An intelligent development assistant that automates code analysis, detects vulnerabilities, and generates comprehensive Pull Request documentation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-code-reviewer
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 ai-code-reviewer 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 AI Code Reviewer?
The AI Code Reviewer is a sophisticated tool within the Openclaw Skills ecosystem designed to streamline the software development lifecycle. By leveraging advanced language models, it provides deep analysis of code changes, identifying everything from minor stylistic inconsistencies to critical security vulnerabilities like SQL injection or memory leaks. This skill serves as a tireless peer reviewer that helps developers maintain high standards without slowing down the delivery pipeline.
Beyond simple linting, the skill understands the context of changes, allowing it to generate meaningful Pull Request descriptions and relevant unit tests for various frameworks. Whether you are working in Python, Go, or TypeScript, the AI Code Reviewer integrates into your workflow to ensure that every commit is robust, secure, and well-documented.
AI Code Reviewer Use Cases
- Automatically auditing code for naming conventions and stylistic consistency during the development phase.
- Identifying complex logical bugs and potential resource leaks before they reach production.
- Scanning for security vulnerabilities such as XSS, SQL injection, and sensitive data exposure.
- Generating structured Markdown Pull Request descriptions based on git diffs.
- Creating boilerplate unit tests for new functions to ensure high code coverage.
How AI Code Reviewer Works
- The agent receives code snippets, files, or git diffs through user triggers or automated hooks.
- It analyzes the code against a comprehensive rule-set covering style, security, performance, and logic.
- The AI generates a structured report, including a quality score and specific line-by-line improvement suggestions.
- If requested, the skill synthesizes the changes into a detailed PR description covering the impact scope and testing checklist.
- For logic-heavy components, it suggests or generates unit test cases compatible with the project's testing framework.
- The final output is delivered as a formatted Markdown report or pushed directly to a version control platform.
AI Code Reviewer Setup
To integrate this skill into your workflow, configure the necessary environment variables and local settings:
# Required: Your OpenRouter API Key
OPENROUTER_API_KEY=sk-or-v1-xxxxx
# Optional: GitHub Token for automated PR management
GITHUB_TOKEN=ghp_xxxxx
# Optional: Configure audit strictness (low/medium/high)
CODE_REVIEW_STRICTNESS=medium
ENABLE_SECURITY_CHECK=true
Update your local configuration to enable specific features within the Openclaw Skills environment.
AI Code Reviewer Data Schema & Taxonomy
The skill organizes its analysis and output using a structured taxonomy to ensure clarity and actionable feedback:
| Component | Details |
|---|---|
| Report Metadata | File paths, quality scores (0-100), and language detection. |
| Issue Classification | Issues categorized by Style, Security, Performance, and Logic. |
| Actionable Items | Line-specific code suggestions with 'Current' vs 'Proposed' comparisons. |
| Test Schema | Generated unit test cases for frameworks like Jest, Pytest, or JUnit. |
| Documentation | Markdown-formatted PR templates including change summaries and impact analysis. |
AI Code Reviewer Advanced Features
- Multi-language support for a wide range of ecosystems including Rust, Go, Java, PHP, and Python.
- Automated repair suggestions that allow users to apply fixes with a single confirmation.
- Customizable review strictness levels to match project-specific quality gates.
- Deep integration with GitHub for automated Pull Request commentary and descriptions.
- Support for major testing frameworks to automate the creation of edge-case test suites.
SKILL.md
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