python-engineering

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Production Python engineering patterns covering architecture, observability, testing, performance/concurrency, and core practices. Use when designing Python systems, implementing async/sync APIs, setting up monitoring, structuring tests, optimizing performance, or following Python best practices.

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Sourceyariv1025/skills

Added on2026-02-10

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SKILL.md Content

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Python Engineering

Production-grade patterns drawn from (not requiring) exemplar projects—FastAPI, Poetry, HTTPX, Celery, Black—as reference. Apply the patterns with any stack.

This skill is structured per the skill-creator: essentials and navigation in SKILL.md; detailed patterns in

references/

, loaded only when needed (progressive disclosure).

When to Read Which Reference

Need	Read
Package layout, DI, config, plugins	references/architecture.md
Logging, metrics, tracing, health	references/observability.md
Test structure, fixtures, CI	references/testing.md
Async safety, concurrency, throughput	references/performance-concurrency.md
GIL, packaging, I/O, reliability	references/core-practices.md

Quick Patterns

Structure: Separate routing, domain, persistence, integration; use dependency injection and typed config (any framework or stdlib).
Observability: Structured logs with correlation IDs; metrics (Prometheus/StatsD); health/readiness endpoints; distinguish operational vs programmer errors.
Tests: Pyramid (unit → integration → e2e); fixtures for isolation; parametrize edge cases; parallel CI; avoid hidden network/time.
Concurrency: Async for I/O; threads for blocking libs; processes for CPU-bound. One long-lived client per dependency; timeouts and backpressure everywhere.
Reliability: Context managers for cleanup; validate at boundaries; no blocking calls in async paths; idempotent retries and circuit breaking for I/O.

Quick Reference / Examples

Task	Approach
Inject dependencies	Use typed getters or framework DI; see references/architecture.md.
Structured logging	Log with extra dict and correlation ID; see references/observability.md.
Reuse HTTP client	One long-lived client per app; timeouts on every call. See references/performance-concurrency.md.
Resource cleanup	Use context managers; avoid blocking in async. See references/core-practices.md.
Isolate tests	Fixtures, parametrize, no hidden network/time. See references/testing.md.

Context manager (reliable cleanup):

python

with open(path) as f:
    process(f.read())
# or async: async with client: ...

Structured log / typed config: