python-async-advisor

Analyze Python projects for race conditions, deadlocks, and inefficient asyncio patterns.

2|1|Updated Jan 25, 2026

Search Tags:#concurrency #race conditions #Python #asyncio #code-review #analyze #deadlocks

Ready to run

npx skills add https://github.com/jorgealves/agent_skills --skill python-async-advisor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: python-async-advisor
Source: https://github.com/jorgealves/agent_skills/tree/main/python-async-advisor
Command: npx skills add https://github.com/jorgealves/agent_skills --skill python-async-advisor

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill identifies race conditions, deadlocks, and inefficient async patterns in Python code, helping teams ship safer asynchronous applications.

Core Features & Use Cases

Static analysis: Detect race conditions, deadlocks, and inefficient await patterns in asyncio, aiohttp, and FastAPI code.
Guidance & recommendations: Provide actionable fixes and best practices for concurrent code.
Use Case: For a FastAPI service with multiple coroutines sharing state, the tool surfaces concurrency issues and suggests fixes.

Quick Start

Run the python-async-advisor against your Python project directory to surface concurrency issues in async code.

Frequently Asked Questions about python-async-advisor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect race conditions in asyncio Python code?▼

Detect race conditions in asyncio Python code by running static analysis on your project directory to identify unsafe shared state access and concurrent execution issues. The tool scans source files locally without transmitting code to external servers.

Can I analyze FastAPI or aiohttp projects for deadlocks and inefficient async patterns?▼

Yes, you can analyze FastAPI or aiohttp projects for deadlocks and inefficient async patterns. The tool scans codebases using these frameworks to surface concurrency issues and provides actionable fixes for safer asynchronous applications.

What is the best way to find shared state concurrency issues in a Python coroutine service?▼

The best way to find shared state concurrency issues in a Python coroutine service is static analysis that examines how multiple coroutines interact. The tool identifies problematic patterns in asyncio code and suggests best practice fixes.

Does this async Python analyzer transmit my source code or credentials externally?▼

No, this async Python analyzer does not transmit your source code or credentials externally. It performs local static analysis directly on your project directory, ensuring your codebase remains secure and private during the concurrency review.

How do I integrate async Python code review into a CI pipeline?▼

Integrate async Python code review into a CI pipeline by running the analyzer against your project directory during automated builds. It detects race conditions, deadlocks, and inefficient asyncio patterns, helping teams ship safer asynchronous applications.

Can I use this tool to refactor legacy Python async code for concurrency issues?▼

Yes, you can use this tool to refactor legacy Python async code for concurrency issues. The analyzer applies to legacy refactors by performing static analysis to identify race conditions, deadlocks, and inefficient await patterns in existing asynchronous codebases.