Results for “pytest”

Use when writing asyncio Python code — async/await coroutines, concurrent I/O with asyncio.gather, task creation and cancellation, semaphore rate limiting, producer-consumer queues, async context managers, async generators, WebSocket servers, aiohttp web scraping, async database operations, run_in_executor for blocking calls, or testing async code with pytest-asyncio. Covers FastAPI and aiohttp patterns, synchronization primitives, timeout handling, and common pitfalls like event loop blocking and missing await.

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Use when creating a new feature development task — scaffolds a structured task file at .claude/tasks/{feature-name}.md with phased breakdown (Design, Implementation, Testing, Documentation), acceptance criteria, context preservation, and TaskCreate tracking. Activates on "create a feature task", "set up development tracking", "plan a feature implementation", or when preparing work for python-cli-architect or python-pytest-architect agents.

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Runs deterministic Python quality checks against a path or scope — formatting, linting, type checking, and typing-boundary policy. Use when checking or fixing code quality via prek, ruff, ty, pytest, or the check-typing-boundaries policy script. Reports results grouped by category; fixes only when explicitly requested.

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Applies and teaches Python 3.11+ modernization patterns with PEP citations. Use when reviewing or writing Python code to apply built-in generics (PEP 585), pipe unions (PEP 604), walrus operator (PEP 572), match-case (PEP 634), Self type (PEP 673), exception notes (PEP 678), StrEnum, tomllib, pytest-mock fixtures, Typer Annotated syntax, or Rich terminal output — or when refactoring legacy typing imports or elif chains to modern equivalents.

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Investigate pytest failures without treating tests or implementation as automatic authority. Use when diagnosing regressions, debugging test errors, or deciding whether to fix a test or code; trace the contract, product, fixture, and observation before choosing an evidence-backed correction.

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Activates on any Python task involving *.py files, uv, ruff, ty, pytest, or pyproject.toml — loads the shared Python 3.11+ standards and routes the task to the specialist skill that applies them: TDD, CLI, web, data, async, typing, packaging, publishing, documentation sites, test design, test-failure analysis, or stdlib-only constrained environments.

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Review pytest test effectiveness against authoritative contracts, relevant faults, behavior-preserving refactors, realistic boundaries, isolation, and diagnostics. Use for test-quality audits, coverage-gap reviews, regression-test changes, or mocking reviews; preserve existing project gates rather than inventing coverage targets.

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Configures pyproject.toml and Python packaging using PEP 517/518/621/660/723 standards. Use when creating or updating pyproject.toml, selecting a build backend (hatchling/setuptools/flit), configuring ruff, ty, mypy, pytest, or coverage tool sections, setting up dependency constraints or optional extras, defining CLI entry points, configuring pre-commit hooks, establishing src-layout directory structure, or preparing a package for PyPI publishing.

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Guides pytest test architecture for Python 3.11+ using behavioral contracts, risk, and maintenance-adjusted value. Use when choosing unit/integration/property/e2e boundaries, fixture strategy, coverage measurement, or mutation testing. Preserves existing project gates but does not invent test-pyramid ratios, coverage percentages, mutation-score targets, or tests for implementation detail.

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Pytest testing patterns for Python — fixtures, behavioral naming, behavior/risk-driven coverage, property-based testing with Hypothesis when useful, and mutation testing when justified. Use when writing tests, designing fixtures, configuring coverage, or applying parametrize, async testing, or property-based strategies.

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Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.

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Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.

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Django testing strategies with pytest-django, TDD methodology, factory_boy, mocking, coverage, and testing Django REST Framework APIs.

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单元测试实现时使用。覆盖 Jest / Vitest / pytest / JUnit 5 / Go testing。融合 AAA 模式 + 测试替身 + 参数化测试。

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实现单元测试 / 集成测试 / 契约测试时使用。覆盖 JUnit 5 / Jest / Vitest / pytest / Go testing / RSpec / xUnit + Pact 契约测试 + Testcontainers。融合测试金字塔实现 + 双倍替身 + AAA 模式。

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Read GitHub Actions CI for the current branch or open PR, triage failing checks, reproduce locally, and fix scoped issues until green. Use whenever the user mentions CI failures, red checks, failed pipeline, GitHub Actions, merge blocked, fix the build, babysit CI, gh run failed, ruff/ty errors, failing pytest, example run failed, or asks to check CI on this branch — even if they only paste a log snippet. Also use after implementing a feature when tests, lint, or typecheck might have broken.

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FastAPI web framework conventions: APIRouter with prefix/tags/dependencies, Pydantic v2 schemas (BaseModel, model_config, field_validator, model_validator), dependency injection with Depends, async SQLAlchemy session yielding, OAuth2PasswordBearer + JWT auth, lifespan context manager, pydantic-settings configuration, OpenAPI customization, and HTTPException error handling. Activated automatically by fastapi-plugin/stack.md. Use this skill to: - Structure FastAPI apps with per-feature APIRouter modules and a central app factory. - Write Pydantic v2 request and response schemas with proper validators. - Build a reusable Depends-based dependency chain for DB session, auth, and pagination. - Implement JWT-based authentication with get_current_user dependency. - Configure the app from environment variables via pydantic-settings. Do NOT use this skill for: - SQLAlchemy ORM model configuration and Alembic migrations — see fastapi-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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Flask web framework conventions: app factory pattern with create_app(), Blueprint registration with url_prefix, MethodView for class-based API views, Marshmallow schema validation, WTForms for HTML forms, Flask-Login for session auth, flask-jwt-extended for stateless API auth, Jinja2 template conventions, error handlers, and extension initialization. Activated automatically by flask-plugin/stack.md. Use this skill to: - Structure Flask applications with the app factory and per-feature Blueprints. - Validate JSON request data with Marshmallow and HTML forms with WTForms. - Implement session-based auth with Flask-Login or token auth with flask-jwt-extended. - Render Jinja2 templates safely or return JSON responses for API mode. - Register global error handlers for consistent error responses. Do NOT use this skill for: - SQLAlchemy ORM model patterns and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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Application and library conventions for plain Python projects (no web framework): src/ package layout, CLI tooling (argparse/click/typer), configuration management (pydantic-settings), logging setup, module design, entry points, and packaging conventions. Activated automatically by python-plugin/stack.md as a convention skill for the development phase. Use this skill to: - Organise Python application code in a src/ layout with proper package structure. - Build CLI tools with argparse, click, or typer and register them as console scripts. - Manage configuration from environment variables using pydantic-settings. - Set up structured logging for production-grade applications. - Package a Python project correctly with pyproject.toml. Do NOT use this skill for: - Language idioms (type hints, dataclasses, enums, match/case) — see python-foundation:python-conventions. - Package manager commands (ruff, mypy, pip/poetry/uv) — see python-foundation:python-tooling. - Testing patterns — see python-foundation:pytest-testing. - Web framework patterns (Django/FastAPI/Flask) — see those framework plugin skills.

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Modern Python idioms for any Python 3.10+ project: PEP 8 style, PEP 484/526 type hints, dataclasses, pathlib, enums, f-strings, structural pattern matching (match/case), context managers, exception handling, and null discipline. Stack-agnostic — referenced by every Python plugin in the marketplace. Use this skill to: - Write strongly-typed code with type hints on every function signature and class attribute. - Replace isinstance chains and magic constants with match/case and enums. - Use dataclasses or NamedTuple for value objects and data containers. - Handle resources safely with context managers and pathlib for all file paths. - Apply PEP 8 naming and structure conventions consistently. Do NOT use this skill for: - Framework-specific idioms (Django ORM, FastAPI routers, Flask blueprints — those live in framework plugin skills). - Packaging and dependency management — see python-foundation:python-tooling. - Testing patterns — see python-foundation:pytest-testing.

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Python project tooling: pyproject.toml as the single source of truth, package managers (pip, Poetry, uv, pipenv), virtual environments, lockfiles, ruff (lint + format), mypy (type checking), and console scripts / entry points. Stack-agnostic — referenced by every Python plugin in the marketplace. Use this skill to: - Read pyproject.toml (or requirements.txt / Pipfile) to learn which package manager and Python version the project uses. - Install, add, and remove dependencies with the project's package manager. - Run ruff for formatting and linting, mypy for type checking. - Define console scripts and entry points for CLI applications. Do NOT use this skill for: - Language idioms — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing. - Framework-specific tooling (django management commands, flask CLI) — see framework plugin skills.

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Django web framework conventions: app layout, settings split (base/local/production), URLconf with app namespacing, CBV and DRF ViewSets, DRF serializers and permissions, form validation, signals, Django admin registration, and middleware. Activated automatically by django-plugin/stack.md. Works alongside python-foundation:python-conventions and django-plugin:django-orm-patterns. Use this skill to: - Structure a Django project with multiple apps and correct URLconf hierarchy. - Write CBVs and DRF ViewSets with appropriate permissions and serializers. - Configure settings correctly for different environments. - Use signals for decoupled event handling between apps. - Register models in Django admin with useful list_display and search_fields. Do NOT use this skill for: - Django ORM model field finalization and migration patterns — see django-plugin:django-orm-patterns. - Python language idioms (type hints, dataclasses, enums) — see python-foundation:python-conventions. - Testing patterns — see python-foundation:pytest-testing.

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Python engineering skill for implementing, debugging, testing, packaging, typing, reviewing, and refactoring Python applications, scripts, APIs, automation, data processing, CLIs, and libraries. Use for .py files, pyproject.toml, requirements files, pytest, async Python, type hints, packaging, dependency management, and Python performance or reliability work.

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Use this skill when applying VCSDD to Python projects. Provides hypothesis property testing patterns, mutmut mutation testing, and pytest integration for the VCSDD pipeline.

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