Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Runs a structured, multi-phase quality control audit on a codebase and produces a report with a PASS, WARN, or FAIL verdict. Supports Python, TypeScript, and GDScript projects, covering tests, imports, type checking, static analysis, smoke tests, and documentation. Results can be saved as a baseline and compared across runs.
Combines language-specific static analysis tools (ruff, eslint, mypy) with AI-generated smoke tests and semantic checks in a single workflow, replacing the need to run and interpret each tool separately.
When to use it
- Auditing a Python package before publishing to PyPI
- Running QC on a pull request before merging
- Comparing code quality metrics between two releases
- Quick sanity check on a TypeScript project after refactoring
- Finding missing docstrings and type errors in a GDScript game project
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Run a full QC audit on my Python project
- 1Detect project type and load .qc-config.yaml if present
- 2Run the test suite with coverage using pytest
- 3Check import integrity, run ruff for static analysis, and mypy for type errors
- 4Generate smoke tests for discovered business logic entry points
- 5Verify documentation and git state, then determine verdict
qc-report.md with PASS/WARN/FAIL verdict and per-phase breakdown, plus .qc-baseline.json saved for future comparison