Skillget

gskill

agent
Security Audit
Warn
Health Warn
  • License — License: MIT
  • No description — Repository has no description
  • Active repo — Last push 0 days ago
  • Community trust — 19 GitHub stars
Code Pass
  • Code scan — Scanned 8 files during light audit, no dangerous patterns found
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose

This agent automatically generates optimized instructions (skills) for coding agents by analyzing a target GitHub repository. It uses evolutionary search and iterative testing to create a SKILL.md file designed to improve an AI's ability to resolve issues within that specific codebase.

Security Assessment

Overall risk: Low. The light code audit scanned 8 files and found no dangerous patterns, hardcoded secrets, or requests for dangerous permissions. However, users should be aware of its operational requirements. The tool requires an `OPENAI_API_KEY` environment variable, which it uses to make external network requests to OpenAI (or a user-specified local model) for skill generation and reflection. Additionally, it inherently relies on executing shell commands to run Docker containers, which it uses to safely sandbox and evaluate test environments. Aside from the necessary API key handling, it does not appear to access unauthorized sensitive data.

Quality Assessment

The project is actively maintained, with its last push occurring today. It is protected by a permissive and standard MIT license, allowing for broad use and modification. Community trust is currently limited but positive, represented by 19 GitHub stars. A minor quality concern is the lack of a description in the repository metadata, though the README provides thorough documentation on how the tool functions.

Verdict

Safe to use.
README.md

gskill

License: MIT
Claude
Last Commit
PRs Welcome

Automatically learns repository-specific skills for coding agents using evolutionary search.

Given a GitHub repository, gskill produces a .claude/skills/{repo}/SKILL.md file containing optimized instructions that dramatically improve an agent's resolve rate on that repo's issues. It implements the pipeline described in the GEPA blog post, which demonstrated improvements from 24% → 93% resolve rate on some repositories.

How it works

  1. Loads verifiable software engineering tasks from SWE-smith for the target repository
  2. Generates an initial skill via static analysis of the repo (README, config files) + gpt 5.2.
  3. Uses GEPA's optimize_anything to iteratively refine the skill through evolutionary search
  4. Each candidate skill is evaluated by running mini-SWE-agent on training tasks inside Docker and checking whether the FAIL_TO_PASS tests pass
  5. Writes the best-scoring skill to disk

Requirements

  • Python 3.13+
  • uv
  • Docker (for running SWE-smith task environments)
  • OPENAI_API_KEY set in your environment (for initial skill generation and GEPA reflection)
  • GSKILL_AGENT_MODEL (optional) — LiteLLM model string for mini-SWE-agent (default: openai/gpt-5.2)

Installation

git clone https://github.com/your-org/gskill
cd gskill
uv sync

Usage

Run the full pipeline

uv run python main.py run https://github.com/pallets/jinja

This will:

  • Load SWE-smith tasks for pallets/jinja
  • Generate an initial skill
  • Run up to 150 mini evaluations to optimize the skill
  • Write the result to .claude/skills/jinja/SKILL.md

Common options

# Custom evaluation budget (more evals = better skill, slower run)
uv run python main.py run https://github.com/pallets/jinja --max-evals 300

# Custom output directory
uv run python main.py run https://github.com/pallets/jinja --output-dir ~/skills

# Skip static analysis, start from an empty seed
uv run python main.py run https://github.com/pallets/jinja --no-initial-skill

# Use a different model for the coding agent
uv run python main.py run https://github.com/pallets/jinja --agent-model openai/gpt-5-mini

# Use a local model (e.g. qwen2.5-coder running on localhost:11434)
OPENAI_BASE_URL=http://localhost:11434/v1 \
  uv run python main.py run https://github.com/pallets/jinja --agent-model openai/gpt-oss-120b

You can also set the agent model via the GSKILL_AGENT_MODEL environment variable instead of passing --agent-model every time.

Preview available tasks

# Show the first 10 SWE-smith tasks for a repo
uv run python main.py tasks pallets/jinja

# Show more
uv run python main.py tasks pallets/jinja --limit 25

Help

uv run python main.py --help
uv run python main.py run --help
uv run python main.py tasks --help

Output

The optimized skill is written to:

.claude/skills/{repo}/SKILL.md

To use it with Claude Code, add the skill path to your project's .claude/settings.json or reference it from your CLAUDE.md.

Task runner

A Taskfile.yml provides shortcuts for common operations (requires Task):

task sync                # uv sync
task lint                # ruff check
task format              # ruff format
task test                # pytest
task run -- owner/repo   # gskill run (pass args via CLI_ARGS)
task tasks               # gskill tasks (pass args via CLI_ARGS)

Project structure

gskill/
├── main.py              # CLI entry point (typer)
├── src/
│   ├── pipeline.py      # Top-level orchestration
│   ├── tasks.py         # SWE-smith dataset loading & splitting
│   ├── evaluator.py     # mini runner + pass/fail evaluation
│   └── skill.py         # Initial skill generation (gpt-5.2) + file I/O
├── Taskfile.yml         # Task runner shortcuts
└── pyproject.toml

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