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/event4u-app/agent-config/async-python-patternsnpx skills add event4u-app/agent-config --skill async-python-patternsgit clone --depth 1 https://github.com/event4u-app/agent-configWhat 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 | $0.00041 | $0.01924 |
| Opus 5 | $0.00020 | $0.00962 |
| Sonnet 5 | $0.00008 | $0.00385 |
| Haiku 4.5 | $0.00004 | $0.00192 |
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 2d 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 — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
async-python-patterns
Decision framework for picking the right Python asyncio primitive. The pattern cookbook lives upstream (links in § Provenance) — this skill is the predicate, not the recipe library. Sunset-policy compliant: the 600+ lines of language-specific cookbook stay in authoritative Python docs.
When to use
- Designing a new async I/O-bound service (FastAPI, aiohttp, async DB client).
- Reviewing a diff that introduces
asyncio.gather,asyncio.create_task,TaskGroup,as_completed, orwait_for. - Mixing sync and async code (calling sync libs from async context, or vice versa).
- Diagnosing event-loop blocking, never-awaited warnings, or cancellation leaks.
Do NOT use when:
- The work is CPU-bound — async will not help; route to multiprocessing or threadpool.
- The runtime is not Python — read the host runtime's concurrency guide.
- The fix is a single missing
await— read the upstream tutorial directly.
Decision framework
Step 1 — Verify async is the right tool
Workload is:
I/O-bound, many concurrent waits → async fits (network, disk, IPC).
CPU-bound (parsing, math, crypto) → async is wrong; use ProcessPoolExecutor.
Mixed → async shell + run_in_executor for CPU bursts.
Single sequential call → don't introduce async; sync is simpler.
Step 2 — Pick the concurrency primitive
Run N independent coroutines, ALL must complete:
Same trust level, exceptions cancel siblings → asyncio.TaskGroup (3.11+; preferred).
Pre-3.11 OR exceptions must NOT cancel peers → asyncio.gather(*, return_exceptions=...).
Run N coroutines, react to results as they finish:
→ asyncio.as_completed (yields completed futures in finish order).
Run N coroutines, race to first success / failure:
→ asyncio.wait(..., return_when=FIRST_COMPLETED) + cancel pending.
Schedule fire-and-forget background work:
→ asyncio.create_task + keep a strong reference (else GC eats it).
Forgetting the reference is the #1 silent-failure source.
Bound the wait time:
→ asyncio.wait_for(coro, timeout=...) → raises TimeoutError on expiry.
→ asyncio.timeout(...) context manager (3.11+; preferred when many awaits share a deadline).
Bound concurrency (rate-limit, connection pool):
→ asyncio.Semaphore(n); acquire around the awaitable.
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.
- 2d ago First seen · 160 lines · 41 tokens per session scan A e5f2ed0ddfa9
async-python-patterns is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed 2d ago), licensed MIT. It adds 41 tokens to every session and 1,924 once invoked, about $0.0002 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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