# Repo To Skill

> Generate an agent skill from a GitHub open-source repository. Use when asked to create a skill from a repo URL, turn a GitHub project into a usable skill, or generate skill files for a CLI/library/framework. Triggers on phrases like "create a skill from this repo", "make a skill for vllm", "turn this GitHub project into a skill", "generate a skill from repo". Also use when asked to "skillify" a t…

- **Type:** Skill
- **Install:** `agentstack add skill-shuyhere-repo-to-skill-repo-to-skill`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [shuyhere](https://agentstack.voostack.com/s/shuyhere)
- **Installs:** 0
- **Category:** [Developer Tools](https://agentstack.voostack.com/c/developer-tools)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [shuyhere](https://github.com/shuyhere)
- **Source:** https://github.com/shuyhere/repo-to-skill

## Install

```sh
agentstack add skill-shuyhere-repo-to-skill-repo-to-skill
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Repo-to-Skill Generator

Generate a complete, tested, and evaluated agent skill from any GitHub repository.

## Command

```
/skill repo-to-skill 
```

Example:
```
/skill repo-to-skill https://github.com/vllm-project/vllm
```

## Overview

Given a GitHub repo URL, this skill:
1. Clones and analyzes the repository
2. Extracts usage patterns from README, docs, examples, CLI help
3. Generates a working skill (SKILL.md + resources)
4. Tests the skill by running basic operations
5. Evaluates the skill with test prompts and assertions

## Process

### Step 1: Clone & Analyze

```bash
# Clone repo (shallow for speed)
git clone --depth 1  /tmp/repo-to-skill/
```

Analyze in this order (stop when you have enough):
1. **README.md** — primary source for installation, quickstart, features
2. **docs/** or **documentation/** — detailed usage guides
3. **examples/** — concrete usage patterns (high value)
4. **CLI help** — if it's a CLI tool, check `--help` output
5. **setup.py / pyproject.toml / package.json** — dependencies and entry points
6. **Source code** — only if docs are insufficient; focus on public API

Extract:
- **What it does** (one sentence)
- **Installation method** (pip, npm, cargo, etc.)
- **Core commands/API** (the 5-10 most common operations)
- **Configuration** (env vars, config files, required setup)
- **Input/Output formats** (what goes in, what comes out)
- **Common patterns** (from examples/ or README)

### Step 2: Classify the Tool

Determine the tool type to shape the skill structure:

| Type | Skill Focus | Example |
|------|------------|---------|
| **CLI tool** | Commands, flags, common workflows | vllm, ffmpeg, gh |
| **Python library** | API patterns, code snippets | transformers, pandas |
| **Framework** | Project setup, config, patterns | FastAPI, Next.js |
| **Service** | API endpoints, auth, integration | Stripe, OpenAI |

### Step 3: Generate Skill Structure

Create the skill in the user's preferred location (default: `~/.agents/skills//`).

```
/
├── SKILL.md                    # Core instructions
├── references/
│   ├── api-reference.md        # Full API/CLI reference (if large)
│   ├── examples.md             # Curated usage examples
│   └── configuration.md        # Config options (if complex)
└── scripts/
    └── setup.sh                # Installation/setup script (if needed)
```

#### SKILL.md Template

```markdown
---
name: 
description: 
---

# 

## Installation

## Quick Start

## Core Operations

## Common Patterns

## Troubleshooting

## References

- For full API details, see [references/api-reference.md](references/api-reference.md)
- For more examples, see [references/examples.md](references/examples.md)
```

### Step 4: Test the Skill

Verify the skill works by actually using the tool:

1. **Install test** — run the installation command
2. **Smoke test** — run the quickstart example
3. **Feature test** — try 2-3 core operations from the skill

If tests fail, update the skill with corrections.

Document test results:
```
✅ Installation: pip install vllm → success
✅ Quick start: vllm serve model → server started
❌ Feature: offline batching → fixed: added --dtype auto flag
```

### Step 5: Validate & Deliver

Before delivering:
- [ ] SKILL.md under 500 lines
- [ ] Frontmatter has name + description
- [ ] Description includes trigger phrases
- [ ] All referenced files exist
- [ ] Examples are tested and working
- [ ] No secrets or credentials in skill files

Present the skill to the user with a summary of what it covers.

## Step 6: Evaluate the Skill

After generating and testing, run a structured evaluation. See [references/eval-schemas.md](references/eval-schemas.md) for full JSON schemas.

### Create Test Prompts

Write 3-5 realistic prompts a user would send to an agent with this skill. Save to `evals/evals.json`:

```json
{
  "skill_name": "vllm",
  "evals": [
    {
      "id": 1,
      "prompt": "Serve Llama-3-8B with vllm on port 8000",
      "expected_output": "Working vllm serve command with correct model and port",
      "expectations": [
        "Command includes 'vllm serve' or 'python -m vllm.entrypoints'",
        "Port 8000 is specified",
        "Model name is correct"
      ]
    }
  ]
}
```

### Run Evals (with-skill vs baseline)

For each test prompt, spawn two runs in parallel:

1. **With-skill run**: Agent has the generated skill loaded
2. **Baseline run**: Same prompt, no skill

Save outputs to `-workspace/iteration-/eval-/with_skill/` and `without_skill/`.

### Grade Results

For each run, evaluate assertions and produce `grading.json`:

```json
{
  "expectations": [
    {
      "text": "Command includes 'vllm serve'",
      "passed": true,
      "evidence": "Output contains: vllm serve meta-llama/Llama-3-8B"
    }
  ],
  "summary": {
    "passed": 3,
    "failed": 0,
    "total": 3,
    "pass_rate": 1.0
  }
}
```

For assertions that can be checked programmatically (file exists, command runs, output matches pattern), write and run a script instead of eyeballing.

### Aggregate & Report

Produce a benchmark comparing with-skill vs baseline:
- **pass_rate**: mean ± stddev across runs
- **time_seconds**: execution time
- **tokens**: token usage
- **delta**: improvement from skill

Present results to user. If pass_rate < 0.7, iterate on the skill.

## Guidelines

- **Be concise** — only include what the model doesn't already know
- **Prefer examples over explanations** — show, don't tell
- **Test everything** — never include untested commands
- **Progressive disclosure** — keep SKILL.md lean, put details in references/
- **Version-aware** — note the repo version/commit analyzed
- **Installation-first** — always verify the tool actually installs cleanly
- **Evaluate** — always run at least 3 test prompts before delivering

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [shuyhere](https://github.com/shuyhere)
- **Source:** [shuyhere/repo-to-skill](https://github.com/shuyhere/repo-to-skill)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-shuyhere-repo-to-skill-repo-to-skill
- Seller: https://agentstack.voostack.com/s/shuyhere
- Browse the marketplace: https://agentstack.voostack.com/browse

---
Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.