Github deep research
Skill yrris/pro-agent/cognition/runtime/skills/github-deep-research
多智能体应用与任务执行平台
npx -y skills add yrris/pro-agent --skill github-deep-researchAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 23 stars23 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
深度调研一个 GitHub 仓库（README/结构/关键源码/活跃度），产出研究报告。纯提示词技能，依赖 web_fetch 工具。
SKILL.md
2.1 KB, as published. Nobody here has run it
GitHub 深度调研
调研一个 GitHub 仓库并产出结构化报告。全程用 web_fetch 抓公开页面（无需 token；
配置 COGNITION_GITHUB_TOKEN 后 web_fetch 对 api.github.com / raw.githubusercontent.com
自动携带认证，限流 60→5000 次/小时，抓取写法不变）。
调研流程（按序执行，每步都要真实抓取）
- 定位仓库（仅当用户没给出明确的 owner/repo 时）：
web_search("site:github.com <主题关键词>")→ 从结果标题/链接确定{owner}/{repo}再进入后续步骤；仓库已明确则跳过本步。 - 仓库概览：
web_fetch("https://api.github.com/repos/{owner}/{repo}")→ JSON 含 stars/forks/语言/描述/最近推送时间/开源协议。 - README：
web_fetch("https://raw.githubusercontent.com/{owner}/{repo}/HEAD/README.md")→ 项目定位、用法、架构说明。 - 目录结构：
web_fetch("https://api.github.com/repos/{owner}/{repo}/contents/")→ 顶层文件/目录 JSON；对关键子目录可再抓/contents/{path}。 - 关键源码（按需 2-4 个文件）：
web_fetch("https://raw.githubusercontent.com/{owner}/{repo}/HEAD/{path}")—— 选入口文件/核心模块/配置（package.json、pyproject.toml、go.mod 判断技术栈）。 - 活跃度：
web_fetch("https://api.github.com/repos/{owner}/{repo}/commits?per_page=5")→ 最近提交时间与主题；/issues?state=open&per_page=5看开放问题。
产出要求
调用 write_report 产出 markdown 报告，结构：
定位与解决的问题 → 技术栈与架构（含目录导览）→ 核心实现要点（引用真实源码路径） → 活跃度与成熟度评估 → 借鉴点/风险点。
所有结论必须来自抓取到的真实内容并注明来源 URL；抓取失败的部分如实说明，不要编造。
详细 URL 模式与备选端点见 references/playbook.md（用 skill_read 查看）。