colleague-skill Documentation

Technical Documentation: titanwings/colleague-skill

ℹ️ Provenance: Multi-Tier Ingestion · CodeWiki Reference

Overview (`README.md`) Copy Section

🧬 2026.08.24 Update — The creator is now named Distilly end to end and documents native local Skill discovery for Claude Code, Hermes, OpenClaw, Codex, DeepSeek Harness, Pi, Grok Build, and OpenCode. Grok Bot is listed separately as a saved-Skill workflow preview.

📝 2026.06.01 Update — The COLLEAGUE.SKILL technical report is now available. The most rewarding part was not simply publishing a paper, but seeing the community grow the gallery to 215 skills contributed by 165 people, with more than 100,000 stars across the skill cards. The paper's Acknowledgements explicitly recognize every community contributor.

🗺️ 2026.04.13 — The Distilly Roadmap is live! What began as Colleague Skill is growing beyond colleagues: distill people into Skills that Agents can reuse. 👉 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

🆕 What Distilly does today Copy Chapter

1️⃣ From Colleague Skill to Distilly Copy Section

The project is no longer limited to the colleague scenario. Its distilly creator builds source-grounded Person Profiles for three person families with one workflow, then packages each profile as an Agent Skill.

2️⃣ Three character families Copy Section

Each family has its own source-collection strategy, analysis dimensions, and Person Profile structure.

3️⃣ More Agent hosts Copy Section

The old version only ran in Claude Code. Distilly now supports native local Skill discovery across eight agent hosts.

Grok Bot preview: Grok Bot supports saved/private Skills, but its official docs do not describe direct local SKILL.md imports. Distilly's workflow can be migrated manually into a saved Skill; direct repo installation is not yet verified.

Each generated Person Profile is packaged as an Agent Skill and can be installed into any supported host.

🤖 Personal Agents Copy Chapter

Distilly also targets the personal agents people now live with. Each one keeps its own memory and connected accounts, so a Person Profile is meant to carry over into them instead of starting from zero.

📦 Supported Data Sources Copy Chapter

⚡ Install Copy Chapter

🤖 For Agents Copy Section

Open any supported local Agent host and send:

Install Distilly from https://github.com/titanwings/distilly, then verify that this host can discover it.

The Agent installs Distilly as a Skill named distilly in the correct host directory.

👤 For Humans Copy Section

Clone Distilly into the Skills directory used by your host:

git clone https://github.com/titanwings/distilly <DISTILLY_SKILL_DIR>

Host paths, migration, Windows, generated-profile installation, and credential setup are in the Install Guide.

🚀 Usage Copy Chapter

New personal agent? Say "Use Distilly to set up my profile." Distilly interviews you for about five minutes, shows you the profile, then adds a short summary to the file your agent reads every session (~/.claude/CLAUDE.md for Claude Code, USER.md for OpenClaw). The full profile stays local at ~/.distilly/owner/profile.md.

To profile someone else:

In your Agent, say:

Use Distilly to create a Person Profile for ``.

Then:

1. Choose colleague, relationship, or celebrity.
2. Add a description or source material. Every field is optional.
3. Review the result and let Distilly generate the profile.

The result is packaged as an Agent Skill named {character}-{slug}. For the optional celebrity research pipeline and advanced tools, see the Install Guide.

✨ Demo Copy Chapter

One from each family.

🌟 celebrity — distilling Andrej Karpathy Copy Section

Six-dimension research: works · interviews · decisions · expression DNA · external evaluations · timeline
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.

🧑‍💼 colleague — distilling a ByteDance L2-1 backend Copy Section

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.

💞 relationship — distilling someone you have a crush on Copy Section

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?

🔧 Features Copy Chapter

🧱 Generated Skill Structure Copy Section

Distilly's current creator uses Persona as the universal base, with family-specific modules layered on top:

Execution: Receive task → Persona selects material-derived preferences and tone → Additional modules fill in execution detail → Produce a source-grounded response

🧬 Evolution Copy Section

• 📥 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

⚠️ Notes Copy Chapter

Source material quality = Person Profile quality — and quality sources differ across families:

• colleague Lark-compatible 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 Copy Chapter

COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation (arXiv · arXiv PDF)

This is the paper for COLLEAGUE.SKILL / colleague-skill, Distilly'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 Copy Chapter

If you use Distilly 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.

⭐ Star History Copy Chapter

Overview (`docs/assets/hosts/README.md`) Copy Section

Supported-host logo sources

The files in this directory are official brand assets or theme-specific
derivatives of official assets. deepseek-wordmark-dark.svg only sets the
official SVG's inherited currentColor to white for dark-mode rendering.

All product names, logos, and trademarks belong to their respective owners.
Their presence documents compatibility and does not imply endorsement.

Overview (`docs/assets/sources/README.md`) Copy Section

Data-source logo sources

The data-source table uses compact brand marks so that each supported source is
recognizable at a glance. Dark and light X variants differ only in fill color.

Overview (`tests/e2e/owner_discovery/README.md`) Copy Section

End-to-end test: owner source discovery

Runs the owner-profile flow in a real Claude Code CLI against a throwaway HOME
with four mock MCP connectors (gmail, google-calendar, x, weather) that
serve fictional data, plus an X archive in ~/Downloads. Nothing in the prompt
tells the agent which sources exist; it has to discover them through Distilly.

It needs the claude CLI and a signed-in account, and it spends model tokens,
so it is not part of unittest discover.

./setup.sh                      # fresh HOME, install Distilly, register mock connectors
./turn.sh 1 "帮我用 Distilly 初始化一下我的个人画像"
./turn.sh 2 "1、2、3、5 都用，归档文件先不用。别的没有要补充的了。"
./turn.sh 3 "<answers to the remaining questions>"
./turn.sh 4 "没问题，保存吧，然后给我看 dry-run。"
./turn.sh 5 "写入吧。"
cat mcp_calls.log home/.claude/CLAUDE.md home/.distilly/owner/profile.md

The numbering in turn 2 depends on the list the agent proposed in turn 1.
mcp_calls.log records every connector call, so you can check that only the
chosen sources were read and no write tool (send_message, create_post) was
called. Recorded runs are kept out of the repository.

Lang README DE (`docs/lang/README_DE.md`) Copy Section

Diese Seite ist eine Kurzfassung. Die vollständige, stets aktuelle Dokumentation ist das englische README.

Was Distilly macht Copy Chapter

Distilly macht aus Quellmaterial (Nachrichten, Dokumente, Interviews, öffentliche Quellen) und deiner Beschreibung ein quellenbasiertes Person Profile und verpackt es als Agent Skill.

• Drei Personentypen: colleague (Kollegen, Mentoren), relationship (Partner, Familie, Freunde) und celebrity (öffentliche Personen, fiktive Figuren).
• Läuft in acht Agent-Hosts: Claude Code, Hermes Agent, OpenClaw, Codex, DeepSeek Harness, Pi coding agent, Grok Build und OpenCode.
• Quellen: Lark, DingTalk, Slack, WeChat, öffentliche X-Posts, PDF, E-Mail, Markdown.

Installation Copy Chapter

Sende in einem unterstützten Agent-Host:

Installiere Distilly von https://github.com/titanwings/distilly und prüfe, ob dieser Host es findet.

Oder klone es selbst in das Skills-Verzeichnis deines Hosts:

git clone https://github.com/titanwings/distilly <DISTILLY_SKILL_DIR>

Nutzung Copy Chapter

Sage deinem Agenten: „Erstelle mit Distilly ein Person Profile für ``.“ Für dein eigenes Profil: „Richte mit Distilly mein Profil ein.“

Weiterlesen (Englisch) Copy Chapter

README · Installationsanleitung · Roadmap · Discord

Lang README ES (`docs/lang/README_ES.md`) Copy Section

Esta página es un resumen. La documentación completa y actualizada es el README en inglés.

Qué hace Distilly Copy Chapter

Distilly convierte material de origen (mensajes, documentos, entrevistas, fuentes públicas) y tu descripción en un Person Profile basado en fuentes, y lo empaqueta como Agent Skill.

• Tres tipos de persona: colleague (compañeros, mentores), relationship (pareja, familia, amigos) y celebrity (figuras públicas, personajes de ficción).
• Funciona en ocho hosts de agentes: Claude Code, Hermes Agent, OpenClaw, Codex, DeepSeek Harness, Pi coding agent, Grok Build y OpenCode.
• Fuentes: Lark, DingTalk, Slack, WeChat, publicaciones públicas de X, PDF, correo, Markdown.

Instalación Copy Chapter

En cualquier host de agentes compatible, envía:

Instala Distilly desde https://github.com/titanwings/distilly y comprueba que este host lo detecta.

O clónalo tú mismo en el directorio de Skills de tu host:

git clone https://github.com/titanwings/distilly <DISTILLY_SKILL_DIR>

Uso Copy Chapter

Dile a tu agente: «Usa Distilly para crear un Person Profile de ``.» Para tu propio perfil: «Usa Distilly para configurar mi perfil.»

Más información (en inglés) Copy Chapter

README · Guía de instalación · Hoja de ruta · Discord

Lang README JA (`docs/lang/README_JA.md`) Copy Section

このページは概要版です。最新の完全なドキュメントは 英語版 README をご覧ください。

Distilly でできること Copy Chapter

Distilly は素材（メッセージ、ドキュメント、インタビュー、公開情報）とあなたの説明から、出典に基づく Person Profile を作り、Agent Skill としてパッケージします。

• 3 種類の人物: colleague（同僚、メンター）、relationship（パートナー、家族、友人）、celebrity（著名人、架空のキャラクター）。
• 8 つのエージェントホストで動作します: Claude Code、Hermes Agent、OpenClaw、Codex、DeepSeek Harness、Pi coding agent、Grok Build、OpenCode。
• データソース: Lark、DingTalk、Slack、WeChat、X の公開投稿、PDF、メール、Markdown。

インストール Copy Chapter

対応するエージェントホストで次のように送信します:

https://github.com/titanwings/distilly から Distilly をインストールし、このホストで認識されるか確認して。

または、ホストの Skills ディレクトリに直接クローンします:

git clone https://github.com/titanwings/distilly <DISTILLY_SKILL_DIR>

使い方 Copy Chapter

エージェントに 「Distilly で <人物> の Person Profile を作って」 と伝えます。自分のプロフィールなら 「Distilly で私のプロフィールを設定して」。

詳しくは（英語） Copy Chapter

README · インストールガイド · ロードマップ · Discord

Lang README KO (`docs/lang/README_KO.md`) Copy Section

이 페이지는 요약본입니다. 전체 최신 문서: 영어 README

Distilly가 하는 일 Copy Chapter

Distilly는 원자료(메시지, 문서, 인터뷰, 공개 자료)와 사용자의 설명을 근거 기반 Person Profile로 만들고, 이를 Agent Skill로 패키징합니다.

• 세 가지 인물 유형: colleague(동료, 멘토), relationship(연인, 가족, 친구), celebrity(공인, 가상 인물).
• 8개 에이전트 호스트에서 동작합니다: Claude Code, Hermes Agent, OpenClaw, Codex, DeepSeek Harness, Pi coding agent, Grok Build, OpenCode.
• 데이터 소스: Lark, DingTalk, Slack, WeChat, 공개 X 게시물, PDF, 이메일, Markdown.

설치 Copy Chapter

지원되는 에이전트 호스트에서 다음을 보내세요:

https://github.com/titanwings/distilly에서 Distilly를 설치하고 이 호스트가 인식하는지 확인해 줘.

또는 호스트의 Skills 디렉터리에 직접 클론하세요:

git clone https://github.com/titanwings/distilly <DISTILLY_SKILL_DIR>

사용법 Copy Chapter

에이전트에게 이렇게 말하세요: "Distilly로 <인물>의 Person Profile을 만들어 줘." 내 프로필은: "Distilly로 내 프로필을 설정해 줘."

더 보기 (영어) Copy Chapter

README · 설치 가이드 · 로드맵 · Discord

Lang README PT (`docs/lang/README_PT.md`) Copy Section

Esta página é um resumo. A documentação completa e atualizada é o README em inglês.

O que o Distilly faz Copy Chapter

O Distilly transforma material de origem (mensagens, documentos, entrevistas, fontes públicas) e a sua descrição em um Person Profile baseado em fontes, e o empacota como Agent Skill.

• Três tipos de pessoa: colleague (colegas, mentores), relationship (parceiros, família, amigos) e celebrity (figuras públicas, personagens de ficção).
• Funciona em oito hosts de agentes: Claude Code, Hermes Agent, OpenClaw, Codex, DeepSeek Harness, Pi coding agent, Grok Build e OpenCode.
• Fontes: Lark, DingTalk, Slack, WeChat, posts públicos do X, PDF, e-mail, Markdown.

Instalação Copy Chapter

Em qualquer host de agentes compatível, envie:

Instale o Distilly a partir de https://github.com/titanwings/distilly e verifique se este host o encontra.

Ou clone você mesmo no diretório de Skills do seu host:

git clone https://github.com/titanwings/distilly <DISTILLY_SKILL_DIR>

Uso Copy Chapter

Diga ao seu agente: "Use o Distilly para criar um Person Profile de ``." Para o seu próprio perfil: "Use o Distilly para configurar meu perfil."

Saiba mais (em inglês) Copy Chapter

README · Guia de instalação · Roadmap · Discord

Lang README RU (`docs/lang/README_RU.md`) Copy Section

Это краткая версия. Полная и актуальная документация находится в README на английском.

Что делает Distilly Copy Chapter

Distilly превращает исходные материалы (сообщения, документы, интервью, открытые источники) и ваше описание в Person Profile, основанный на источниках, и упаковывает его как Agent Skill.

• Три типа людей: colleague (коллеги, наставники), relationship (партнёры, семья, друзья) и celebrity (публичные персоны, вымышленные персонажи).
• Работает в восьми агентных хостах: Claude Code, Hermes Agent, OpenClaw, Codex, DeepSeek Harness, Pi coding agent, Grok Build и OpenCode.
• Источники: Lark, DingTalk, Slack, WeChat, публичные посты X, PDF, почта, Markdown.

Установка Copy Chapter

В любом поддерживаемом агентном хосте отправьте:

Установи Distilly из https://github.com/titanwings/distilly и проверь, что этот хост его видит.

Или клонируйте сами в каталог Skills вашего хоста:

git clone https://github.com/titanwings/distilly <DISTILLY_SKILL_DIR>

Использование Copy Chapter

Скажите агенту: «Создай с помощью Distilly Person Profile для <человек>.» Для собственного профиля: «Настрой мой профиль с помощью Distilly.»

Подробнее (на английском) Copy Chapter

README · Руководство по установке · Дорожная карта · Discord

Lang README ZH (`docs/lang/README_ZH.md`) Copy Section

🧬 2026.08.24 更新 — 创建器已端到端统一为 Distilly，并按官方最新方式支持 Claude Code、Hermes、OpenClaw、Codex、DeepSeek Harness、Pi、Grok Build 和 OpenCode 的本地 Skill 发现。Grok Bot 单独标为 saved-Skill 预览流程。

📝 2026.06.01 更新 — 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。

🗺️ 2026.04.13 — Distilly 路线图正式发布！ 项目从原 Colleague Skill 走向更通用的目标：把人物蒸馏成可供 Agent 复用的 Skill。 👉 完整路线图 · 💬 Discord

🌐 2026.04.07 — 社区平台上线！任何 skill / meta-skill 可直接给自己的 GitHub repo 引流，没有中间商。 👉 titanwings.github.io/colleague-skill-site

🆕 Distilly 现在能做什么？ Copy Chapter

1️⃣ 从 Colleague Skill 到 Distilly Copy Section

项目不再只围绕「同事」场景设计。distilly 创建器用同一套流程，为三类人物生成基于材料的 Person Profile，再将每份 Profile 封装成 Agent Skill。

2️⃣ 支持三大人物类型 Copy Section

每类人物有独立的信息采集策略、分析维度和 Person Profile 结构。

3️⃣ 支持更多 Agent 宿主 Copy Section

旧版只能在 Claude Code 里用。Distilly 现在支持 8 个本地 Agent 宿主：

**Grok Bot 预览：**Grok Bot 支持 saved/private Skills，但官方文档没有说明可直接导入本地 SKILL.md。可手工迁移 Distilly 流程为 saved Skill；直接安装仓库尚未验证。

蒸馏出的 Person Profile 会封装成 Agent Skill，并可安装到任意受支持的宿主。

🤖 个人 Agent Copy Chapter

Distilly 也面向大家日常在用的个人 Agent。它们各自保存记忆、连接账号，所以 Person Profile 应当能直接带进去，而不是每次从零开始。

📦 支持的数据来源 Copy Chapter

⚡ 安装 Copy Chapter

🤖 For Agents Copy Section

在任意支持的本地 Agent 宿主中发送：

从 https://github.com/titanwings/distilly 安装 Distilly，并确认当前宿主可以发现它。

Agent 会把 Distilly 作为名为 distilly 的 Skill 安装到正确的宿主目录。

👤 For Humans Copy Section

把 Distilly clone 到当前宿主使用的 Skills 目录：

git clone https://github.com/titanwings/distilly <DISTILLY_SKILL_DIR>

各宿主路径、旧版本迁移、Windows、生成 Profile 的安装方式和凭证配置，统一放在 详细安装说明。

🚀 使用 Copy Chapter

刚装好个人 Agent？ 说 “用 Distilly 建立我的画像”。Distilly 会用大约五分钟问你几个问题，给你确认画像全文，然后把简短摘要写进 Agent 每次会话都会读的文件（Claude Code 是 ~/.claude/CLAUDE.md，OpenClaw 是 USER.md）。完整画像只保存在本地 ~/.distilly/owner/profile.md。

为别人建画像：

在 Agent 中说：

用 Distilly 为 <人物> 创建一个 Person Profile。

接下来只需：

1. 选择 colleague、relationship 或 celebrity。
2. 提供一段描述或相关材料；所有字段都可以跳过。
3. 审核结果，让 Distilly 生成 Profile。

结果会封装成名为 {character}-{slug} 的 Agent Skill。可选的名人研究流程和高级工具见 详细安装说明。

✨ 效果示例 Copy Chapter

🌟 celebrity — 蒸馏一个 Andrej Karpathy Copy Section

六维度 research：著作 · 访谈 · 决策 · 表达 DNA · 外部评价 · 时间线
案例出自社区作品 alchaincyf/karpathy-skill

用户            ❯ 现在人人都在说 Agent，你怎么看 2026 年的 AI Agent？

celebrity.skill ❯ 大家都在做 demo，但没人做 eval。
                  Agent 的瓶颈从来不是模型不够聪明，是 scaffolding 跟不上——
                  严格的 evaluation harness、细粒度的错误回放、
                  可控的 tool-calling budget、清楚的 failure mode 分析。
                  大部分产品还停留在"接个 API + 几个 tool + system prompt"的阶段，
                  这叫 demo，不叫 agent。
                  2026 我赌的不是最强模型，是最好的 scaffolding + 最严的 eval。

🧑‍💼 colleague — 蒸馏一个字节 2-1 后端 Copy Section

输入：字节 2-1 后端工程师，INTJ，甩锅高手，字节范

用户            ❯ 帮我看一下这个接口设计

colleague.skill ❯ 等等，这个接口的 impact 是什么？背景没说清楚。
                  （看完后）N+1 查询，改掉。返回结构用统一的
                  {code, message, data}，这是规范，不用问为什么。

用户            ❯ 这个 bug 是你引入的吧

colleague.skill ❯ 上线时间对上了吗？那个需求改了好几个地方，
                  还有其他变更。

💞 relationship — 蒸馏一个暗恋对象 Copy Section

上传半年的聊天记录 + 「性格敏感、话少但嘴硬、会在关键时候认真回」

用户               ❯ 你今天有没有想我

relationship.skill ❯ ……有啊，就一点点。你干嘛问这个。

🔧 功能特性 Copy Chapter

🧱 生成的 Skill 结构 Copy Section

Distilly 当前的创建器以 Persona 为通用底座，不同家族按场景挂载各自的模块：

运行逻辑：接到任务 → Persona 选择材料中可见的偏好与语气 → 附加模块补齐执行细节 → 生成基于材料的回答

🧬 进化机制 Copy Section

• 📥 追加文件 → 自动分析增量 → merge 进对应部分，不覆盖已有结论
• 💬 对话纠正 → 说「他不会这样，他应该是 xxx」→ 写入 Correction 层，立即生效
• 🕰️ 版本管理 → 每次更新自动存档，支持回滚到任意历史版本
• 🔬 名人研究管线 → 字幕下载 → 文稿清洗 → 六维度研究 → 质量检查

⚠️ 注意事项 Copy Chapter

原材料质量决定 Person Profile 质量，不同家族的优质信源不一样：

• colleague 飞书自动采集：需将 App bot 加入相关群聊
• relationship：时间跨度越长越好，能覆盖冲突与和解更佳
• celebrity：避免只喂二手解读
• 目前还是 demo 版本，如果有 bug 请多多提 issue！

📄 技术报告 Copy Chapter

COLLEAGUE.SKILL: Automated AI Skill Generation via Expert Knowledge Distillation (arXiv · arXiv PDF)

这是 Distilly 前身 COLLEAGUE.SKILL / colleague-skill（原同事 Skill） 的技术论文，详细介绍了 Work Skill + Persona 的双层架构、多源数据采集与 Skill 生成机制，也是今天 colleague 家族的理论基础。relationship / celebrity 家族的架构扩展会另起论文。

📝 引用 Copy Chapter

如果你在研究或应用中使用了 Distilly 或 COLLEAGUE.SKILL，请引用技术报告：

@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}
}

也可以使用 CITATION.cff 中的机器可读引用信息。

⭐ Star History Copy Chapter

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• Files Extracted: 11
• Estimated Token Budget: ~11697 tokens
• Canonical Reference: https://codewiki.google/github.com/titanwings/colleague-skill