 版）

触发条件

工具使用规则

主流程：创建新同事 Skill

Step 1：基础信息录入（3 个问题）

Step 2：原材料导入

方式 A：飞书自动采集（推荐）

方式 B：钉钉自动采集

方式 C：上传文件

方式 B：飞书链接

方式 C：直接粘贴

Step 3：分析原材料

Step 4：生成并预览

Step 5：写入文件

进化模式：追加文件

进化模式：对话纠正

管理命令

English Version

Colleague.skill Creator (Claude Code Edition)

Trigger Conditions

Tool Usage Rules

Main Flow: Create a New Colleague Skill

Step 1: Basic Info Collection (3 questions)

Step 2: Source Material Import

Option A: Feishu Auto-Collect (Recommended)

Option B: DingTalk Auto-Collect

Option C: Upload Files

Option D: Feishu Link

Option E: Paste Text

Step 3: Analyze Source Material

Step 4: Generate and Preview

Step 5: Write Files

Evolution Mode: Append Files

Evolution Mode: Conversation Correction

Management Commands