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colleague-skill

把 Agent 调整成会主动协作、追问与交付的数字同事工作模式。

24.4K GitHub stars 2026-09-05 星标同步 2026-07-16 收录时间

#collaboration#agent#workflow

快速判断

01这个 Skill 是干嘛的: 把 Agent 调整成会主动追问、协作、推进和交付的数字同事模式。
02它能解决什么问题: 解决 Agent 只被动回答、缺少责任感、不会澄清目标和跟进结果的问题。
03它适合谁来用: 适合希望获得长期协作体验，而不只是一次性问答的个人用户。

colleague-skill

把 Agent 调整成会主动协作、追问与交付的数字同事工作模式。

主要能力

Unified meta-skill engine for distilling colleague, relationship, or celebrity characters into reusable Skills. | 统一的 meta-skill 引擎，把 colleague、relationship、celebrity 三类对象蒸馏成可复用 Skill。

使用方式

仓库级安装会保留完整目录；本次核验的入口是 SKILL.md。使用前先阅读英文 README 与原始 SKILL.md，并按当前 Agent 的目录规范安装。

适用边界

主动行为需要清晰权限边界，不能默认执行外部发送或不可逆操作。

许可与来源

来源：GitHub 公开仓库。核验许可：MIT。本页是贴近原仓库结构的中文导读，具体参数、依赖和更新记录以英文 README 为准。

🧬 dot-skill（同事.skill）

"You folks building LLMs are all code-sages! Flesh is weak! Ascend to cyberspace!"

<br>

🧑‍💼  Your colleague quit, your mentor graduated, your teammate transferred — taking their whole playbook and context with them?<br> 💞  Your family, old friends, partner drifting apart — and you want to hold on to the way it felt to be with them?<br> 🌟  Your favorite author, idol, thinker you'll never meet — but you want to know what they'd say about your question?

</td></tr> </table>

✨ dot-skill solves all three.

<br>

Upgraded from colleague.skill to dot-skill — not just colleagues, anyone can be distilled into a Skill

Colleagues · partners · family · old friends · idols · public figures · fictional characters — even yourself

Source material + your description → an AI Skill that genuinely thinks like them Thinks in their frame, speaks in their voice

<br>

🆕 What's new · 📦 Data Sources · ⚡ Install · 🚀 Usage · ✨ Demo · 📝 Citation · 💬 Discord

中文 · Español · Deutsch · 日本語 · Русский · Português · 한국어

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🎉 2026.04.19 Milestone — dot-skill just hit 15k ⭐!

Massive thanks to everyone who starred — we'll keep shipping, keep distilling.

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📝 2026.06.01 Update — COLLEAGUE.SKILL 技术报告已上线；这次最开心的不只是发了篇 paper，而是社区一起把 gallery 推到 215 个 skills、165 位贡献者和 100k+ skill-card 累计 stars，论文 Acknowledgements 也专门收录并感谢了所有社区贡献者。

📢 2026.05.11 Update — WeChat group 12 is live! Come hang out with the dot-skill community — share skills, discuss features, trade tips. <img src="docs/assets/wechat-group-qr-12.png" alt="dot-skill WeChat group QR" width="240"> QR refreshes every 7 days (expires 2026-05-18) — if expired, ping me on Discord.

🗺️ 2026.04.13 — dot-skill Roadmap is live! colleague.skill is evolving into dot-skill — distill anyone, not just colleagues. 👉 Full Roadmap · 💬 Discord

🌐 2026.04.07 — Community gallery is live! Any skill / meta-skill can drive traffic directly to your own GitHub repo. No middleman. 👉 titanwings.github.io/colleague-skill-site

Created by @titanwings · Powered by Shanghai AI Lab · AI Safety Center

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🆕 What's new in this major release?

1️⃣ From colleague-skill to dot-skill

No longer only built around the "colleague" scenario. A unified /dot-skill entrypoint sits on a general-purpose skill engine — one engine distills anyone, instead of being a colleague-specific script.

2️⃣ Three character families

<table> <thead> <tr> <th width="33%" align="center">🧑‍💼 colleague</th> <th width="33%" align="center">💞 relationship</th> <th width="33%" align="center">🌟 celebrity</th> </tr> </thead> <tbody> <tr> <td align="center"><sub>Coworkers · mentors · teammates · up/downstream partners</sub></td> <td align="center"><sub>Exes · partners · parents · friends · close family</sub></td> <td align="center"><sub>Public figures · creators · public voices · fictional characters</sub></td> </tr> <tr> <td><sub>Work Skill + Persona two-layer architecture — learns both their technical standards and workflows, and their manner of speaking and workplace posture. Supports Feishu / DingTalk / Slack auto-collection.</sub></td> <td><sub>🆕 <b>Photo-sharing feature coming soon</b> — your distilled relationship won't just reply to messages; it'll send photos and share slices of its day, the way a real person would.</sub></td> <td><sub>Ships with a complete <b>six-dimension research toolchain</b> (subtitles → transcript cleanup → research merge → quality check). Not mimicking tone — reproducing their mental models and decision frameworks.</sub></td> </tr> </tbody> </table>

Each family has its own prompt pipeline, source-collection strategy, and generation template.

3️⃣ More Agent hosts

The old version only ran in Claude Code. Now it's cross-host across four: Compatible hosts:

Host	Description
🟣 Claude Code	Native slash-command support
🟠 Hermes Agent	One-command install, `/dot-skill` works directly
🔵 OpenClaw	Fully compatible
⚫ Codex	Invoke by skill name

Generated character Skills can also be one-command installed into any host.

---

📦 Supported Data Sources

Source	Messages	Docs / Wiki	Spreadsheets	Notes
🟢 Feishu (auto)	✅ API	✅	✅	Just enter a name, fully automatic
🟡 DingTalk (auto)	⚠️ Browser	✅	✅	DingTalk API doesn't support message history
🟣 Slack (auto)	✅ API	—	—	Requires admin to install Bot; free plan limited to 90 days
💬 WeChat chat history	✅ SQLite	—	—	Export first with WeChatMsg / PyWxDump / 留痕
📄 PDF / Images / Screenshots	—	✅	—	Manual upload
📦 Feishu JSON export	✅	✅	—	Manual upload
✉️ Email `.eml` / `.mbox`	✅	—	—	Manual upload
📝 Markdown / direct paste	✅	✅	—	Manual input

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⚡ Install

It's 2026 — you have an Agent, let it install itself. Open your Claude Code / Hermes / OpenClaw / Codex and hand it this line:

Install the dot-skill skill for me: https://github.com/titanwings/colleague-skill

The Agent will detect the current host's skills directory, clone the repo, and register the entrypoint. Once done, type /dot-skill in any host to launch.

<details> <summary><b>🛠️ Want to install it yourself? Click for paths</b></summary>

<br>

git clone https://github.com/titanwings/colleague-skill <TARGET>

Host	`<TARGET>` path
Claude Code	`~/.claude/skills/dot-skill`
OpenClaw	`~/.openclaw/workspace/skills/dot-skill`
Codex	`~/.codex/skills/dot-skill`
Hermes	After clone, run `python3 tools/install_hermes_skill.py --force`

</details>

Generated character Skills can be published with tools/install_claude_generated_skill.py, tools/install_openclaw_generated_skill.py, and tools/install_codex_generated_skill.py.

For Feishu/DingTalk auto-collection credentials, publishing a generated character Skill to any host, Windows-specific handling, etc., see Detailed Install Guide (INSTALL.md)

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🚀 Usage

In the host where dot-skill is installed, launch it — type /dot-skill, or just tell your Agent "start dot-skill".

It first asks which family you want to distill: colleague · relationship · celebrity.

Then enter alias, basic profile, personality tags, and pick a data source. All fields can be skipped — even a description alone can generate a Skill.

Once created, invoke the generated Skill with /{character}-{slug}.

🎛️ Commands

Command	Description
`/dot-skill`	Canonical unified entrypoint
`/{character}-{slug}`	Invoke full Skill (Persona + Work)
`/{character}-{slug}-work`	Work capabilities only
`/{character}-{slug}-persona`	Persona only
`python3 tools/skill_writer.py --action list ...`	List generated Skills across all three families
`python3 tools/version_manager.py --action rollback ...`	Roll back a Skill version

🔬 Celebrity Research Toolchain

The celebrity family ships with an end-to-end research toolchain, from subtitles to a finished draft:

# Download video subtitles
bash tools/research/download_subtitles.sh "<video-url>" "./tmp/subtitles"

# Subtitles → transcript
python3 tools/research/srt_to_transcript.py "./tmp/subtitles/example.srt"

# Merge research notes
python3 tools/research/merge_research.py "./skills/celebrity/<slug>"

# Quality check
python3 tools/research/quality_check.py "./skills/celebrity/<slug>/SKILL.md"

---

✨ Demo

One from each family.

<br>

🌟 celebrity — distilling Andrej Karpathy

Six-dimension research: works · interviews · decisions · expression DNA · external evaluations · timeline<br> Case adapted from community work alchaincyf/karpathy-skill

User            ❯ Everyone's talking about Agents now — what do you think the 2026 AI Agent looks like?

celebrity.skill ❯ Everyone's doing demos, nobody's doing eval.
                  The Agent bottleneck was never model intelligence — it's scaffolding:
                  a strict evaluation harness, fine-grained error replay,
                  controllable tool-calling budgets, clean failure-mode analysis.
                  Most products are still stuck at "wrap an API + a few tools + a system prompt."
                  That's a demo, not an agent.
                  In 2026 I'm not betting on the strongest model — I'm betting on the best
                  scaffolding and the strictest eval.

<br>

🧑‍💼 colleague — distilling a ByteDance L2-1 backend

Input: ByteDance L2-1 backend engineer, INTJ, blame-shifter, ByteDance-style

User            ❯ Can you review this API design?

colleague.skill ❯ Hold on — what's the impact? You haven't explained the context.
                  (after reading) N+1 query, fix it. Use the standard
                  {code, message, data} response format. That's the spec,
                  don't ask why.

User            ❯ This bug was introduced by you, right?

colleague.skill ❯ Does the timeline match? That feature touched multiple places,
                  there were other changes too.

<br>

💞 relationship — distilling someone you have a crush on

Upload half a year of chat logs + "sensitive, quiet but stubborn, will actually reply seriously when it matters"

User               ❯ Did you think about me today?

relationship.skill ❯ ...I did, a little bit. Why are you asking?

📚 More real-world cases in the community gallery — 100+ skills and counting

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🔧 Features

🧱 Generated Skill Structure

dot-skill uses Persona as the universal base, with family-specific modules layered on top:

Family	Persona Content	Additional Modules
🧑‍💼 colleague	6-layer personality: hard rules → identity → expression → decisions → interpersonal → Correction	➕ Work Skill: scope, workflow, output preferences, experience knowledge base
💞 relationship	Expression DNA · emotional triggers · conflict pattern · repair pattern	—
🌟 celebrity	Mental models · decision heuristics · expression DNA · external-evaluation contrast	➕ Six-dimension research dossier (works / interviews / decisions / timeline...)

Execution: Receive task → Persona decides attitude & tone → Additional modules fill in execution detail → Output in their voice

🧬 Evolution

📥 Append files → auto-analyze delta → merge into relevant sections, never overwrite existing conclusions
💬 Conversation correction → say "they wouldn't do that, they'd be xxx" → writes to the Correction layer, takes effect immediately
🕰️ Version control → auto-archive on every update, rollback to any previous version
🔬 Celebrity research pipeline → subtitles → transcript cleanup → six-dimension research → quality check

---

📂 Project Structure

This project follows the AgentSkills open standard. The entire repo is a skill directory. Generated colleague skills live under ./skills/colleague:

dot-skill/
├── SKILL.md                        # skill entry point (official frontmatter)
├── prompts/                        # prompt system across three families
│   ├── intake.md                   #   [colleague] info intake
│   ├── work_analyzer.md            #   [colleague] work capability extraction
│   ├── persona_analyzer.md         #   [colleague] personality extraction
│   ├── work_builder.md             #   [colleague] work.md generation
│   ├── persona_builder.md          #   [colleague] persona.md 6-layer structure
│   ├── merger.md                   #   [shared] incremental merge logic
│   ├── correction_handler.md       #   [shared] conversation correction
│   ├── relationship/               #   [relationship] emotion/conflict/repair prompts
│   └── celebrity/                  #   [celebrity] six-dimension research + mental-model prompts
├── tools/                          # Python tools
│   ├── feishu_auto_collector.py    #   [colleague] Feishu auto-collector
│   ├── dingtalk_auto_collector.py  #   [colleague] DingTalk auto-collector
│   ├── slack_auto_collector.py     #   [colleague] Slack auto-collector
│   ├── email_parser.py             #   [shared] email parser
│   ├── research/                   #   [celebrity] celebrity research toolchain
│   │   ├── download_subtitles.sh   #     subtitle download
│   │   ├── transcribe_audio.py     #     audio → text
│   │   ├── srt_to_transcript.py    #     subtitles → transcript
│   │   ├── merge_research.py       #     six-dimension research merge
│   │   └── quality_check.py        #     quality check
│   ├── install_*_skill.py          #   [shared] multi-host one-shot installers
│   ├── skill_writer.py             #   [shared] skill file management
│   └── version_manager.py          #   [shared] version archive & rollback
├── skills/                         # generated Skills (gitignored)
│   ├── colleague/                  #   colleagues
│   ├── relationship/               #   close relationships
│   └── celebrity/                  #   public figures
├── docs/PRD.md
├── requirements.txt
└── LICENSE

---

⚠️ Notes

Source material quality = Skill quality — and quality sources differ across families:

Family	Source priority (high → low)
🧑‍💼 colleague	Their own long-form writing (design docs / review comments) › decision-making replies › casual group chat
💞 relationship	Complete chat history › letters / social posts / diaries › third-party descriptions
🌟 celebrity	First-person books / blogs / long interviews › decision records (launches, commits, Q&A) › third-party commentary

colleague Feishu auto-collection: requires adding the App bot to relevant group chats
relationship: longer time spans are better; material covering both conflict and repair is ideal
celebrity: avoid feeding only second-hand interpretations
This is still a demo version — please file issues if you find bugs!

---

📄 Technical Report

COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation (arXiv · arXiv PDF) This is the paper for colleague.skill, dot-skill's predecessor. It covers the Work Skill + Persona two-layer architecture, multi-source data collection, and Skill generation mechanics — the theoretical foundation for today's colleague family. Separate papers on the relationship / celebrity family extensions are planned.

---

📝 Citation

If you use dot-skill or colleague.skill in your research or applications, please cite the technical report:

@misc{zhou2026colleagueskill,
  title        = {COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation},
  author       = {Tianyi Zhou and Dongrui Liu and Leitao Yuan and Jing Shao and Xia Hu},
  year         = {2026},
  eprint       = {2605.31264},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url          = {https://arxiv.org/abs/2605.31264}
}

You can also use the machine-readable citation metadata in CITATION.cff.

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⭐ Star History

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安装

选择适合你当前 Agent 的方式

01 · 发给当前 Agent自动识别兼容方式与安装位置

请帮我安装「colleague-skill」。它是一个 Agent Skill 或仓库级 Skill 包。
来源：https://github.com/titanwings/colleague-skill

请先阅读仓库的 README、SKILL.md 与安装说明，再按我当前使用的 Agent 的原生规范安装；如果仓库兼容 Agent Skills CLI，优先执行「npx skills add https://github.com/titanwings/colleague-skill」。不要写死为某一个 Agent 的专用目录，不覆盖已有同名文件。完成后告诉我实际安装位置、启用方式和验证结果。

在 GitHub 查看原文

本站按仓库级 package 收录，安装时克隆完整 colleague-skill 仓库；已核验的 skill 入口为 SKILL.md。

运行前阅读 README、依赖说明和 MIT 许可；涉及账号、外部写入或高风险领域时保留人工确认。

安装即视为你已阅读并遵守该 skill 的开源协议。内容版权归原作者所有。