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skill-from-github skill

/skills/skill-from-github

This skill helps you learn from high quality GitHub projects and encode their proven methodologies into reusable, maintainable AI capabilities.

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SKILL.md
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---
name: skill-from-github
description: Create skills by learning from high-quality GitHub projects
model: sonnet
---

# Skill from GitHub

When users want to accomplish something, search GitHub for quality projects that solve the problem, understand them deeply, then create a skill based on that knowledge.

## When to Use

When users describe a task and you want to find existing tools/projects to learn from:

- "I want to be able to convert markdown to PDF"
- "Help me analyze sentiment in customer reviews"
- "I need to generate API documentation from code"

## Workflow

### Step 1: Understand User Intent

Clarify what the user wants to achieve:
- What is the input?
- What is the expected output?
- Any constraints (language, framework, etc.)?

### Step 2: Search GitHub

Search for projects that solve this problem:

```
{task keywords} language:{preferred} stars:>100 sort:stars
```

**Search tips:**
- Start broad, then narrow down
- Try different keyword combinations
- Include "cli", "tool", "library" if relevant

**Quality filters (must meet ALL):**
- Stars > 100 (community validated)
- Updated within last 12 months (actively maintained)
- Has README with clear documentation
- Has actual code (not just awesome-list)

### Step 3: Present Options to User

Show top 3-5 candidates:

```markdown
## Found X projects that can help

### Option 1: [project-name](github-url)
- Stars: xxx | Last updated: xxx
- What it does: one-line description
- Why it's good: specific strength

### Option 2: ...

Which one should I dive into? Or should I search differently?
```

**Wait for user confirmation before proceeding.**

### Step 4: Deep Dive into Selected Project

Once user selects a project, thoroughly understand it:

1. **Read README** - Understand purpose, features, usage
2. **Read core source files** - Understand how it works
3. **Check examples** - See real usage patterns
4. **Note dependencies** - What it relies on
5. **Identify key concepts** - The mental model behind it

Extract:
- Core algorithm/approach
- Input/output formats
- Error handling patterns
- Best practices encoded in the code

### Step 5: Summarize Understanding

Present what you learned to user:

```markdown
## Understanding [project-name]

### Core Approach
How it solves the problem...

### Key Techniques
1. ...
2. ...

### Limitations
- ...

### What I'll encode in the skill
- ...

Does this look right? Should I proceed to create the skill?
```

**Wait for user confirmation.**

### Step 6: Create Skill via skill-creator

Hand off to skill-creator with:

1. **Clear purpose** - What the skill does
2. **Methodology** - The approach learned from the project
3. **Step-by-step process** - Concrete instructions
4. **Quality criteria** - What good output looks like
5. **Common pitfalls** - What to avoid (learned from project issues/docs)

**Important:** The skill should encode the *knowledge* from the project, not just wrap the tool. The skill should work even without the original project installed.

## Example

**User:** "I want to write better commit messages"

**Step 2 - Search:**
```
commit message convention stars:>100 sort:stars
```

**Step 3 - Present:**
```
## Found 4 projects

### Option 1: conventional-commits/conventionalcommits.org (4.2k ⭐)
- Specification for human and machine readable commit messages
- Why: Industry standard, used by Angular, Vue, etc.

### Option 2: commitizen/cz-cli (15k ⭐)
- CLI tool for creating conventional commits
- Why: Battle-tested, lots of adapters

### Option 3: carloscuesta/gitmoji (14k ⭐)
- Emoji guide for commit messages
- Why: Visual, fun approach

Which one interests you?
```

**User:** "Let's go with conventional commits"

**Step 4 - Deep dive:**
- Read the specification
- Understand types (feat, fix, docs, etc.)
- Learn about scopes, breaking changes
- Study real-world examples

**Step 5 - Summarize:**
```
## Understanding Conventional Commits

### Core Approach
Structured format: <type>(<scope>): <description>

### Key Types
- feat: new feature
- fix: bug fix
- docs: documentation
- ...

### Benefits Learned
- Automated changelog generation
- Semantic versioning
- Clear history

Ready to create a commit message skill based on this?
```

**Step 6 - Create skill with these principles encoded**

## Important Notes

1. **Always get user confirmation** - At step 3 (project selection) and step 5 (before creating)
2. **Prefer learning over wrapping** - Encode the knowledge, not just "run this tool"
3. **Check license** - Mention if project has restrictive license
4. **Credit the source** - Include attribution in generated skill
5. **Quality over speed** - Take time to truly understand the project

## What This Skill is NOT

- NOT a package installer
- NOT a tool wrapper
- It's about **learning from the best projects** and encoding that knowledge into a reusable skill

Overview

This skill creates reliable AI skills by learning from high-quality GitHub projects and encoding their proven methodologies. It finds relevant repositories, evaluates their quality, extracts core techniques, and converts that knowledge into a standalone skill specification. The result is a skill that follows expert practices without requiring the original project to be installed.

How this skill works

First it clarifies user intent, inputs, outputs, and constraints. Then it searches GitHub with focused queries and quality filters (stars, recency, documentation, real code). After presenting top candidates and getting confirmation, it deep-dives into the chosen project to extract algorithms, data formats, error handling, and best practices. Finally it summarizes findings and produces a skill design that embodies the learned approach and notes license and attribution.

When to use it

  • You want a skill that implements a well-established solution pattern from open-source projects.
  • You need a robust starting point for tasks like parsing, conversion, or automation where GitHub hosts mature tools.
  • You want a skill informed by battle-tested code and real examples rather than ad-hoc heuristics.
  • You need to learn proven input/output formats, error patterns, or dependency constraints before implementing a skill.

Best practices

  • Start by clarifying exact input, desired output, and constraints before searching.
  • Use broad-to-narrow GitHub queries and include keywords like cli, library, or tool when relevant.
  • Apply quality filters: >100 stars, updated within 12 months, clear README, and real code.
  • Always present 3–5 top candidates and get user confirmation before deep-diving.
  • Encode knowledge (algorithms, formats, pitfalls) into the skill, do not merely wrap the tool.
  • Check and record the repository license and include attribution in the skill output.

Example use cases

  • Create a skill that converts Markdown to PDF by learning from popular converters and templates.
  • Design a sentiment-analysis skill by studying top NLP libraries and example preprocessing pipelines.
  • Build a code-documentation skill by extracting techniques from projects that generate API docs.
  • Produce a commit-message assistant by encoding the Conventional Commits specification and examples.
  • Develop a CLI-style file transformer skill informed by a well-maintained command-line tool.

FAQ

Will the skill require the original project to be installed?

No. The approach is to encode the project's knowledge and patterns into the skill so it works independently of the original repository.

How do you ensure the chosen projects are high quality?

I apply strict filters (stars >100, recent updates, clear README, actual source code) and review core files and examples before selecting candidates.