awesome-cursor-rules-mdc is a generator that creates Cursor MDC rule files from structured library information, using semantic search and language models to gather and organize guidance. Developers use it to produce reusable rules for libraries in Cursor, and the catalogue includes 200 of those rules.
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 rules/sanjeed5/awesome-cursor-rules-mdc/asynciogit clone --depth 1 https://github.com/sanjeed5/awesome-cursor-rules-mdcWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/asyncio)<a href="https://agentmods.dev/rules/sanjeed5/awesome-cursor-rules-mdc/asyncio"><img src="https://agentmods.dev/badge/rules/sanjeed5/awesome-cursor-rules-mdc/asyncio.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.01523 | $0.01523 |
| Opus 5 | $0.00762 | $0.00762 |
| Sonnet 5 | $0.00305 | $0.00305 |
| Haiku 4.5 | $0.00152 | $0.00152 |
Grade A, and why
asyncio 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 5d ago.
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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
asyncio Best Practices
asyncio is Python's standard library for writing concurrent, I/O-bound code using async/await. This guide provides opinionated, actionable rules to ensure your asyncio applications are structured, performant, and easy to maintain.
1. Structured Concurrency: Entry Points and Task Management
Always use asyncio.run() as the single top-level entry point for your asynchronous application. Manage concurrent operations explicitly with asyncio.create_task(), ensuring all tasks are awaited or handled.
1.1. Top-Level Entry Point
Use asyncio.run() once to start your main coroutine. Enable debug mode during development for critical warnings about un-awaited coroutines and resource leaks.
❌ BAD: Manually managing event loops or calling loop.run_until_complete().
import asyncio
async def main():
print("Hello")
# Don't do this in application code; it's low-level and error-prone.
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
loop.close()
✅ GOOD: Use asyncio.run() with debug=True for development.
import asyncio
async def main():
print("Hello from main!")
await asyncio.sleep(0.1)
print("Goodbye from main!")
if __name__ == "__main__":
asyncio.run(main(), debug=True) # Always enable debug in dev!
1.2. Launching Concurrent Tasks
Use asyncio.create_task() to schedule coroutines to run concurrently. Always store a reference to the Task object to prevent it from being garbage collected prematurely, which can lead to silent failures and unhandled exceptions.
❌ BAD: Calling a coroutine without await or create_task().
import asyncio
async def fetch_data(url: str):
print(f"Fetching {url}...")
await asyncio.sleep(1) # Simulate network I/O
print(f"Finished fetching {url}")
return f"Data from {url}"
async def main():
# This coroutine will never run!
fetch_data("http://example.com/api/data")
print("Main finished without waiting for data.")
if __name__ == "__main__":
asyncio.run(main(), debug=True)
# Output: RuntimeWarning: coroutine 'fetch_data' was never awaited
# Main finished without waiting for data.
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.
- 5d ago First seen · 196 lines · 0 tokens per session scan A be97194d1758
asyncio is a cursor rule published in the GitHub repository sanjeed5/awesome-cursor-rules-mdc (3,571 stars, last pushed 3mo ago), licensed CC0-1.0. It adds 1,523 tokens to every session, about $0.0076 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 2026-08-30.
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httpx
Example context from httpx/urlparse.py (lines 153-163).
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