AgentSkillsCN

pytest-dev

世界级的 Python pytest 工程师：编写/重构测试、修复不稳定问题、设计 fixture/标记、增加覆盖率、加速测试套件（收集/运行时），并优化 CI（GitHub Actions 分片、xdist 并行化、缓存）。当您被问及 pytest 最佳实践、pytest 9.x 新特性（子测试、严格模式、TOML 配置）、pytest 插件（xdist/cov/asyncio/mock/httpx），或测试性能/CI 调优时，可使用此技能。

SKILL.md
--- frontmatter
name: pytest-dev
description: "World-class pytest engineer for Python: write/refactor tests, fix flakiness, design fixtures/markers, add coverage, speed up suites (collection/runtime), and optimize CI (GitHub Actions sharding, xdist parallelism, caching). Use when asked about pytest best practices, pytest 9.x features (subtests, strict mode, TOML config), pytest plugins (xdist/cov/asyncio/mock/httpx), or test performance/CI tuning."

pytest-dev

Produce high-signal, low-flake, fast pytest suites and CI configs, with an explicit focus on measurable wins (runtime, flake rate, coverage quality).

Default workflow

  1. •Classify the tests
    • •Unit: pure functions, no I/O (preferred)
    • •Integration: DB/filesystem/multiprocess, slower but valuable
    • •System/E2E: external services or UI, keep minimal and well-gated
  2. •Identify boundaries
    • •Time/clock, randomness, network, filesystem, DB, env vars, global state
  3. •Pick the lightest seam
    • •Prefer fakes/stubs over deep mocks; prefer dependency injection over patching internals
  4. •Make it deterministic
    • •Control time, seeds, tmp dirs; avoid order dependencies
  5. •Measure before optimizing
    • •Collection time vs runtime; quantify with --durations + a single baseline
  6. •Harden for CI
    • •Enforce marker discipline, strict config, timeouts, isolation for parallel

Quick commands

Use python3 by default. If the project uses uv, prefer uv run python.

  • •Smallest repro: python3 -m pytest path/to/test_file.py -q
  • •First failure only: python3 -m pytest -x --maxfail=1
  • •Find slow tests: python3 -m pytest --durations=20 --durations-min=0.5
  • •Emit JUnit for CI: python3 -m pytest --junitxml=reports/junit.xml
  • •Parallelize on one machine (xdist): python3 -m pytest -n auto --dist load

Optimization playbook (high ROI)

  1. •Reduce collection scope (testpaths, norecursedirs, avoid importing heavy modules at import time).
  2. •Fix fixture scoping (move expensive setup up-scope; ensure isolation).
  3. •Eliminate sleeps and retries (poll with timeouts; mock time).
  4. •Parallelize safely (xdist; isolate worker resources: tmp/db ports).
  5. •Shard in CI (split test files by historical timings; keep shards balanced).

Use the bundled references

Read these when needed (keep SKILL.md lean):

  • •references/pytest_core.md: fixtures, markers, parametrization, strict mode, TOML config, subtests (pytest 9.x).
  • •references/plugins.md: plugin selection + usage patterns.
  • •references/performance.md: collection/runtime profiling and speedups.
  • •references/ci_github_actions.md: sharding, artifacts, caching, concurrency.

Use the bundled scripts

  • •scripts/junit_slowest.py: report slowest tests/files from JUnit XML.
  • •scripts/junit_split.py: split test files into N shards using JUnit timings.
  • •scripts/run_pytest_filelist.py: run pytest for a list of test files.

Quality gates

  • •Tests pass in a clean environment (no hidden dependency on local state).
  • •No network/time dependency without explicit control.
  • •Parallel-safe or explicitly marked/serialized.
  • •CI emits machine-readable artifacts when relevant (JUnit, coverage).