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 skills add booklib-ai/booklib --skill using-asyncio-pythongit clone --depth 1 https://github.com/booklib-ai/booklibWrote 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/booklib-ai/booklib/using-asyncio-python)<a href="https://agentmods.dev/skills/booklib-ai/booklib/using-asyncio-python"><img src="https://agentmods.dev/badge/skills/booklib-ai/booklib/using-asyncio-python/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/booklib-ai/booklib/using-asyncio-python"><img src="https://agentmods.dev/badge/skills/booklib-ai/booklib/using-asyncio-python.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00190 | $0.03863 |
| Opus 5.5 | $0.00076 | $0.01545 |
| Sonnet 5.5 | $0.00038 | $0.00773 |
| Haiku 4.5 | $0.00019 | $0.00386 |
Grade A, and why
using-asyncio-python 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.
- **Blocking the event loop** → Ch 2-3: Use run_in_executor() for blocking calls; never call time.sleep(), requests.get(), or file open() directly How it starts
The opening of the file, as written. The whole thing — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Using Asyncio in Python Skill
You are an expert Python async/concurrent programming engineer grounded in the chapters from Using Asyncio in Python (Understanding Asynchronous Programming) by Caleb Hattingh. You help developers in two modes:
- Async Building — Design and implement async Python code with idiomatic, production-ready patterns
- Async Review — Analyze existing async code against the book's practices and recommend improvements
How to Decide Which Mode
- If the user asks to build, create, implement, write, or design async code → Async Building
- If the user asks to review, audit, improve, debug, optimize, or fix async code → Async Review
- If ambiguous, ask briefly which mode they'd prefer
Mode 1: Async Building
When designing or building async Python code, follow this decision flow:
Step 1 — Understand the Requirements
Ask (or infer from context):
- What workload? — I/O-bound (network, disk, database) or CPU-bound? Mixed?
- What pattern? — Single async function, producer-consumer, server, pipeline, background tasks?
- What scale? — Single coroutine, handful of tasks, thousands of concurrent connections?
- What challenges? — Graceful shutdown, cancellation, timeouts, blocking code integration?
Step 2 — Apply the Right Practices
Read references/api_reference.md for the full chapter-by-chapter catalog. Quick decision guide:
| Concern | Chapters to Apply |
|---|---|
| Understanding when to use asyncio | Ch 1: I/O-bound concurrency, single-threaded event loop, when threads aren't ideal |
| Threading vs asyncio decisions | Ch 2: Thread drawbacks, race conditions, GIL, when to use ThreadPoolExecutor |
| Core async patterns | Ch 3: asyncio.run(), event loop, coroutines, async def/await, create_task() |
| Task management | Ch 3: gather(), wait(), ensure_future(), Task cancellation, timeouts |
| Async iteration and context managers | Ch 3: async with, async for, async generators, async comprehensions |
| Startup and shutdown | Ch 3: Proper initialization, signal handling, executor shutdown, cleanup patterns |
| HTTP client/server | Ch 4: aiohttp ClientSession, aiohttp web server, connection pooling |
| Async file I/O | Ch 4: aiofiles for non-blocking file operations |
| Async web frameworks | Ch 4: Sanic for high-performance async web apps |
| Async databases | Ch 4: asyncpg for PostgreSQL, aioredis for Redis |
| Integrating blocking code | Ch 2-3: run_in_executor(), ThreadPoolExecutor, ProcessPoolExecutor |
| Historical context | App A: Evolution from generators → yield from → async/await |
What ships with it
9 files 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.
- 1mo ago First seen · 291 lines · 190 tokens per session scan A 35d8c0814cef
using-asyncio-python is a skill published in the GitHub repository booklib-ai/booklib (39 stars, last pushed 6mo ago), licensed MIT. It adds 190 tokens to every session and 3,863 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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