A structured 8-phase quality control tool that automates code audits, static analysis, and smoke testing for multiple programming languages.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install code-qc
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install code-qc using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Code QC: Automated Codebase Quality Audit?
Code QC is a comprehensive quality control skill designed for AI agents to perform deep technical audits on software projects. It moves beyond simple linting by integrating semantic understanding, cross-module consistency checks, and dynamic smoke test generation. By utilizing Openclaw Skills, developers can ensure their codebase meets high standards for test coverage, type safety, and architectural integrity across Python, TypeScript, and GDScript environments.
The tool is designed to be highly configurable, allowing teams to set specific thresholds for failure and success. It bridges the gap between raw static analysis and high-level architectural review by automating the tedious parts of code checking while providing clear, actionable verdicts for developers and CI/CD pipelines.
Code QC: Automated Codebase Quality Audit Use Cases
- Auditing a new codebase for technical debt, security vulnerabilities, and quality issues.
- Running a quick sanity check before committing code or merging pull requests to maintain high standards.
- Comparing current code quality against a historical baseline to prevent regressions in large projects.
- Automatically fixing common linting and formatting issues using the integrated fix mode.
- Generating professional quality reports for stakeholders or as part of a formal release process.
How Code QC: Automated Codebase Quality Audit Works
- Detects the project type and language profile to apply the correct diagnostic tooling for Python, TypeScript, or GDScript.
- Loads custom configurations from a .qc-config.yaml file to handle specific thresholds or directory exclusions.
- Executes an 8-phase audit lifecycle covering tests, import integrity, static analysis, type checking, smoke tests, UI verification, file consistency, and documentation.
- Synthesizes findings into a standardized verdict of PASS, PASS WITH WARNINGS, or FAIL based on user-defined metrics.
- Generates detailed reports in Markdown and JSON formats, saving a baseline for future delta comparisons.
Code QC: Automated Codebase Quality Audit Setup
To begin using this skill within your Openclaw Skills ecosystem, ensure your environment has the necessary language-specific tools installed. You can trigger an audit directly from your agent interface or terminal.
# Install prerequisites for Python projects
pip install ruff pytest pytest-cov mypy
# Install prerequisites for TypeScript projects
npm install eslint typescript
# Run a full audit on the current directory
# (Command executed via the AI agent using the code-qc skill)
Create an optional .qc-config.yaml in your root directory to customize failure thresholds and exclude specific folders like vendor or dist.
Code QC: Automated Codebase Quality Audit Data Schema & Taxonomy
Code QC organizes its output into structured files and metadata to track quality trends over time. This data can be consumed by other Openclaw Skills for further automation.
| File Name | Purpose | Format |
|---|---|---|
.qc-config.yaml |
User-defined thresholds and exclusion rules | YAML |
.qc-baseline.json |
Machine-readable historical data for delta reporting | JSON |
qc-report.md |
Human-readable audit report with detailed phase breakdowns | Markdown |
scripts/import_check.py |
Utility for verifying runtime import behavior | Python |
Code QC: Automated Codebase Quality Audit Advanced Features
- Multi-language support with specialized profiles for Python, TypeScript, and GDScript environments.
- AI-powered smoke test generation that automatically discovers API entry points and tests business logic.
- Automated fixing mode that applies safe corrections for linting and formatting issues instantly.
- Delta reporting to highlight specific improvements or regressions relative to the previous baseline.
- Deep import integrity analysis that distinguishes between critical core failures and optional dependency issues.
SKILL.md
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