async-python-patterns

Overview of async-python-patterns skill

The async-python-patterns skill is a practical guide for designing and reviewing Python code that uses asyncio, async/await, and common concurrency patterns. It is best for backend engineers, API developers, scraper builders, and agent users who need to make I/O-heavy Python systems faster without introducing subtle blocking bugs.

What async-python-patterns is for

Use async-python-patterns when the real job is not “explain asyncio,” but “help me choose the right async design and write code that behaves correctly under load.” It is especially relevant for:

• async web APIs such as FastAPI, aiohttp, or Sanic
• services making many network or database calls
• background workers handling many independent I/O tasks
• WebSocket or real-time connection handling
• scrapers and crawlers with controlled concurrency

Best-fit users and jobs-to-be-done

This skill fits readers who already know Python basics and need help with decisions like:

• should this code path stay sync or go async?
• where should I use gather, queues, semaphores, or cancellation?
• how do I avoid blocking the event loop?
• how do I combine async I/O with CPU-heavy work safely?

If that is your problem, async-python-patterns gives more value than a generic “write async Python” prompt because it centers tradeoffs, not just syntax.

What makes this skill different

The strongest differentiator is decision guidance. The source explicitly frames when async is the right tool, when it is not, and highlights a key operational rule: keep a call path fully sync or fully async. That matters more than examples alone, because most adoption failures come from partial async refactors and hidden blocking calls.

When this skill is not the right fit

Skip async-python-patterns if your workload is mostly CPU-bound, your script is simple and low-concurrency, or you only need a quick syntax refresher. In those cases, plain synchronous Python, multiprocessing, or a smaller targeted prompt may be a better fit than committing to async architecture.

How to Use async-python-patterns skill

Install context for async-python-patterns

This skill lives in the wshobson/agents repository under plugins/python-development/skills/async-python-patterns. If your environment supports Skills installation, use:

npx skills add https://github.com/wshobson/agents --skill async-python-patterns

The repository evidence shows only one file, SKILL.md, so adoption is simple: there are no helper scripts, references, or rules to inspect first.

Read this file first

Start with:

• plugins/python-development/skills/async-python-patterns/SKILL.md

Because the skill is self-contained, you do not need to hunt through extra folders. Read it in this order for fastest decision-making:

1. When to Use This Skill
2. Sync vs Async Decision Guide
3. Core Concepts
4. pattern-specific sections relevant to your task

That order helps you avoid the most common mistake: choosing async before confirming async is justified.

What input the skill needs from you

async-python-patterns works best when you provide concrete execution context, not just “make this async.” Include:

• framework: FastAPI, aiohttp, plain asyncio, worker service, scraper
• workload type: network I/O, DB I/O, file I/O, WebSocket, mixed CPU + I/O
• concurrency shape: many independent tasks, producer-consumer, rate-limited calls
• constraints: throughput, latency, cancellation, retries, backpressure, memory
• blocking dependencies: sync ORM, sync SDKs, CPU-heavy transforms
• target output: new implementation, refactor plan, bug review, performance review

Without that context, the skill can still explain patterns, but it cannot choose the right one confidently.

Turn a rough goal into a strong prompt

Weak prompt:

“Use async-python-patterns to make this Python code faster.”

Stronger prompt:

“Use the async-python-patterns skill to refactor this FastAPI endpoint. It makes 12 external HTTP calls and 2 PostgreSQL queries per request. We expect 500 concurrent users. Keep request cancellation safe, limit outbound concurrency to avoid rate limits, and point out any blocking libraries that should stay sync or move to asyncio.to_thread().”

Why this works better:

• it defines the I/O profile
• it states concurrency pressure
• it gives architectural constraints
• it asks for both code and design review

Prompt template for async-python-patterns usage

Use this structure for reliable async-python-patterns usage:

• current code or pseudocode
• framework/runtime
• what is slow or failing
• whether calls are independent or ordered
• what can run concurrently
• what must be rate-limited
• whether cancellation/timeouts matter
• any sync libraries you cannot replace
• desired output format: code, review notes, migration steps, tests

Example:

“Apply the async-python-patterns skill. I have a scraper using requests in a loop across 2,000 URLs. I need a migration plan to asyncio with bounded concurrency, retries, timeout handling, and a note on whether parsing should remain in the event loop or be offloaded.”

Practical workflow for real projects

A good workflow is:

1. classify the workload as I/O-bound, CPU-bound, or mixed
2. use the skill's sync-vs-async guidance before rewriting anything
3. identify hidden blocking calls in the current path
4. choose one async pattern for the bottleneck, not five at once
5. ask for a minimal implementation first
6. then ask for production concerns: cancellation, limits, cleanup, errors

This staged approach keeps async-python-patterns for Backend Development practical instead of theoretical.

High-value topics to ask the skill about

The source clearly supports these use cases:

• event loop behavior and mental model
• async web API implementation
• concurrent I/O operations
• background tasks and queues
• WebSocket or real-time service patterns
• mixed async and CPU workloads using asyncio.to_thread()
• deciding between sync and async for a given code path

If your need falls outside those areas, the skill may be less useful than a framework-specific guide.

Repository-backed constraints to respect

The most important constraint surfaced by the source is architectural consistency: do not mix sync and async casually in one call path. The skill also signals that async is mainly for I/O-bound concurrency, not raw CPU acceleration.

That means async-python-patterns install is easy, but safe adoption depends on disciplined scope. If your stack still depends on blocking database drivers or sync-only SDKs, ask the skill for containment strategies instead of forcing a full async rewrite.

Common use cases where the skill adds real value

async-python-patterns is particularly helpful when you need to:

• convert sequential API calls into bounded concurrent tasks
• design async database access in a high-concurrency API
• add timeout and cancellation behavior to request fan-out
• separate CPU-heavy post-processing from async I/O
• build worker pipelines with queues and backpressure

These are the places where ordinary prompts often produce code that runs but fails operationally.

async-python-patterns skill FAQ

Is async-python-patterns good for beginners?

Yes, with a caveat. Beginners can use async-python-patterns to learn the event loop and async mental model, but it is most valuable once you have a concrete backend or I/O problem. If you are new to Python entirely, start with basic async/await syntax first, then use this skill for design decisions.

Do I need an async framework to use it?

No. The skill covers plain asyncio concepts as well as framework-aligned scenarios like FastAPI or aiohttp. It is still useful for scripts, workers, or scrapers if they perform many concurrent I/O operations.

When should I not use async-python-patterns?

Do not use async-python-patterns as your default answer for CPU-heavy workloads, tiny scripts, or codebases where async would add more complexity than benefit. If your work is mostly numerical processing or data transforms, ask for multiprocessing or thread-offloading guidance instead.

How is this different from a normal async prompt?

A normal prompt may generate valid async syntax but miss the adoption decision, hidden blocking calls, or concurrency limits. async-python-patterns skill is better when you need pattern selection, migration judgment, and backend-safe tradeoffs, not just a code sample.

Is async-python-patterns suitable for production backend work?

Yes, especially for async-python-patterns for Backend Development where correctness under concurrency matters. The value is in helping you structure request fan-out, task coordination, and I/O-heavy call paths safely. You still need framework-specific validation, testing, and observability in your own stack.

How to Improve async-python-patterns skill

Give workload shape, not just code

The fastest way to improve results from async-python-patterns is to describe the shape of the workload:

• “20 independent HTTP calls per request”
• “single DB transaction with ordered steps”
• “thousands of URLs with rate limits”
• “mixed image processing and network fetches”

Pattern choice depends on independence, ordering, and pressure. Code alone often hides that.

State what must not block

Many weak outputs come from missing dependency details. Tell the skill which libraries are sync-only, which calls may block, and what you cannot replace yet. That lets it recommend containment patterns such as asyncio.to_thread() instead of unsafe “async rewrites” that still block the event loop.

Ask for failure handling explicitly

If you care about production readiness, ask for:

• timeouts
• retries
• cancellation behavior
• cleanup on task failure
• bounded concurrency
• backpressure handling

These concerns materially change the code. If you omit them, first-pass output may look correct but behave poorly under real traffic.

Request a pattern choice before code generation

A strong way to use async-python-patterns guide style output is:

“First choose the right async pattern and explain why. Then provide code.”

This avoids premature code generation and surfaces whether you should use simple awaits, gather, semaphores, queues, background workers, or thread offloading.

Improve first-pass output through iteration

After the first response, refine with focused follow-ups such as:

• “Now make outbound calls concurrency-limited to 10.”
• “Show where cancellation propagates.”
• “Mark any sync boundaries that should remain sync.”
• “Separate CPU-bound parsing from I/O-bound fetches.”
• “Add notes for FastAPI request lifecycle integration.”

This turns async-python-patterns usage into a practical design loop instead of a one-shot answer.

Common failure modes to watch for

Watch for these issues in generated output:

• wrapping blocking code in async def without making it non-blocking
• using unbounded concurrency
• mixing sync and async database access in one flow
• treating CPU-bound work as if async will speed it up
• missing timeout or cancellation paths
• overcomplicating simple scripts that should stay sync

If you see any of these, ask the skill to simplify or to justify the async architecture from first principles.

Best way to evaluate whether the skill helped

A good async-python-patterns install decision is not based on how much code it generates. Judge it by whether it helps you answer:

• should this become async at all?
• where are the true bottlenecks?
• what must remain sync or move to a thread/process?
• what concurrency controls are necessary?
• what operational risks appear after the refactor?

If the skill improves those decisions, it is doing useful work beyond a repo skim or generic prompt.