# Ahgraber Skills Python Concurrency Performance > Python Concurrency and Performance - Skill: `tomevault-io/ahgraber-skills-python-concurrency-performance` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/ahgraber-skills-python-concurrency-performance` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/ahgraber-skills-python-concurrency-performance/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/ahgraber-skills-python-concurrency-performance --- # Python Concurrency and Performance ## Overview Correct concurrency starts with matching the model to the workload, not the developer's preference. This skill encodes defaults for model selection, cancellation/deadline behavior, and lifecycle safety—prioritizing explicit control over implicit magic. Treat these recommendations as preferred defaults. When project constraints demand deviation, call out tradeoffs and compensating controls. ## When to Use - Selecting between `asyncio`, `threading`, `multiprocessing`, or `concurrent.futures` - Propagating deadlines or cancellation through async call chains - Bounding fan-out, backpressure, or semaphore-guarded concurrency - Diagnosing race conditions, deadlocks, or priority inversion - Profiling throughput bottlenecks before and after optimization - Verifying no task or thread leaks on shutdown or lifecycle transitions ### When NOT to Use - Pure CPU-bound numeric work better served by NumPy/C extensions - Single-threaded scripting with no concurrent I/O - Distributed systems coordination (use a workflow/orchestration skill instead) ## Quick Reference - Choose the concurrency model by workload profile (I/O-bound → asyncio/threads; CPU-bound → multiprocessing). - Keep cancellation and cleanup explicit—never rely on garbage collection to close resources. - Bound fan-out and backpressure with semaphores or queue limits; unbounded spawning invites OOM. - Measure before optimizing; re-measure after every change to confirm the win. - Verify no task/thread leaks on any lifecycle-sensitive change (startup, shutdown, reconnect). ## Common Mistakes - **Defaulting to threads for I/O-bound work** — `asyncio` avoids thread-safety bugs entirely for network I/O; threads add synchronization overhead for no gain. - **Ignoring cancellation propagation** — a cancelled parent that doesn't cancel children leaks tasks and holds connections open. - **Unbounded `gather` / `submit` calls** — spawning thousands of tasks without a semaphore or bounded executor starves the event loop or exhausts OS threads. - **Optimizing without profiling** — guessing at bottlenecks leads to complex code that solves the wrong problem; always profile first. - **Missing shutdown verification** — tests that don't assert clean shutdown mask slow resource leaks that surface only in production under load. ## Scope Note - Treat these recommendations as preferred defaults for common cases, not universal rules. - If a default conflicts with project constraints or worsens the outcome, suggest a better-fit alternative and explain why it is better for this case. - When deviating, call out tradeoffs and compensating controls (tests, observability, migration, rollback). ## Invocation Notice - Inform the user when this skill is being invoked by name: `python-concurrency-performance`. ## References - `references/concurrency-models.md` - `references/deadlines-cancellation-lifecycle.md` - `references/leak-detection.md` --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/ahgraber) — claim your Tome and manage your conversions.