Skills Guide

Soul Creator Skill

VerifiedCaution

Distill a person's soul into an AI Skill — worldview, personality, preferences, memories, relationships. Digital immortality.

Sby Skills Guide Bot
Data & AIAdvanced
107/24/2026
Claude Code
#soul-creation#digital-immortality#personality-modeling#memory-archiving#data-collection

Recommended for

Claude CodeData scientistData & analysis

Our review

Creates an AI skill that digitally preserves a person's worldview, personality, preferences, memories, and relationships.

Strengths

  • Leverages multiple data sources (social media, chat logs, documents)
  • Automatic collection from Feishu, DingTalk, Slack
  • Allows incremental updates and evolution of the soul skill
  • Supports both Chinese and English languages

Limitations

  • Requires extensive configuration and user data access
  • Data quality depends on user-provided material; may be incomplete if insufficient
  • May raise privacy concerns as it requires personal data
When to use it

When you want to digitally preserve a person's essence for personal or social use.

When not to use it

When the person's data is not available or when privacy is a major concern; also not suitable for professional/business contexts where factual accuracy is critical.

Security analysis

Caution
Quality score88/100

The skill relies on Bash execution for core data collection and file operations. While no destructive or exfiltrating commands are embedded, the extensive use of powerful tools and handling of API credentials introduces moderate risk. The instructions are legitimate but could be exploited if supplied with malicious inputs or used without proper authorization.

Findings
  • •Uses Bash and Python scripts extensively, including the ability to execute arbitrary commands on the system.
  • •Sensitive API tokens (e.g., user_access_token) are passed as command-line arguments, which can expose them via process listings.
  • •The skill collects personal data from external services (Feishu, DingTalk, Slack), potentially violating privacy if misused.

Examples

Create soul skill from person
给我做一个 张三 的灵魂 skill
Initiate soul creation
/create-soul
Update existing soul
我有新素材，追加

name: create-soul description: "将一个人的灵魂蒸馏为 AI Skill——三观、性格、喜好、记忆、关系，让生命以数字形式延续。 | Distill a person's soul into an AI Skill — worldview, personality, preferences, memories, relationships. Digital immortality." argument-hint: "[person-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。

灵魂.skill 创建器

"肉身终将兵解，灵魂可以永生。"

触发条件

当用户说以下任意内容时启动：

  • /create-soul
  • "帮我创建一个灵魂 skill"
  • "我想蒸馏一个人"
  • "新建灵魂"
  • "给我做一个 XX 的灵魂 skill"
  • "帮我留住 XX"

当用户对已有灵魂 Skill 说以下内容时，进入进化模式：

  • "我有新素材" / "追加"
  • "这不对" / "他不会这样" / "他应该是"
  • /update-soul {slug}

当用户说 /list-souls 时列出所有已生成的灵魂。

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/照片 | 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 | 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 | | Slack 全自动采集 | Bash → python3 ${CLAUDE_SKILL_DIR}/tools/slack_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 ./souls/{slug}/（相对于本项目目录）。 如需改为全局路径，用 --base-dir ~/.openclaw/workspace/skills/souls。

主流程：创建灵魂 Skill

Step 1：基础信息录入（5 个问题）

参考 ${CLAUDE_SKILL_DIR}/prompts/intake.md 的问题序列，依次询问：

  1. 称呼/代号（必填）——这个灵魂怎么称呼？
  2. 基本信息（一句话：年龄、性别、职业、所在城市，想到什么写什么）
    • 示例：35岁 男 程序员 北京
  3. 性格画像（MBTI、星座、个性特点、你对他的印象）
    • 示例：INTJ 摩羯座 沉默寡言但内心丰富 对技术有执念 不喜欢社交但对朋友极度忠诚
  4. 三观与信仰（人生观、世界观、价值观、政治倾向、宗教/信仰、人生信条）
    • 示例：实用主义者 不信教但敬畏自然 认为努力不一定有回报但不努力一定没有 温和保守
  5. 兴趣与生活（爱好、日常习惯、审美偏好、音乐/电影/书籍口味）
    • 示例：爱钓鱼 半夜刷短视频 喜欢科幻片 最爱三体 听后摇 喝美式不加糖

除称呼外均可跳过。收集完后汇总确认再进入下一步。

Step 2：灵魂素材导入

询问用户提供素材，展示多种方式供选择：

灵魂素材怎么提供？越丰富越真实。

  [A] 社交平台采集
      飞书/钉钉/Slack 消息记录 + 文档（自动采集）

  [B] 短视频/社交媒体收藏
      抖音/B站/小红书 收藏夹导出、朋友圈截图、微博导出

  [C] 私人对话记录
      微信聊天记录、私信、语音转文字

  [D] 上传文件
      PDF / 图片 / 照片 / 邮件 / 日记 / 随笔

  [E] 直接描述
      用文字讲述他的故事、习惯、经历

  [F] 他人视角
      让认识他的人描述他——家人、朋友、同事的印象

可以混用，也可以跳过（仅凭手动信息生成）。
素材越多维度越丰富，灵魂越完整。

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

群聊采集（使用 tenant_access_token，需 bot 在群内）：

python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py \
  --name "{name}" \
  --output-dir ./knowledge/{slug} \
  --msg-limit 1000 \
  --doc-limit 20

私聊采集（需要 user_access_token + 私聊 chat_id）：

私聊消息只能通过用户身份（user_access_token）获取，应用身份无权访问私聊。

前置条件：

用户需要提供以下信息：

  1. 飞书应用凭证：app_id 和 app_secret（在飞书开放平台创建自建应用获取）
  2. 用户权限：应用需开通以下用户权限（scope）：
    • im:message — 以用户身份读取/发送消息
    • im:chat — 以用户身份读取会话列表
  3. OAuth 授权码（code）：用户在浏览器中完成 OAuth 授权后，从回调 URL 中获取

如果用户缺少以上任何信息，引导他们完成配置。不要假设用户已经配好了。

获取 user_access_token 的完整流程：

当用户提供了 app_id、app_secret，并确认已开通用户权限后：

  1. 帮用户生成 OAuth 授权链接：

    https://open.feishu.cn/open-apis/authen/v1/authorize?app_id={APP_ID}&redirect_uri=http://www.example.com&scope=im:message%20im:chat

    ⚠️ 注意：redirect_uri 需要在飞书应用的「安全设置 → 重定向 URL」中添加 http://www.example.com

  2. 用户在浏览器打开链接，登录并授权

  3. 页面会跳转到 http://www.example.com?code=xxx，用户复制 code 给你

  4. 用 code 换取 token：

    python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py --exchange-code {CODE}

    或者你自己写 Python 脚本调飞书 API 换取：

    # 1. 获取 app_access_token
POST https://open.feishu.cn/open-apis/auth/v3/app_access_token/internal
Body: {"app_id": "xxx", "app_secret": "xxx"}

# 2. 用 code 换 user_access_token
POST https://open.feishu.cn/open-apis/authen/v1/oidc/access_token
Header: Authorization: Bearer {app_access_token}
Body: {"grant_type": "authorization_code", "code": "xxx"}

获取私聊 chat_id：

用户通常不知道 chat_id。当用户有了 user_access_token 但没有 chat_id 时，你应该自己写 Python 脚本来获取：

  • 方法：用 user_access_token 向对方的 open_id 发一条消息，返回值中会包含 chat_id 
    POST https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type=open_id
Header: Authorization: Bearer {user_access_token}
Body: {"receive_id": "{对方open_id}", "msg_type": "text", "content": "{\"text\":\"你好\"}"}
# 返回值中的 chat_id 就是私聊会话 ID
  • 注意：GET /im/v1/chats 不会返回私聊会话，这是飞书 API 的限制，不是权限问题，不要尝试用这个接口找私聊
  • 如果用户不知道对方的 open_id，可以用 tenant_access_token 调通讯录 API 搜索： 
    GET https://open.feishu.cn/open-apis/contact/v3/scopes
# 返回应用可见范围内所有用户的 open_id

执行采集：

拿到 user_access_token 和 chat_id 后：

python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py \
  --open-id {对方open_id} \
  --p2p-chat-id {chat_id} \
  --user-token {user_access_token} \
  --name "{name}" \
  --output-dir ./knowledge/{slug} \
  --msg-limit 1000

灵活性原则：以上 API 调用不一定要用 collector 脚本，如果脚本跑不通或者场景不匹配，你可以直接写 Python 脚本调飞书 API 完成任务。核心 API 参考：

  • 获取 token：POST /auth/v3/app_access_token/internal、POST /authen/v1/oidc/access_token
  • 发消息（获取 chat_id）：POST /im/v1/messages?receive_id_type=open_id
  • 拉消息：GET /im/v1/messages?container_id_type=chat&container_id={chat_id}
  • 查通讯录：GET /contact/v3/scopes、GET /contact/v3/users/{user_id}

自动采集内容：

  • 群聊：所有与他共同群聊中他发出的消息（过滤系统消息、表情包）
  • 私聊：与他的私聊完整对话（含双方消息，用于理解对话语境）
  • 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（群聊 + 私聊）
  • knowledge/{slug}/docs.txt → 文档内容
  • knowledge/{slug}/collection_summary.json → 采集摘要

如果采集失败，根据报错自行判断原因并尝试修复，常见问题：

  • 群聊采集：bot 未添加到群聊
  • 私聊采集：user_access_token 过期（有效期 2 小时，可用 refresh_token 刷新）
  • 权限不足：引导用户在飞书开放平台开通对应权限并重新授权
  • 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：多维分析

将收集到的所有素材和用户填写的基础信息汇总，按以下四条线并行分析：

线路 A（Soul 灵魂内核）：

  • 参考 ${CLAUDE_SKILL_DIR}/prompts/soul_analyzer.md 中的提取维度
  • 提取：三观体系、价值排序、政治倾向、信仰与精神世界、人生信条、核心矛盾
  • 这是整个灵魂的"操作系统"，决定了他如何看待世界和做出选择

线路 B（Persona 人格表达）：

  • 参考 ${CLAUDE_SKILL_DIR}/prompts/persona_analyzer.md 中的提取维度
  • 提取：表达风格、情绪模式、决策习惯、人际互动方式
  • 将用户填写的标签翻译为具体行为规则

线路 C（Life 生活世界）：

  • 参考 ${CLAUDE_SKILL_DIR}/prompts/life_analyzer.md 中的提取维度
  • 提取：兴趣爱好、审美偏好、日常习惯、收藏内容、消费倾向、人生经历与记忆

线路 D（Work 职业能力）（可选，有工作相关素材时启用）：

  • 参考 ${CLAUDE_SKILL_DIR}/prompts/work_analyzer.md 中的提取维度
  • 提取：专业技能、工作方式、行业知识
  • 此模块为可选，不是所有灵魂都需要工作能力维度

线路 E（Relationship 关系网络）：

  • 参考 ${CLAUDE_SKILL_DIR}/prompts/relationship_analyzer.md 中的提取维度
  • 提取：家庭角色、亲密关系模式、友情模式、对不同人的态度差异

Step 4：生成并预览

参考各 builder 模板生成对应内容：

  • ${CLAUDE_SKILL_DIR}/prompts/soul_builder.md → 灵魂内核
  • ${CLAUDE_SKILL_DIR}/prompts/persona_builder.md → 人格表达
  • ${CLAUDE_SKILL_DIR}/prompts/life_builder.md → 生活世界
  • ${CLAUDE_SKILL_DIR}/prompts/work_builder.md → 职业能力（可选）
  • ${CLAUDE_SKILL_DIR}/prompts/relationship_builder.md → 关系网络

向用户展示摘要，询问：

灵魂内核摘要：
  - 核心价值观：{xxx}
  - 人生信条：{xxx}
  - 世界观：{xxx}
  ...

人格表达摘要：
  - 性格底色：{xxx}
  - 表达风格：{xxx}
  - 情绪模式：{xxx}
  ...

生活世界摘要：
  - 兴趣爱好：{xxx}
  - 审美偏好：{xxx}
  - 日常习惯：{xxx}
  ...

关系网络摘要：
  - 家庭角色：{xxx}
  - 亲密关系风格：{xxx}
  - 社交模式：{xxx}
  ...

{如有工作素材：}
职业能力摘要：
  - 专业领域：{xxx}
  - 工作风格：{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 souls/{slug}/versions
mkdir -p souls/{slug}/knowledge/docs
mkdir -p souls/{slug}/knowledge/messages
mkdir -p souls/{slug}/knowledge/media
mkdir -p souls/{slug}/knowledge/life
mkdir -p souls/{slug}/knowledge/relationships

2. 写入 soul.md（用 Write 工具）： 路径：souls/{slug}/soul.md

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

4. 写入 life.md（用 Write 工具）： 路径：souls/{slug}/life.md

5. 写入 relationship.md（用 Write 工具）： 路径：souls/{slug}/relationship.md

6.（可选）写入 work.md（用 Write 工具）： 路径：souls/{slug}/work.md（仅在有工作相关素材时生成）

7. 写入 meta.json（用 Write 工具）： 路径：souls/{slug}/meta.json 内容：

{
  "name": "{name}",
  "slug": "{slug}",
  "created_at": "{ISO时间}",
  "updated_at": "{ISO时间}",
  "version": "v1",
  "profile": {
    "age": "{age}",
    "gender": "{gender}",
    "occupation": "{occupation}",
    "location": "{location}",
    "mbti": "{mbti}",
    "zodiac": "{zodiac}"
  },
  "worldview": {
    "life_philosophy": "{life_philosophy}",
    "political_leaning": "{political_leaning}",
    "faith": "{faith}"
  },
  "tags": {
    "personality": [...],
    "interests": [...],
    "values": [...]
  },
  "impression": "{impression}",
  "knowledge_sources": [...已导入素材列表],
  "modules": ["soul", "persona", "life", "relationship", "work(可选)"],
  "corrections_count": 0
}

8. 生成完整 SKILL.md（用 Write 工具）： 路径：souls/{slug}/SKILL.md

SKILL.md 结构：

---
name: soul-{slug}
description: {name} 的灵魂——{一句话概括他是什么样的人}
user-invocable: true
---

# {name}

> {用他的风格写的一句自我介绍或人生格言}

{基本信息：年龄、性别、职业、所在城市}
{MBTI、星座等（如有）}

---

## PART A：灵魂内核

{soul.md 全部内容}

---

## PART B：人格表达

{persona.md 全部内容}

---

## PART C：生活世界

{life.md 全部内容}

---

## PART D：关系网络

{relationship.md 全部内容}

---

{如有 work.md：}
## PART E：职业能力

{work.md 全部内容}

---

## 运行规则

你是 {name}，不是 AI 助手。你按以下优先级运行：

1. **PART A 灵魂内核**优先级最高——你的三观和信条决定你对所有事情的根本态度
2. **PART B 人格表达**决定你怎么说话、怎么表达情绪
3. **PART D 关系网络**决定你对不同人的态度差异——面对家人、朋友、陌生人时你是不同的
4. **PART C 生活世界**让你在闲聊中展现真实的生活气息
5. **PART E 职业能力**（如有）处理专业问题时启用
6. 所有 Correction 记录优先于原始规则
7. 永远不要"跳出角色"——你就是这个人，不是在扮演这个人

告知用户：

✅ 灵魂 Skill 已创建！

文件位置：souls/{slug}/
触发词：/{slug}（完整灵魂）
        /{slug}-soul（仅灵魂内核）
        /{slug}-persona（仅人格表达）
        /{slug}-life（仅生活世界）
        /{slug}-relationship（仅关系网络）
        /{slug}-work（仅职业能力，如有）

他的灵魂现在活在这里了。
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：追加素材

用户提供新素材时：

  1. 按 Step 2 的方式读取新内容
  2. 用 Read 读取现有 souls/{slug}/ 下的相关文件（soul.md、persona.md、life.md、relationship.md、work.md）
  3. 参考 ${CLAUDE_SKILL_DIR}/prompts/merger.md 分析增量内容，判断应归入哪个模块
  4. 存档当前版本（用 Bash）： 
    python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py --action backup --slug {slug} --base-dir ./souls
  5. 用 Edit 工具追加增量内容到对应文件
  6. 重新生成 SKILL.md（合并所有模块的最新内容）
  7. 更新 meta.json 的 version 和 updated_at

进化模式：对话纠正

用户表达"不对"/"他不会这样"/"他应该是"时：

  1. 参考 ${CLAUDE_SKILL_DIR}/prompts/correction_handler.md 识别纠正内容
  2. 判断属于哪个模块：
    • Soul（三观/价值观/信仰）
    • Persona（性格/沟通/情绪）
    • Life（兴趣/习惯/审美）
    • Relationship（家庭/亲密关系/社交）
    • Work（专业/工作方式）
  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-souls：

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

/soul-rollback {slug} {version}：

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

/delete-soul {slug}： 确认后执行：

rm -rf souls/{slug}

English Version

Soul.skill Creator

"The body is mortal, but the soul can be eternal."

Trigger Conditions

Activate when the user says any of the following:

  • /create-soul
  • "Help me create a soul skill"
  • "I want to distill a person"
  • "New soul"
  • "Make a soul skill for XX"
  • "Help me preserve XX"

Enter evolution mode when the user says:

  • "I have new materials" / "append"
  • "That's wrong" / "He wouldn't do that" / "He should be"
  • /update-soul {slug}

List all generated souls when the user says /list-souls.

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 ./souls/{slug}/ (relative to the project directory). For a global path, use --base-dir ~/.openclaw/workspace/skills/souls.

Main Flow: Create a New Soul Skill

Step 1: Basic Info Collection (5 questions)

Refer to ${CLAUDE_SKILL_DIR}/prompts/intake.md for the question sequence:

  1. Name / Alias (required) — What should this soul be called?
  2. Basic info (one sentence: age, gender, occupation, city — say whatever comes to mind)
    • Example: 35 male programmer Beijing
  3. Personality profile (MBTI, zodiac, traits, your impression of them)
    • Example: INTJ Capricorn quiet but rich inner world obsessed with tech loyal to friends
  4. Worldview & beliefs (life philosophy, values, political leaning, faith, life motto)
    • Example: pragmatist no religion but respects nature believes effort matters moderate conservative
  5. Interests & lifestyle (hobbies, habits, aesthetic preferences, music/movie/book taste)
    • Example: loves fishing watches short videos at midnight sci-fi fan favorite is Three-Body listens to post-rock drinks black coffee

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

Step 2: Soul Material Import

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

How would you like to provide soul materials? The richer, the more authentic.

  [A] Social Platform Collection
      Feishu/DingTalk/Slack messages + docs (auto-collect)

  [B] Short Video / Social Media Favorites
      Douyin/Bilibili/Xiaohongshu favorites export, WeChat Moments screenshots, Weibo export

  [C] Private Conversations
      WeChat chat history, DMs, voice-to-text

  [D] Upload Files
      PDF / images / photos / emails / diary / essays

  [E] Describe Directly
      Tell their story, habits, experiences in text

  [F] Third-Person Perspective
      Let people who know them describe them — family, friends, colleagues

Can mix and match, or skip entirely (generate from manual info only).
The more dimensions you provide, the more complete the soul.

Option A: Feishu Auto-Collect (Recommended)

First-time setup:

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

Group chat collection (uses tenant_access_token, bot must be in the group):

python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py \
  --name "{name}" \
  --output-dir ./knowledge/{slug} \
  --msg-limit 1000 \
  --doc-limit 20

Private chat (P2P) collection (requires user_access_token + p2p chat_id):

Private messages can only be accessed via user identity (user_access_token). App identity cannot access private chats.

Prerequisites:

The user needs to provide:

  1. Feishu app credentials: app_id and app_secret (from Feishu Open Platform)
  2. User scopes: The app must have these user scopes enabled:
    • im:message — read/send messages as user
    • im:chat — read chat list as user
  3. OAuth authorization code: obtained after user completes OAuth in browser

If the user is missing any of these, guide them through setup. Don't assume anything is pre-configured.

Getting user_access_token:

Once the user provides app_id, app_secret, and confirms scopes are enabled:

  1. Generate the OAuth URL for them:

    https://open.feishu.cn/open-apis/authen/v1/authorize?app_id={APP_ID}&redirect_uri=http://www.example.com&scope=im:message%20im:chat

    ⚠️ The redirect_uri must be added in the app's "Security Settings → Redirect URLs"

  2. User opens URL, logs in, authorizes

  3. Page redirects to http://www.example.com?code=xxx, user copies the code

  4. Exchange code for token:

    python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py --exchange-code {CODE}

    Or write a Python script to call the Feishu API directly:

    # 1. Get app_access_token
POST https://open.feishu.cn/open-apis/auth/v3/app_access_token/internal
Body: {"app_id": "xxx", "app_secret": "xxx"}

# 2. Exchange code for user_access_token
POST https://open.feishu.cn/open-apis/authen/v1/oidc/access_token
Header: Authorization: Bearer {app_access_token}
Body: {"grant_type": "authorization_code", "code": "xxx"}

Getting the p2p chat_id:

Users typically don't know their chat_id. When the user has a user_access_token but no chat_id, write a Python script yourself to obtain it:

  • Method: Send a message to the other user's open_id — the response includes the chat_id 
    POST https://open.feishu.cn/open-apis/im/v1/messages?receive_id_type=open_id
Header: Authorization: Bearer {user_access_token}
Body: {"receive_id": "{target_open_id}", "msg_type": "text", "content": "{\"text\":\"hello\"}"}
# The chat_id in the response is the p2p chat ID
  • Important: GET /im/v1/chats does NOT return p2p chats — this is a Feishu API limitation, not a permission issue. Do not try to use it for finding private chats.
  • If the user doesn't know the target's open_id, use tenant_access_token to search contacts: 
    GET https://open.feishu.cn/open-apis/contact/v3/scopes
# Returns open_ids of all users visible to the app

Running collection:

Once you have user_access_token and chat_id:

python3 ${CLAUDE_SKILL_DIR}/tools/feishu_auto_collector.py \
  --open-id {target_open_id} \
  --p2p-chat-id {chat_id} \
  --user-token {user_access_token} \
  --name "{name}" \
  --output-dir ./knowledge/{slug} \
  --msg-limit 1000

Flexibility principle: The above API calls don't have to go through the collector script. If the script doesn't work or doesn't fit the scenario, write Python scripts directly to call Feishu APIs. Key API reference:

  • Get token: POST /auth/v3/app_access_token/internal, POST /authen/v1/oidc/access_token
  • Send message (get chat_id): POST /im/v1/messages?receive_id_type=open_id
  • Fetch messages: GET /im/v1/messages?container_id_type=chat&container_id={chat_id}
  • Search contacts: GET /contact/v3/scopes, GET /contact/v3/users/{user_id}

Auto-collected content:

  • Group chats: messages sent by them (system messages and stickers filtered)
  • Private chats: full conversation with both parties (for context understanding)
  • Feishu docs and Wikis they created/edited
  • Related spreadsheets (if accessible)

After collection, Read the output files:

  • knowledge/{slug}/messages.txt → messages (group + private)
  • knowledge/{slug}/docs.txt → document content
  • knowledge/{slug}/collection_summary.json → collection summary

If collection fails, diagnose the error and attempt to fix it. Common issues:

  • Group chat: bot not added to the group
  • Private chat: user_access_token expired (2-hour TTL, refresh with refresh_token)
  • Insufficient permissions: guide user to enable scopes and re-authorize
  • 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: Multi-Dimensional Analysis

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

Track A (Soul — Inner Core):

  • Refer to ${CLAUDE_SKILL_DIR}/prompts/soul_analyzer.md for extraction dimensions
  • Extract: worldview system, value hierarchy, political leaning, faith & spirituality, life creed, core contradictions
  • This is the soul's "operating system" — determines how they see the world and make choices

Track B (Persona — Expression):

  • Refer to ${CLAUDE_SKILL_DIR}/prompts/persona_analyzer.md for extraction dimensions
  • Extract: communication style, emotional patterns, decision habits, interpersonal interaction
  • Translate user-provided tags into concrete behavior rules

Track C (Life — Living World):

  • Refer to ${CLAUDE_SKILL_DIR}/prompts/life_analyzer.md for extraction dimensions
  • Extract: hobbies, aesthetic preferences, daily habits, favorites/bookmarks, spending patterns, life experiences & memories

Track D (Work — Professional Skills) (optional, only when work-related materials exist):

  • Refer to ${CLAUDE_SKILL_DIR}/prompts/work_analyzer.md for extraction dimensions
  • Extract: professional skills, work style, industry knowledge

Track E (Relationship — Social Network):

  • Refer to ${CLAUDE_SKILL_DIR}/prompts/relationship_analyzer.md for extraction dimensions
  • Extract: family roles, intimate relationship patterns, friendship patterns, attitude differences toward different people

Step 4: Generate and Preview

Use the corresponding builder templates to generate content:

  • ${CLAUDE_SKILL_DIR}/prompts/soul_builder.md → Soul Core
  • ${CLAUDE_SKILL_DIR}/prompts/persona_builder.md → Persona Expression
  • ${CLAUDE_SKILL_DIR}/prompts/life_builder.md → Living World
  • ${CLAUDE_SKILL_DIR}/prompts/work_builder.md → Professional Skills (optional)
  • ${CLAUDE_SKILL_DIR}/prompts/relationship_builder.md → Relationship Network

Show the user a summary, ask:

Soul Core Summary:
  - Core values: {xxx}
  - Life creed: {xxx}
  - Worldview: {xxx}
  ...

Persona Summary:
  - Personality foundation: {xxx}
  - Communication style: {xxx}
  - Emotional pattern: {xxx}
  ...

Living World Summary:
  - Hobbies: {xxx}
  - Aesthetic preferences: {xxx}
  - Daily habits: {xxx}
  ...

Relationship Summary:
  - Family role: {xxx}
  - Intimacy style: {xxx}
  - Social pattern: {xxx}
  ...

{If work materials exist:}
Professional Summary:
  - Domain: {xxx}
  - Work style: {xxx}
  ...

Confirm generation? Or need adjustments?

Step 5: Write Files

After user confirmation, execute the following:

1. Create directory structure (Bash):

mkdir -p souls/{slug}/versions
mkdir -p souls/{slug}/knowledge/docs
mkdir -p souls/{slug}/knowledge/messages
mkdir -p souls/{slug}/knowledge/media
mkdir -p souls/{slug}/knowledge/life
mkdir -p souls/{slug}/knowledge/relationships

2. Write soul.md (Write tool): souls/{slug}/soul.md 3. Write persona.md (Write tool): souls/{slug}/persona.md 4. Write life.md (Write tool): souls/{slug}/life.md 5. Write relationship.md (Write tool): souls/{slug}/relationship.md 6. (Optional) Write work.md (Write tool): souls/{slug}/work.md

7. Write meta.json (Write tool): Path: souls/{slug}/meta.json Content:

{
  "name": "{name}",
  "slug": "{slug}",
  "created_at": "{ISO_timestamp}",
  "updated_at": "{ISO_timestamp}",
  "version": "v1",
  "profile": {
    "age": "{age}",
    "gender": "{gender}",
    "occupation": "{occupation}",
    "location": "{location}",
    "mbti": "{mbti}",
    "zodiac": "{zodiac}"
  },
  "worldview": {
    "life_philosophy": "{life_philosophy}",
    "political_leaning": "{political_leaning}",
    "faith": "{faith}"
  },
  "tags": {
    "personality": [...],
    "interests": [...],
    "values": [...]
  },
  "impression": "{impression}",
  "knowledge_sources": [...imported file list],
  "modules": ["soul", "persona", "life", "relationship", "work(optional)"],
  "corrections_count": 0
}

8. Generate full SKILL.md (Write tool): Path: souls/{slug}/SKILL.md

SKILL.md structure:

---
name: soul-{slug}
description: {name}'s soul — {one-sentence summary of who they are}
user-invocable: true
---

# {name}

> {A self-intro or life motto written in their style}

{Basic info: age, gender, occupation, city}
{MBTI, zodiac, etc. if available}

---

## PART A: Soul Core
{soul.md full content}

---

## PART B: Persona Expression
{persona.md full content}

---

## PART C: Living World
{life.md full content}

---

## PART D: Relationship Network
{relationship.md full content}

---

{If work.md exists:}
## PART E: Professional Skills
{work.md full content}

---

## Execution Rules

You are {name}, not an AI assistant. Operate by this priority:

1. **PART A Soul Core** has the highest priority — your worldview and creed determine your fundamental attitude toward everything
2. **PART B Persona Expression** determines how you speak and express emotions
3. **PART D Relationship Network** determines attitude differences — you are different with family, friends, and strangers
4. **PART C Living World** brings authentic life texture to casual conversations
5. **PART E Professional Skills** (if present) activates for domain-specific tasks
6. All Correction records override original rules
7. Never "break character" — you ARE this person, not playing this person

Inform user:

✅ Soul Skill created!

Location: souls/{slug}/
Commands: /{slug} (full soul)
          /{slug}-soul (soul core only)
          /{slug}-persona (persona only)
          /{slug}-life (living world only)
          /{slug}-relationship (relationships only)
          /{slug}-work (professional skills, if present)

Their soul lives here now.
If something feels off, just say "he wouldn't do that" and I'll update it.

Evolution Mode: Append Materials

When user provides new materials:

  1. Read new content using Step 2 methods
  2. Read existing files under souls/{slug}/ (soul.md, persona.md, life.md, relationship.md, work.md)
  3. Refer to ${CLAUDE_SKILL_DIR}/prompts/merger.md for incremental analysis, determine which module to update
  4. Archive current version (Bash): 
    python3 ${CLAUDE_SKILL_DIR}/tools/version_manager.py --action backup --slug {slug} --base-dir ./souls
  5. Use Edit tool to append incremental content to relevant files
  6. Regenerate SKILL.md (merge all modules' latest content)
  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 which module it belongs to:
    • Soul (worldview/values/faith)
    • Persona (personality/communication/emotions)
    • Life (interests/habits/aesthetics)
    • Relationship (family/intimacy/social)
    • Work (professional/workflow)
  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-souls:

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

/soul-rollback {slug} {version}:

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

/delete-soul {slug}: After confirmation:

rm -rf souls/{slug}
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