github-stars-analyzer
- Repo stars 0
- Author updated Live
- Author repo skills-registry
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Manual integration
- External API key
- Required · Vendor-specific
- Operating systems
- Docker
- Runtime requirements
- Python · Docker
- Permissions
-
- Read-only
- Write / modify
- Network behavior
- External requests
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools，默认拥有全部工具权限。
---
name: github-stars-analyzer
description: 通过 GitHub 公开 API 抓取用户所有 starred 仓库，按大类自动分组，生成标准化中文 Markdown 报告文件。 github-stars-analyzer/ ├── SKI…
category: ai
runtime: Python / Docker
---
# github-stars-analyzer output preview
## PART A: Task fit
- Use case: 通过 GitHub 公开 API 抓取用户所有 starred 仓库，按大类自动分组，生成标准化中文 Markdown 报告文件。 github-stars-analyzer/ ├── SKILL.md ← 本文件 │ └── fetch_stars.py ← 抓取 + 报告生成脚本 └── template.md ← 标准化报告模板（说明格式规范） requires Vendor-specific API key; runs on Python. Works with Claude Code, Cursor, Cline and 23 more..
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “技能概述 / 文件结构 / 执行流程” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “通过 GitHub 公开 API 抓取用户所有 starred 仓库，按大类自动分组，生成标准化中文 Markdown 报告文件。 github-stars-analyzer/ ├── SKILL.md ← 本文件 │ └── fetch_stars.py ← 抓取 + 报告生成脚本 └── template.md ← 标准化报告模板（说明格式规范） requires Vendor-specific API key; runs on Python. Works with Claude Code, Cursor, Cline and 23 more.”.
- **02** When the source has headings, the agent prioritizes “技能概述 / 文件结构 / 执行流程” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
## Running Rules
- read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. Choose the trigger
The source mentions slash commands such as `/path`, `/home`, `/mnt`; use them first when your agent supports command triggers.
Provide inputs and boundaries
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files.
Validate with a small run
Start with a small task and check whether the result follows “技能概述 / 文件结构 / 执行流程”. Inspect diffs, logs, previews, or tests before expanding scope.
Review before delivery
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: github-stars-analyzer
description: 通过 GitHub 公开 API 抓取用户所有 starred 仓库，按大类自动分组，生成标准化中文 Markdown 报告文件。 github-stars-analyzer/ ├── SKI…
category: ai
source: tomevault-io/skills-registry
---
# github-stars-analyzer
## When to use
- 通过 GitHub 公开 API 抓取用户所有 starred 仓库，按大类自动分组，生成标准化中文 Markdown 报告文件。 github-stars-analyzer/ ├── SKILL.md ← 本文件 │ └── fetc…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “技能概述 / 文件结构 / 执行流程” and keep inference separate from source facts.
- read files, write/modify files; may access external network resources; requires Vendor-specific API keys.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. Evidence boundary and execution chain
The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "github-stars-analyzer" {
input -> user goal + target files + boundaries + acceptance criteria
context -> 技能概述 / 文件结构 / 执行流程
rules -> SKILL.md triggers / order / output contract
runtime -> Python / Docker | read files, write/modify files | may access external network resources
guardrails -> requires Vendor-specific API keys + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}GitHub Stars 分析器技能
技能概述
通过 GitHub 公开 API 抓取用户所有 starred 仓库，按大类自动分组，生成标准化中文 Markdown 报告文件。
文件结构
github-stars-analyzer/
├── SKILL.md ← 本文件
├── scripts/
│ └── fetch_stars.py ← 抓取 + 报告生成脚本
└── assets/
└── template.md ← 标准化报告模板（说明格式规范）执行流程
第一步：解析用户名
从用户消息中提取 GitHub 用户名，支持以下格式：
https://github.com/USERNAME?tab=starshttps://github.com/USERNAME@USERNAME- 裸用户名
第二步：运行脚本
将脚本复制到工作目录后执行：
cp /path/to/skill/scripts/fetch_stars.py /home/claude/fetch_stars.py
python3 /home/claude/fetch_stars.py USERNAME --output /home/claude/USERNAME_github_stars.md可选参数：
--token <PAT>：GitHub Personal Access Token，将 API 限额从 60次/小时 提升至 5000次/小时--output <路径>：指定输出文件路径，默认为<username>_github_stars.md
第三步：交付文件
cp /home/claude/USERNAME_github_stars.md /mnt/user-data/outputs/USERNAME_github_stars.md然后使用 present_files 工具将文件提供给用户。
报告大类分类规则
脚本按仓库 topics 和语言自动划分以下大类（优先级从上到下匹配）：
| 大类 | 匹配关键词（topics / 语言） |
|---|---|
| 🤖 人工智能与机器学习 | ai, ml, machine-learning, deep-learning, llm, nlp, neural-network, gpt, pytorch, tensorflow |
| 🛠️ 开发工具与效率 | cli, tool, productivity, devtools, vscode, vim, ide, automation, workflow |
| 🌐 前端与界面 | frontend, react, vue, angular, css, html, ui, design, javascript, typescript |
| ⚙️ 后端与框架 | backend, api, rest, graphql, microservice, server, django, flask, fastapi, go, rust |
| 📦 基础设施与运维 | devops, docker, kubernetes, ci-cd, cloud, aws, infra, terraform, nginx |
| 🗄️ 数据库与数据 | database, sql, nosql, redis, postgres, mongodb, data, analytics, etl |
| 🔒 安全与隐私 | security, hacking, pentest, crypto, privacy, auth, vulnerability |
| 📚 学习资源与文档 | awesome, tutorial, learning, course, book, guide, roadmap, cheatsheet |
| 🎮 游戏与创意 | game, graphics, animation, art, creative, shader |
| 🐍 Python 生态 | 语言为 Python 且未命中以上类别 |
| 其他 | 未匹配任何类别的仓库 |
报告模板规范
详见 assets/template.md，生成报告时严格遵循该模板的结构和中文命名规范。
错误处理
- HTTP 404：提示用户名不存在，终止
- HTTP 403：提示 API 超限，显示剩余等待秒数，建议添加
--token - 网络不通：提示检查网络连接（Claude 服务器无外网出口，脚本需在用户本地运行）
向用户说明
完成后告知用户：
- 文件已生成，可直接下载
- 如需在本地重新运行：
python3 fetch_stars.py <用户名> - 如遇 API 限速，在 GitHub 设置页生成一个免费 Token（无需任何权限勾选）后加上
--token参数
Source: AgentWorkers/skills — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review