An expert AI reviewer that enforces PEP8 standards, type safety, and efficient async patterns in Python codebases.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install python-code-review
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 python-code-review 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 Python Code Review?
The Python Code Review skill is a specialized tool within the Openclaw Skills ecosystem designed to maintain high-quality Python code. It acts as an automated auditor that scrutinizes .py files for structural integrity, performance bottlenecks in asynchronous code, and robust error handling practices.
By leveraging this skill, developers can ensure their projects adhere to professional standards like PEP8 while catching subtle bugs such as mutable default arguments or blocking calls in async loops. It serves as a rigorous gatekeeper that transforms raw code into production-ready, type-safe software.
Python Code Review Use Cases
- Standardizing code style across large Python repositories to meet PEP8 requirements.
- Auditing asynchronous functions to prevent performance-killing blocking calls.
- Enhancing type safety by identifying missing type hints or improper Any usage.
- Improving system reliability by catching bare except clauses and ensuring proper exception context.
- Onboarding new developers by providing immediate, automated feedback on Pythonic best practices.
How Python Code Review Works
- Scope Definition: The skill identifies all relevant Python paths or globs for the current review session.
- Pattern Analysis: It scans the code against a comprehensive library of references including type-safety, async patterns, and error handling protocols.
- False-Positive Screening: Findings are filtered against a set of valid patterns, such as intentional type assertions or necessary Any usage for untyped libraries.
- Evidence Localization: Every valid issue is mapped to a specific [FILE:LINE] anchor for precise developer action.
- Verification & Shipping: The skill executes a mandatory verification protocol for each finding before generating the final, structured report for the user.
Python Code Review Setup
To integrate this capability into your workflow, ensure the following directory structure and commands are used within your environment:
# Navigate to your project's skill directory
cd skills/python-code-review
# Ensure reference files are present
touch references/pep8-style.md references/type-safety.md
# Trigger the review through your AI agent
openclaw run python-code-review --path ./src
Python Code Review Data Schema & Taxonomy
The skill organizes its audit data using a hierarchical reference system and a strict reporting gate.
| Data Component | Description |
|---|---|
| References | Markdown files in /references defining the rules for PEP8, Async, and Types. |
| Issue Types | Categorized into Style, Type Safety, Async Patterns, Error Handling, and Common Mistakes. |
| Location Anchors | Mandatory [FILE:LINE] metadata for every reported finding. |
| Verification Log | Internal metadata generated during the mandatory verification protocol gate. |
Python Code Review Advanced Features
- Multi-Layered Verification: Integrates with the review-verification-protocol to eliminate low-quality or hallucinated reports.
- Context-Sensitive Flagging: Only reports issues like generic exceptions if specific, meaningful alternatives are available.
- Async-Specific Auditing: Deep analysis of async/await usage to prevent the inclusion of blocking calls like time.sleep or requests.
- Stack Trace Preservation: Enforces the use of
raise ... fromto ensure debugging information is never lost during exception handling. - Flexible Ignore Rules: Supports
noqacomments and specific valid patterns to reduce noise in professional environments.
SKILL.md
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