首页/教育与培训/self-learning
S

self-learning

by @philschmidv
4.4(20)

自主技能生成器,从网络学习新技术,帮助用户了解新库/框架/工具并创建技能。

self-directed-learningeducational-technologypersonal-developmentknowledge-acquisitionskill-developmentGitHub
安装方式
npx skills add philschmid/self-learning-skill --skill self-learning
compare_arrows

Before / After 效果对比

1
使用前

面对层出不穷的新技术,手动搜索和学习效率低下,难以系统掌握。用户常因信息过载而感到迷茫,错过学习机会。

使用后

自主技能生成器能从网络学习新技术,帮助用户快速了解新库、框架和工具。系统化学习,高效创建技能,保持技术前沿。

SKILL.md

Self-Learning Skill Generator

Autonomously research and learn new technologies from the web, then generate a reusable skill.

Usage

/learn <topic>

If <topic> is missing, show usage. If topic is ambiguous, ask to clarify:

  • "react" → "React for web, React Native, or a specific library like react-query?"
  • "apollo" → "Apollo GraphQL client, Apollo Server, or Apollo Federation?"
  • "aws" → "Which AWS service? (S3, Lambda, DynamoDB, etc.)"

Normalize to kebab-case for filenames.

2. Discover Sources (Web Search)

Use web search tool to find authoritative documentation:

Search queries to try:

  1. <topic> official documentation
  2. <topic> getting started guide
  3. <topic> API reference
  4. <topic> GitHub repository

Source prioritization:

  1. Official docs sites (e.g., docs.*, *.dev)
  2. Official GitHub repositories (README, /docs)
  3. Official blogs/announcements

Select 3–5 high-quality URLs maximum.

If no credible sources found, ask user to provide a URL.


3. Extract Content (URL Reading)

For each selected URL, read the content:

Extract only relevant sections:

  • Installation / setup
  • Core concepts
  • API reference / key functions
  • Common patterns / examples
  • Version information

Skip irrelevant content:

  • Navigation, ads, login prompts
  • Unrelated sidebar content
  • Comments, forums

If reading the content fails (JavaScript-heavy sites), fall back to browser agent:

Task: Navigate to <URL> and extract the main content including:
- Installation instructions
- Core concepts and API reference
- Code examples
Return the extracted content as markdown.

Record scrape timestamp for each source (use current date: YYYY-MM-DD format).


4. Generate Skill

Skills are modular, self-contained packages. Every skill consists of a required SKILL.md file and optional bundled resources:

skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter metadata (required)
│   │   ├── name: (required)
│   │   └── description: (required)
│   └── Markdown instructions (required)
└── Bundled Resources (optional)
    ├── scripts/          - Executable code (Python/Bash/etc.)
    ├── references/       - Documentation intended to be loaded into context as needed
    └── assets/           - Files used in output (templates, icons, fonts, etc.)
  1. Read references/skill_creation_guide.md to understand the format and principles.
  2. Synthesize the learned and extracted information into a new skill.
    • Trigger: Write a description that clearly defines when to use it.
    • Workflow: Create step-by-step instructions.
    • Format: Ensure valid YAML frontmatter and proper file structure.

5. Save the Skill

Antigravity supports two types of skills, save a global-workspace if asked.

  • .agent/skills/<skill-folder>/ Workspace-specific
  • ~/.gemini/antigravity/skills/<skill-folder>/ Global (all workspaces)

Create directory if it doesn't exist, warn user before overwriting existing skill.


6. Confirm to User

Report:

✓ Created skill: <topic>
  Sources scraped: <N>
  Saved to: .agent/skills/<topic>/SKILL.md
  This skill will auto-trigger when working with <topic>.

Tool Reference

  • search_web: Discover documentation URLs
  • read_url_content: Extract content from static pages
  • browser_subagent: Extract content from JavaScript-heavy sites
  • write_to_file: Save the generated skill

Critical Rules

  1. Never hallucinate documentation: Only include information from scraped sources.
  2. Never invent APIs: If documentation is unclear, ask the user what to do.
  3. Ask for URLs: If automated discovery fails, ask user for specific URLs.
  4. Verify sources: Prefer official sources over third-party tutorials.

用户评价 (0)

发表评价

效果
易用性
文档
兼容性

暂无评价

统计数据

安装量3.0K
评分4.4 / 5.0
版本
更新日期2026年5月19日
对比案例1 组

用户评分

4.4(20)
5
35%
4
50%
3
15%
2
0%
1
0%

为此 Skill 评分

0.0

兼容平台

🔧Claude Code
🔧OpenClaw
🔧OpenCode
🔧Codex
🔧Gemini CLI
🔧GitHub Copilot
🔧Amp
🔧Kimi CLI

时间线

创建2026年3月16日
最后更新2026年5月19日