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Agent Skill · AI 工具

skill-gen(技能生成)

skill-gen 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

skill-gen 是AI 工具类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证为 MIT,第三方扫描已通过,按安装量在AI 工具类的 2,536 个 Skill 里排第 1,294。

总安装 881周安装 36GitHub Stars 76下载 285核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:skill-gen(技能生成)
来源仓库:https://github.com/htlin222/dotfiles
仓库路径:skills/skill-gen
安装命令:
npx skills add https://github.com/htlin222/dotfiles --skill skill-gen
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/htlin222/dotfiles --skill skill-gen

这个 Skill 是做什么的

AI 工具类 Skill 用来扩展助手本身的能力,例如接模型、接检索、接外部服务。

skill-gen 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态或协作事项进行整理。适用于需要快速获取仓库变更、Issue 进展或 PR 审查的场景,提升代码协作效率。

通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和文件访问权限。使用前建议核实维护状态,避免触发敏感操作如命令执行或网络请求。可结合原始 README 进一步了解具体功能和调用方式。

来源仓库内路径:skills/skill-gen

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/htlin222/dotfiles --skill skill-gen;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

skill-gen 怎么安装?

在终端执行 npx skills add https://github.com/htlin222/dotfiles --skill skill-gen。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skill-gen 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

skill-gen 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

skill-gen 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

skill-gen 用的人多吗?

公开数据是累计安装 881 次,近一周安装 36 次,下载 285 次,GitHub 76 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

skill-gen 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理AI 工具类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

AI 工具类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
executing-plans

执行计划 · Codex、Claude

1,515,336173,110只读MIT通过
verification-before-completion

完成前的验证 · Codex、Claude

1,319,472173,121权限未标注MIT通过
subagent-driven-development

子 Agent 驱动开发 · Codex、Claude

1,312,416173,138只读MIT通过
copy-editing

复制编辑 · Codex、Claude

1,209,57625,632只读MIT通过
paywall-upgrade-cro

付费专区升级 cro · Codex、Claude

1,030,17625,617只读MIT通过
skill-gen

技能生成 · 本页

88176调用外部服务MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Skill Generator

Generates properly structured Claude Code skills from user requirements.

Quick Start

To create a new skill, gather from the user:

  1. What should this skill do?
  2. A short name (e.g., processing-pdfs, testing-code)
  3. When should Claude use it?
  4. Does it need scripts or just instructions?

Workflow

Step 1: Gather Requirements

  • Purpose: What does the skill do?
  • Triggers: When should it activate? (keywords, scenarios)
  • Complexity: Instructions-only, or needs scripts/references?
  • Freedom level: Rigid (exact steps) vs flexible (general guidance)?

Step 2: Generate Skill Structure

Create directory: ~/.claude/skills/{skill-name}/

{skill-name}/
├── SKILL.md           # Required: Main instructions (<500 lines)
├── scripts/           # Optional: Executable code
│   └── main.py
└── references/        # Optional: Detailed docs (one level deep)
    └── examples.md

Step 3: Write SKILL.md

---
name: {skill-name}
description: {What it does in third person}. Use when {trigger conditions}.
---

# {Skill Title}

{Brief overview - assume Claude is smart, only add context it doesn't already have}

## Quick Start

{Minimal working example or first step}

## Instructions

{Step-by-step guidance with appropriate freedom level}

## Advanced Features

**Feature A**: See [references/feature-a.md](references/feature-a.md)
**Feature B**: See [references/feature-b.md](references/feature-b.md)

Step 4: Check Line Count & Apply Progressive Disclosure

After drafting SKILL.md, count its lines. If approaching or exceeding 500 lines:

  1. Inform the user: "The SKILL.md is {N} lines — exceeding the recommended 500-line limit for optimal performance."
  2. Identify splittable sections: detailed references, long examples, advanced features, API docs
  3. Move to reference files: references/{topic}.md — keep links one level deep
  4. Replace in SKILL.md with a short summary + link: **Advanced feature X**: See [references/feature-x.md](references/feature-x.md)
  5. Add table of contents to any reference file over 100 lines

Even under 500 lines, prefer splitting if a section is self-contained and only needed in specific scenarios.

Step 5: Validate

Run through the validation checklist before finalizing.

Naming Rules

  • Length: 1-64 characters
  • Format: lowercase letters, numbers, hyphens only
  • Prefer gerund form: processing-pdfs, testing-code, writing-documentation
  • Acceptable alternatives: pdf-processing, process-pdfs
  • Not allowed: start/end with -, consecutive --, reserved words (anthropic, claude), XML tags
  • Avoid: vague names (helper, utils, tools), overly generic (documents, data)
  • Must match: folder name

Description Rules

  • Maximum: 1024 characters, non-empty, no XML tags
  • Always third person: "Processes Excel files" not "I can help you" or "You can use this"
  • Include both: what it does AND when to use it
  • Be specific with key terms: Claude uses descriptions to choose from 100+ skills

Good examples:

description: Extracts text and tables from PDF files, fills forms, merges documents. Use when working with PDF files or when the user mentions PDFs, forms, or document extraction.
description: Generates descriptive commit messages by analyzing git diffs. Use when the user asks for help writing commit messages or reviewing staged changes.

Avoid: "Helps with documents", "Processes data", "Does stuff with files"

Core Principles

Conciseness

The context window is shared. Challenge each piece of information:

  • "Does Claude really need this explanation?"
  • "Can I assume Claude knows this?"
  • "Does this paragraph justify its token cost?"

Only add context Claude doesn't already have.

Degrees of Freedom

Match specificity to the task's fragility:

FreedomUse WhenExample
High (text instructions)Multiple valid approaches, context-dependentCode review guidelines
Medium (pseudocode/params)Preferred pattern exists, some variation okReport generation template
Low (exact scripts)Fragile operations, consistency criticalDatabase migrations

Progressive Disclosure

  • SKILL.md = overview + navigation (table of contents)
  • Reference files = detailed content (loaded on-demand)
  • Keep SKILL.md body under 500 lines
  • Keep references one level deep from SKILL.md (no nested references)
  • Structure long reference files (100+ lines) with a table of contents

Consistent Terminology

Pick one term and use it throughout. Don't mix "API endpoint" / "URL" / "API route" / "path".

Patterns

Workflow with Checklist

For complex multi-step operations:

## Workflow

Copy this checklist and track progress:
  • [ ] Step 1: Analyze input
  • [ ] Step 2: Generate plan
  • [ ] Step 3: Validate plan
  • [ ] Step 4: Execute
  • [ ] Step 5: Verify output

Feedback Loop

Run validator → fix errors → repeat:

1. Make changes
2. Validate: `python scripts/validate.py`
3. If validation fails: fix issues, return to step 2
4. Only proceed when validation passes

Template Pattern

Provide output format templates, matching strictness to requirements.

Examples Pattern

Provide input/output pairs for output-quality-dependent skills.

Conditional Workflow

Guide through decision points:

**Creating new?** → Follow "Creation workflow"
**Editing existing?** → Follow "Editing workflow"

Scripts Best Practices

  • Handle errors explicitly — don't punt to Claude
  • No voodoo constants — justify and document all values
  • List dependencies — specify packages to install
  • Make execution intent clear:

- "Run script.py to extract fields" (execute) - "See script.py for the algorithm" (read as reference)

  • Use forward slashes in all file paths (scripts/helper.py not scripts\helper.py)
  • MCP tool references — use fully qualified names: ServerName:tool_name

Anti-Patterns

  • Explaining things Claude already knows (what PDFs are, how libraries work)
  • Offering too many tool/library options — provide a default with escape hatch
  • Time-sensitive information — use "old patterns" section with <details> tag
  • Deeply nested references (file → file → file)
  • Windows-style paths
  • Assuming packages are installed without explicit install instructions

Validation Checklist

Core Quality

  • Name: lowercase, numbers, hyphens only, 1-64 chars, no reserved words
  • Description: third person, specific, includes what + when, under 1024 chars
  • SKILL.md body under 500 lines
  • Additional details in separate files (one level deep)
  • No time-sensitive information
  • Consistent terminology throughout
  • Concrete examples (not abstract)
  • Progressive disclosure used appropriately
  • Workflows have clear steps

Scripts (if applicable)

  • Scripts handle errors explicitly
  • No voodoo constants (all values justified)
  • Required packages listed with install instructions
  • Forward slashes in all paths
  • Validation/verification steps for critical operations
  • Feedback loops for quality-critical tasks

Testing

  • Tested with real usage scenarios
  • Works across target models (Haiku needs more guidance, Opus needs less)

Further Reading

Eval-driven development: See references/eval-driven-development.md for the build → test → observe → iterate cycle, two-Claude iterative development, and observation patterns for refining skills after initial creation.

平台分布

Claude Code

28.58%
按下载量换算81

Gemini CLI

22.68%
按下载量换算65

windsurf

18.87%
按下载量换算54

Antigravity

12.82%
按下载量换算37

OpenCode

8.01%
按下载量换算23

Cursor

3.3%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

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