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python-async-best-practices
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1.0.0
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SKILL.md
Python Async Best Practices
Guidelines for writing and reviewing asyncio code. 5 rules in 1 category, prioritized by impact.
A rule match is a signal, not a verdict. These failures typically pass single-request smoke tests and surface under load — weigh the rule against the code's real concurrency profile.
Quick-reference lines are triggers, not licenses: before applying a rule as a review finding or a transformation, open the rule file and check its counter-signal — the marker-opened paragraph (When ... / Scope: / Keep ...) saying when NOT to apply it.
When to Apply
Writing or reviewing async def code, task spawning, or streaming consumers
Auditing an async service for blocking calls or unbounded fan-out
Debugging hangs, orphaned tasks, or unraisable warnings at teardown
Impact Levels
HIGH — stalls or silent failures affecting every task on the loop. Fix when found.
MEDIUM — resource and cleanup discipline; apply to new code and code under review.
Rule Categories by Priority
Priority
Category
Impact
Prefix
1
Concurrency & Async
MEDIUM-HIGH
async-
Quick Reference
Concurrency & Async (async-)
async-no-blocking-event-loop — No sync I/O, sleeps, or heavy CPU in async def; asyncio.to_thread for blocking calls
async-own-your-tasks — TaskGroup by default; hold references and cancel-then-drain longer-lived tasks
async-bound-concurrency — Semaphore/queue bounds when fan-out scales with input size
async-generator-cleanup — aclosing() / explicit aclose() when leaving an async generator early
async-preserve-cancellation — Cancellation is control flow: cleanup, then re-raise; never a logged failure
Related Skills
python-best-practices — production Python generally; its error-specific-exceptions rule covers broad-catch hygiene and points here for asyncio cancellation depth.
python-pytest — determinism-sync-not-sleep applies these ideas to concurrency tests.