async-python-patterns

A guide to Python's asyncio system for running many waiting tasks concurrently, using async and await. It explains event loops, coroutines, and when to use asynchronous work, threads, or separate processes.

In plain words
What is it for?
Use it when building high-concurrency web APIs or programs that make many network or database requests, and when deciding how to handle CPU-heavy work.
Why use it?
It helps avoid blocking programs and reduces confusion when choosing between synchronous code, asynchronous code, threads, and processes.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/jartan-llc/grimoire/async-python-patterns
Any agent
npx skills add Jartan-LLC/grimoire --skill async-python-patterns
Clone the repo
git clone --depth 1 https://github.com/Jartan-LLC/grimoire

Made for: Claude Code, Codex.

Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,654 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00021 $0.01654
Opus 5 $0.00010 $0.00827
Sonnet 5 $0.00004 $0.00331
Haiku 4.5 $0.00002 $0.00165

Measured yesterday against content hash f368700e75aa, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

async-python-patterns scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/pythonica/skills/async-python-patterns/SKILL.md · 287 lines

How it starts

The opening of the file, as written. The whole thing — 287 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Async Python Patterns

Comprehensive guidance for implementing asynchronous Python applications using asyncio, concurrent programming patterns, and async/await for building high-performance, non-blocking systems.

Sync vs Async Decision Guide

Before adopting async, consider whether it's the right choice for your use case.

Use Case Recommended Approach
Many concurrent network/DB calls asyncio
CPU-bound computation multiprocessing or thread pool
Mixed I/O + CPU Offload CPU work with asyncio.to_thread()
Simple scripts, few connections Sync (simpler, easier to debug)
Web APIs with high concurrency Async frameworks (FastAPI, aiohttp)

Key Rule: Stay fully sync or fully async within a call path. Mixing creates hidden blocking and complexity.

Core Concepts

1. Event Loop

The event loop is the heart of asyncio, managing and scheduling asynchronous tasks.

Key characteristics:

  • Single-threaded cooperative multitasking
  • Schedules coroutines for execution
  • Handles I/O operations without blocking
  • Manages callbacks and futures

2. Coroutines

Functions defined with async def that can be paused and resumed.

Syntax:

async def my_coroutine():
    result = await some_async_operation()
    return result

3. Tasks

Scheduled coroutines that run concurrently on the event loop.

4. Futures

Low-level objects representing eventual results of async operations.

5. Async Context Managers

Resources that support async with for proper cleanup.

6. Async Iterators

Objects that support async for for iterating over async data sources.

Quick Start

import asyncio

async def main():
    print("Hello")
    await asyncio.sleep(1)
    print("World")

# Python 3.7+
asyncio.run(main())

Fundamental Patterns

Pattern 1: Basic Async/Await

import asyncio

async def fetch_data(url: str) -> dict:
    """Fetch data from URL asynchronously."""
    await asyncio.sleep(1)  # Simulate I/O
    return {"url": url, "data": "result"}

async def main():
    result = await fetch_data("https://api.example.com")
    print(result)

asyncio.run(main())

Read the full file on GitHub · 287 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 287 lines · 21 tokens per session scan A f368700e75aa

Subscribe to this mod's changes

async-python-patterns is a skill published in the GitHub repository Jartan-LLC/grimoire (2 stars, last pushed 14d ago), licensed MIT. It adds 21 tokens to every session and 1,654 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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