Agent skill

GitHub Repo Search

by yunshu0909 in yunshu0909/yunshu_skillshub

帮助用户搜索和筛选 GitHub 开源项目,输出结构化推荐报告。当用户说"帮我找开源项目"、"搜一下GitHub上有什么"、"找找XX方向的仓库"、"开源项目推荐"、"github搜索"、"/github-search"时触发。

MITAuto-check passedProduct & Project Management

Install GitHub Repo Search

skills CLI
$ npx skills add yunshu0909/yunshu_skillshub --skill github-repo-search -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install yunshu0909/yunshu_skillshub github-repo-search --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/yunshu0909/yunshu_skillshub.git skills-src && mkdir -p .claude/skills && cp -r skills-src/coding/github-repo-search .claude/skills/github-repo-search && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
github-repo-search
GitHub stars
768
Used in
1 other repo
Token cost
~935 tokens
SKILL.md length
307 words
Files
1
Skills in repo
25
Repo updated
First seen
Licence
MIT

At a glance

帮助用户搜索和筛选 GitHub 开源项目,输出结构化推荐报告。当用户说"帮我找开源项目"、"搜一下GitHub上有什么"、"找找XX方向的仓库"、"开源项目推荐"、"github搜索"、"/github-search"时触发。

  • Works in 5 steps: 主题(如:agent 记忆、RAG、浏览器自动化) → 数量(Top 10 / Top 20) → 最低 stars(默认 100) → …
  • Product & Project Management work in your project
  • SKILL.md covers 用途, 适用范围(V1.1), 工作流程 and 默认参数(V1.1), plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

GitHub Repo Search is an agent skill from yunshu0909/yunshu_skillshub. 帮助用户搜索和筛选 GitHub 开源项目,输出结构化推荐报告。当用户说"帮我找开源项目"、"搜一下GitHub上有什么"、"找找XX方向的仓库"、"开源项目推荐"、"github搜索"、"/github-search"时触发。

Its SKILL.md is about 940 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Product & Project Management. It works with GitHub. The repository describes itself as: 云舒精选的 Claude Code Skills 集合,提升开发和产品管理效率. The licence is MIT.

When your agent uses it

  • Product & Project Management work in your project

Example prompts

  • “帮我找开源项目”
  • “搜一下GitHub上有什么”
  • “找找XX方向的仓库”
  • “/github-repo-search”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. 主题(如:agent 记忆、RAG、浏览器自动化)
  2. 数量(Top 10 / Top 20)
  3. 最低 stars(默认 100)
  4. 排序模式(必须二选一):相关性优先 / 星标优先(默认:相关性优先)
  5. 目标形态(必须二选一或多选)

What it can do on your machine

Read from SKILL.md and the folder at commit da0d31f. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

GitHub Repo Search loads about 935 tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 307 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~935

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from yunshu0909/yunshu_skillshub at commit da0d31f, republished under its MIT licence (© yunshu0909). 307 words, ~935 tokens.

Download SKILL.mdSave it as .claude/skills/github-repo-search/SKILL.md (or your agent's skills folder).
name
github-repo-search
description
帮助用户搜索和筛选 GitHub 开源项目,输出结构化推荐报告。当用户说"帮我找开源项目"、"搜一下GitHub上有什么"、"找找XX方向的仓库"、"开源项目推荐"、"github搜索"、"/github-search"时触发。
metadata.status
active
metadata.status_updated_at
2026-10-06

GitHub 开源项目搜索助手

用途

从用户自然语言需求出发,经过需求挖掘、检索词拆解、GitHub 检索、过滤分类、深度解读,最终产出结构化推荐结果。

目标不是"给很多链接",而是"给用户可理解、可比较、可决策、可直接行动的候选仓库列表"。

适用范围(V1.1)

  • 数据源:GitHub 公开仓库。
  • 默认不授权(不使用用户 Token)。
  • 默认硬过滤:stars >= 100、archived=false、is:public。
  • 默认输出:单榜单(Top N),榜单内按"仓库归属类型"标注。
  • 本流程默认不包含安装与落地实施(除非用户单独提出)。
配额说明(必须知晓)
  • 未授权 Core API:60 次/小时。
  • Search API:10 次/分钟(独立于 Core 额度)。
  • 需要在报告中注明检索时间与配额状态,避免结果不可复现。

工作流程

环节一:需求收敛(必须完成,不可跳过)

硬性门控:环节一是整个流程的前置条件。无论用户的需求描述多么清晰,都必须走完本环节并获得用户明确确认后,才能进入环节二。禁止根据用户的初始描述直接推断需求并开始检索。即使用户说"直接搜就行",也要先输出需求摘要让用户确认。

第一步:需求挖掘与对齐

目标:把"我想看看 XX"转成可执行、可排序、可解释的检索目标。

需确认信息(最少):

  1. 主题(如:agent 记忆、RAG、浏览器自动化)
  2. 数量(Top 10 / Top 20)
  3. 最低 stars(默认 100)
  4. 排序模式(必须二选一):相关性优先 / 星标优先(默认:相关性优先)
  5. 目标形态(必须二选一或多选): 可直接使用的产品 / 可二次开发的框架 / 资料清单/方法论

建议补充信息(可选):

  1. 偏好技术栈(Python/TS/Go 等)
  2. 使用场景(学习、生产、对标)
  3. 排除项(教程仓库、归档仓库、纯论文复现等)
  4. 部署偏好(本地优先/云端优先/混合)

阶段输出(固定格式):

text
核心诉求:
- 主题:xxx
- 数量:Top N
- 最低 stars:>= 100
- 排序模式:相关性优先 / 星标优先(默认:相关性优先)
- 目标形态:xxx
- 偏好:xxx(可空)
- 排除:xxx(可空)

向用户确认以上信息。用户明确确认后才能进入环节二,否则停在这里继续对齐。


环节二:检索执行(以下环节由模型自主执行,无需用户介入,直到环节四交付报告)
第二步:检索词拆解(5-10 组)

目标:平衡"召回率"和"相关性",避免只靠单词硬搜导致偏题。

拆词规则:

每组 query 由以下维度组合:

  1. 核心词:用户目标词
  2. 同义词:替代表达(如 long-term memory / stateful memory)
  3. 场景词:coding、mcp、tool、platform、awesome、curated
  4. 技术词:agent、sdk、framework、database、os
  5. 排除思路:不在 query 里硬写过多负例,放到后续过滤阶段

产出格式:

text
Query-1: "xxx"
目的:高召回核心主题

Query-2: "xxx"
目的:补同义词盲区
第三步:执行检索与候选召回

执行原则:

  1. 每组 query 都执行检索(建议每组 30-50 条)。
  2. 合并结果形成候选池。
  3. 按 owner/repo 去重。
  4. 记录检索时间与 API 额度信息。

候选池字段(最少):

  1. owner/repo
  2. stars
  3. description
  4. repo_url
  5. archived
  6. language
  7. updated_at
  8. topics
  9. license
第四步:去重与硬过滤

硬过滤(默认):

  1. stars >= 100
  2. archived = false
  3. is:public

可选硬过滤(按需):

  1. fork = false
  2. 指定语言:language:xxx
  3. 更新时效:最近 6-12 个月

环节三:质量精炼
第五步:噪音剔除与相关性重排

目标:解决"命中 memory 但其实不是 agent memory"的噪音问题。

噪音剔除规则(示例):

  1. 与主题无关的通用工程仓库(即使 stars 很高)
  2. 关键词误命中仓库(仅描述中偶然出现 memory/agent)
  3. 无实质内容或异常仓库

排序原则(V1.1):

star 不再作为主排序,只作为召回门槛之一。 建议综合排序权重:

  1. 需求相关性:35%
  2. 场景适用性:30%
  3. 活跃度(更新时效):15%
  4. 工程成熟度(文档/示例/可维护):15%
  5. stars:5%
第六步:仓库归属类型分类(必须)

目标:让用户一眼看懂"这个仓库到底是什么角色",避免把框架、应用、目录混为一谈。

推荐类型字典:

  1. 通用框架层
  2. 应用产品层(可直接使用)
  3. 记忆层/上下文基础设施
  4. MCP 服务层
  5. 目录清单层(awesome/curated)
  6. 垂直场景方案层
  7. 方法论/研究层
第七步:深读与项目介绍撰写(必须)

目标:不是"仓库简介复述",而是输出"对用户有决策价值"的详细介绍。

深读最低要求:

每个入选仓库至少查看:

  1. README 核心定位段
  2. 快速开始/功能章节标题
  3. 近期维护信号(更新时间、Issue/PR 活跃)

项目介绍写作要求(固定):

"项目介绍"必须包含两部分并写细:

  1. 这是什么:它在系统架构中的角色和边界
  2. 为什么推荐:它在用户当前目标下的价值(不是泛泛优点)

可补充:

  1. 典型适用场景(1-2 条)
  2. 限制或不适用场景(1 条)

环节四:交付与迭代
第八步:单榜生成与报告交付(最终)

交付结构(固定):

  1. 需求摘要
  2. 检索词清单(5-10 组 + 目的)
  3. 筛选与重排规则(明确写出)
  4. 结果总览(原始召回/去重后/过滤后)
  5. Top N 单榜(表格)
  6. 结论与下一步建议

Top N 表格字段(固定):

仓库星标仓库归属类型项目介绍(是什么 + 推荐理由)其它信息补充链接

"其它信息补充"建议内容:

  • 语言 / License / 最近更新时间
  • 上手复杂度(低/中/高)
  • 风险提示(若有)
第九步:用户确认与迭代(可选)

迭代触发条件:

用户反馈"太泛/太窄/不够准/解释不够细"。

迭代动作:

  1. 调整检索词(增加场景词或同义词)
  2. 调整 stars 门槛(100 -> 200/500)
  3. 增加限定(语言/方向/更新时间)
  4. 调整类型权重(例如优先应用层或优先框架层)

默认参数(V1.1)

  1. 最低 stars:100
  2. 默认输出:Top 10
  3. 默认过滤:archived=false
  4. 默认必须分类:是
  5. 默认项目介绍粒度:详细(至少"是什么 + 为什么推荐")

质量检查清单(交付前自检)

  1. 是否完成需求对齐并明确"目标形态"
  2. 是否有 5-10 组 query 且每组有目的
  3. 是否记录了检索时间与配额状态
  4. 是否执行了去重、硬过滤和噪音剔除
  5. 是否完成仓库归属类型分类
  6. 是否每个推荐都有详细项目介绍(不是一句话)
  7. 是否使用固定表格字段交付
  8. 是否避免把安装实施混入本流程

© yunshu0909, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in coding/github-repo-search of yunshu0909/yunshu_skillshub.

Open the folder on GitHubat commit da0d31f

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in yunshu0909/yunshu_skillshub, which our catalogue first saw on October 7, 2026.

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Works with

Questions about GitHub Repo Search

What does GitHub Repo Search do?

帮助用户搜索和筛选 GitHub 开源项目,输出结构化推荐报告。当用户说"帮我找开源项目"、"搜一下GitHub上有什么"、"找找XX方向的仓库"、"开源项目推荐"、"github搜索"、"/github-search"时触发。. GitHub Repo Search is an agent skill from yunshu0909/yunshu_skillshub.

When should I use GitHub Repo Search?

GitHub Repo Search fits situations like: product & Project Management work in your project.

How do I install GitHub Repo Search in Claude Code?

Run `npx skills add yunshu0909/yunshu_skillshub --skill github-repo-search -a claude-code`. Or copy the skill folder (coding/github-repo-search in yunshu0909/yunshu_skillshub) into .claude/skills/github-repo-search in your project. Claude Code loads it when a task matches its description.

How do I install GitHub Repo Search in Codex?

Run `npx skills add yunshu0909/yunshu_skillshub --skill github-repo-search -a codex`. Or copy the skill folder (coding/github-repo-search in yunshu0909/yunshu_skillshub) into .agents/skills/github-repo-search in your project. Codex loads it when a task matches its description.

Can I use GitHub Repo Search in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add yunshu0909/yunshu_skillshub --skill github-repo-search -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/github-repo-search, .gemini/skills/github-repo-search, .github/skills/github-repo-search and .opencode/skills/github-repo-search in your project.

What does GitHub Repo Search need to run?

SKILL.md names no scripts, command-line tools or credentials: GitHub Repo Search is instructions for the agent only.

Does GitHub Repo Search access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is GitHub Repo Search safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does GitHub Repo Search use?

GitHub Repo Search is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does GitHub Repo Search use?

About 935 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to GitHub Repo Search?

Skills that share tags, products or a category with GitHub Repo Search: CCPM Project Management (automazeio/ccpm, 8.4k stars), Release Validation (Mesh-LLM/mesh-llm, 3.5k stars), Final Release Review (openai/openai-agents-python, 30k stars) and Ouroboros PM Interview (Q00/ouroboros, 6.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains GitHub Repo Search?

yunshu0909 (a GitHub user) maintains it in yunshu0909/yunshu_skillshub, which has 768 GitHub stars. The repository holds 25 skills in this directory. The repository was last updated on October 6, 2026.

Source: yunshu0909/yunshu_skillshub on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.