Borrowing it
Nothing to install: this file belongs to loonghao/auroraview. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/loonghao/auroraview/main/.agents/skills/async-python-patterns/SKILL.mdgit clone --depth 1 https://github.com/loonghao/auroraviewWrote 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/skills/loonghao/auroraview/async-python-patterns)<a href="https://agentmods.dev/skills/loonghao/auroraview/async-python-patterns"><img src="https://agentmods.dev/badge/skills/loonghao/auroraview/async-python-patterns/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/loonghao/auroraview/async-python-patterns"><img src="https://agentmods.dev/badge/skills/loonghao/auroraview/async-python-patterns.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 285 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 368 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 576 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 5.1 | $0.00042 | $0.04838 |
| Opus 5.5 | $0.00017 | $0.01935 |
| Sonnet 5.5 | $0.00008 | $0.00968 |
| Haiku 4.5 | $0.00004 | $0.00484 |
Grade A, and why
async-python-patterns scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(url) # Also blocks! This is a copy
86% identical to async-python-patterns — 73 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
1 near-identical copy found in the catalogue:
- async-python-patterns — 78% identical, 466 lines differ
How it starts
The opening of the file, as written. The whole thing — 758 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.
When to Use This Skill
- Building async web APIs (FastAPI, aiohttp, Sanic)
- Implementing concurrent I/O operations (database, file, network)
- Creating web scrapers with concurrent requests
- Developing real-time applications (WebSocket servers, chat systems)
- Processing multiple independent tasks simultaneously
- Building microservices with async communication
- Optimizing I/O-bound workloads
- Implementing async background tasks and queues
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.
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.
- 1mo ago First seen · 758 lines · 42 tokens per session scan A ae0667be2431
async-python-patterns is a skill published in the GitHub repository loonghao/auroraview (47 stars, last pushed 19d ago), licensed MIT. It adds 42 tokens to every session and 4,838 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to async-python-patterns, differing in 73 lines, and is treated as a copy.
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