A comprehensive AI-driven code analysis engine that automates quality scoring, deep code reviews, and cross-platform reporting.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install code-quality-system
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-quality-system 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 Quality System?
The Code Quality System is a sophisticated technical framework built to provide deep insights into repository health and developer contributions. It functions by analyzing git repository changes across weekly or monthly dimensions, generating structured reports that are synchronized with a backend database. As a key component of the Openclaw Skills library, it ensures that code quality isn't just monitored but actively improved through automated oversight.
This skill bridges the gap between raw git data and actionable intelligence by providing AI-generated quality scores and line-by-line code reviews. It is designed for engineering leaders and developers who need to maintain high standards across multiple projects while automating the tedious aspects of manual review and reporting.
Code Quality System Use Cases
- Automated generation of weekly and monthly developer productivity and quality reports.
- Performing deep-dive AI code reviews for security, performance, and maintainability.
- Synchronizing commit data and quality metrics to a centralized database for dashboard visualization.
- Notifying team members of code quality trends via Teams webhooks or automated email digests.
- Tracking project-specific statistics like task completion and code churn metrics.
How Code Quality System Works
- The system initiates a git fetch to ensure it has the most recent changes across all branches.
- It matches branches based on specific period values (such as Thursday dates for weekly cycles) to identify relevant features.
- The skill calculates differences between source and feature branches, automatically filtering out noise like lock files and build artifacts.
- AI agents perform a multi-dimensional analysis to generate quality scores (1-10) and identify specific code issues.
- For complex reviews, the system spawns a sub-task using high-context models like Kimi-k2.5 for deep reasoning.
- The resulting data is structured and synchronized into the database following a strict dependency order.
- Reports are finalized and dispatched through Teams notifications or HTML-formatted email reports.
Code Quality System Setup
To deploy this within your Openclaw Skills environment, configure the core directory and settings:
# Navigate to the skill directory
cd ~/.openclaw/skills/code-quality-system
# Prepare the configuration file
cp config.example.json config.json
# Execute a standard weekly analysis
node scripts/analyze-code-v2.js
# Synchronize the results to your database
node scripts/sync-to-db.js
Code Quality System Data Schema & Taxonomy
The skill organizes information into several key database entities to ensure a comprehensive taxonomy of code health:
| Table | Key Metadata | Description |
|---|---|---|
| code_analyses | user_id, ai_quality_score, file_changes | Stores the primary metrics for a specific analysis period. |
| code_issues | file_path, issue_type, severity, suggestion | Details specific flaws identified by the AI during code review. |
| code_reviews | commit_hash, committer_name, status | Maps individual git commits to the broader analysis cycle. |
| team_statistics | total_members, active_members, avg_score | Aggregates data at the organizational level for team health tracking. |
| project_statistics | total_contributors, total_commits | Focuses on repository-level activity and quality trends. |
Code Quality System Advanced Features
- Intelligent model switching: Automatically spawns sub-tasks to Kimi-k2.5 for deep code review logic.
- Custom mapping: Translates standard commit types (feat, fix, refactor) into localized structured data.
- Comprehensive exclusion engine: Uses regex patterns to skip dependencies and generated build files.
- Multi-channel reporting: Integrated support for SMTP email reports and 360Teams webhook integration.
- Incremental synchronization: Supports period-based data overrides to prevent historical data corruption.
SKILL.md
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