# Build Async Python Applications

Async Python code can be hard to structure, test, and debug. This skill provides patterns for asyncio, concurrency, timeouts, queues, rate limits, and async testing.

## Install

```bash
npx skillstore add activeinferenceinstitute/async-python-patterns
```

## Metadata

- Status: approved
- Slug: activeinferenceinstitute-async-python-patterns
- Skillstore revision: r1
- Version status: missing
- Tree hash: 7aa7bc68aeff4e4137e3c53805f0bd101c5121f476306f8362e2986f944b4cd6
- Author: ActiveInferenceInstitute
- GitHub username: ActiveInferenceInstitute
- License: MIT
- Repository: https://github.com/ActiveInferenceInstitute/Journal-Utilities/tree/main/.claude/skills/async-python-patterns
- Ref: d46e62089a6dfc9e14ea02eebd9b8e2ad2dfe1f4
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, network
- Quality score: 69
- Public page: https://skillstore.pages.dev/skills/activeinferenceinstitute-async-python-patterns
- Manifest: https://skillstore.pages.dev/api/skills/activeinferenceinstitute-async-python-patterns/manifest

## Capabilities

- Explains asyncio core concepts, including event loops, coroutines, tasks, futures, and async iterators.
- Shows practical patterns for await, gather, create\_task, wait\_for, and cancellation handling.
- Demonstrates async context managers, queues, semaphores, locks, and producer-consumer workflows.
- Provides examples for aiohttp scraping, simulated async database access, and WebSocket broadcasting.
- Covers performance practices such as connection pools, batching, rate limits, and executor usage.
- Includes testing guidance with pytest-asyncio and timeout assertions.

## Use Cases

- Modernize I/O Workflows: Convert sequential network, file, or database tasks into structured asyncio workflows with timeouts and cleanup.
- Design Concurrent Services: Plan task orchestration, queue processing, and rate limiting for APIs, workers, and real-time services.
- Review Async Code Quality: Check async code for missing awaits, blocking calls, weak cancellation handling, and unbounded concurrency.

## Prompt Templates

### Explain Asyncio Basics

```
Explain how event loops, coroutines, tasks, and await work in Python asyncio. Use simple examples and common mistakes.
```

### Refactor Sequential I/O

```
Review this sequential Python I/O workflow and propose an asyncio design using gather, timeouts, and clear error handling.
```

### Design Bounded Concurrency

```
Design an async worker pattern for many API requests. Include semaphores, batching, retries, cancellation, and resource cleanup.
```

### Audit Production Async Code

```
Audit this async Python service for blocking calls, leaked tasks, missing cancellation paths, unsafe database access, and test gaps.
```

## Limitations

- It is an educational Markdown skill, not an executable library or framework plugin.
- Several examples use simulated clients, placeholder URLs, or simplified data models.
- It does not provide full application scaffolds for FastAPI, aiohttp, or database drivers.
- It does not profile running code or verify production performance automatically.

## Best Practices

- Use bounded concurrency, timeouts, and cancellation handling for all external I/O.
- Prefer async context managers for resources that need deterministic cleanup.
- Test async success paths, failures, timeouts, and cancellation behavior explicitly.

## Anti Patterns

- Do not call blocking sleep, network, file, or database functions directly inside coroutines.
- Do not create unbounded tasks for large input sets without backpressure.
- Do not format untrusted values into SQL strings in async database code.

## Security Audit

- Audited at: 2026-07-07T17:51:09.77\+00:00
- Summary: The static command-execution findings are false positives from Markdown backticks and Python code fences, not executable shell or Ruby code. The hardcoded URLs and system-reconnaissance detections are examples or resource links. One semantic issue remains: the async database example teaches SQL f-string interpolation and should use parameterized queries.

## Stats

- Views: 328
- Downloads: 11
- Favorites: 0
- Popularity score: 0