A specialized technical audit tool for FastAPI applications that ensures idiomatic routing, secure dependency injection, and non-blocking asynchronous performance.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install fastapi-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 fastapi-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 FastAPI Code Review?
The FastAPI Code Review skill is a high-precision analysis tool designed for Openclaw Skills to maintain the structural integrity of modern Python web applications. It focuses on the specific idioms of the FastAPI framework, ensuring that developers leverage built-in features like Pydantic validation and dependency injection rather than reinventing wheels or introducing technical debt.
By implementing this skill, teams can automate the detection of common pitfalls such as blocking I/O in async handlers, missing response models, and improper status code management. It enforces a standardized review protocol that prioritizes type safety and high-performance asynchronous patterns, making it an essential asset for scaling robust backend services.
FastAPI Code Review Use Cases
Primary Applications
- Automated pull request auditing for Python-based microservices.
- Performance bottleneck detection by identifying blocking I/O calls in async def handlers.
- Ensuring consistent API documentation through mandatory response_model and status_code declarations.
- Validating complex dependency injection chains and resource cleanup in yield-based dependencies.
- Standardizing error handling via FastAPI-native HTTPException protocols.
How FastAPI Code Review Works
Operational Workflow
- The skill identifies FastAPI route decorators (@router or @app) to establish the entry point for analysis.
- It maps the request and response surface, verifying the presence of Pydantic models for automatic validation and serialization.
- The engine performs a signature analysis on handler parameters to validate Depends() usage and security hooks.
- A deep scan of the handler body is conducted to identify blocking calls such as time.sleep, sync file operations, or synchronous database drivers.
- Context-sensitive gates are applied to verify findings against actual code state (file:line) before flagging issues.
- A comprehensive report is generated, distinguishing between legitimate idiomatic patterns and genuine architectural flaws.
FastAPI Code Review Setup
Installation and Configuration
To integrate this logic into your agent using Openclaw Skills, ensure the skill directory is accessible and your environment is configured for Python analysis.
# Navigate to your Openclaw Skills directory
cd path/to/skills
# Clone or add the fastapi-code-review skill
git clone [repository-url] fastapi-code-review
# Ensure the review-verification-protocol sibling skill is available
ls ../review-verification-protocol/SKILL.md
FastAPI Code Review Data Schema & Taxonomy
Contextual Reference Architecture
The skill utilizes a modular reference system to handle specific FastAPI domains:
| Reference Module | Domain Focus | Key Metrics |
|---|---|---|
routes.md |
Routing & Decorators | response_model, status_codes, HTTP methods |
dependencies.md |
DI Container | Depends(), yield cleanup, shared dependencies |
validation.md |
Schema/Pydantic | Pydantic models, HTTPException, 422 errors |
async.md |
Concurrency | Async def, blocking I/O, background tasks |
All findings are anchored to a specific file:line format to ensure traceability during the verification gate process.
FastAPI Code Review Advanced Features
Power-User Capabilities
- Verification Gates: A multi-step verification protocol that prevents false positives by requiring hard evidence (code snippets) before asserting a finding.
- Blocking I/O Detection: Intelligent scanning for sync calls within async handlers that can lead to event loop starvation.
- Idiomatic Pattern Recognition: Advanced logic that ignores standard FastAPI "false positives," such as async endpoints without await keywords or dict returns when response models are present.
- Cross-Skill Integration: Seamlessly works with the review-verification-protocol to standardize reporting across multi-agent workflows.
SKILL.md
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