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.
npx agentmods add skills/jartan-llc/grimoire/async-python-patternsnpx skills add Jartan-LLC/grimoire --skill async-python-patternsgit clone --depth 1 https://github.com/Jartan-LLC/grimoireWhat 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.
| Model | Per session | Once 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 |
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.
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())
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.
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.
- yesterday First seen · 287 lines · 21 tokens per session scan A f368700e75aa
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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