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Found 143 Skills
Guide for selecting and executing the correct pytest suites (unit, integration, redis, R2, routing rules, magic link) with environment setup and coverage expectations.
Automated test generation, review, and execution for pytest-based projects. Auto-activates on keywords test, coverage, pytest, unittest, integration test, e2e, performance, benchmark, security testing. Routes to specialized testing workflows based on user intent.
Consult this skill for Python testing implementation and patterns. Use when writing unit tests, setting up test suites, implementing TDD, configuring pytest, creating fixtures, async testing, writing integration tests, mocking dependencies, parameterizing tests, setting up CI/CD testing. Do not use when evaluating test quality - use pensive:test-review instead. DO NOT use when: infrastructure test config - use leyline:pytest-config.
factory_boy test data generation specialist. Covers Factory, DjangoModelFactory, SQLAlchemyModelFactory, all field declarations (Faker, LazyAttribute, Sequence, SubFactory, RelatedFactory, post_generation, Trait, Maybe, Dict, List), batch creation, pytest integration, and Celery task testing patterns. USE WHEN: user mentions "factory_boy", "test factory", "DjangoModelFactory", "SQLAlchemyModelFactory", asks about "test data generation", "factory traits", "SubFactory", "factory fixtures". DO NOT USE FOR: pytest internals - use `pytest`; Django setup - use `pytest-django`; Hypothesis property testing - use `pytest` with Hypothesis
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.
Comprehensive Python engineering guidelines for writing production-quality Python code. This skill should be used when writing Python code, performing Python code reviews, working with Python tools (uv, ruff, mypy, pytest), or answering questions about Python best practices and patterns. Applies to CLI tools, AI agents (langgraph), and general Python development.
Sets up async tests with proper fixtures and mocks using pytest-asyncio patterns. Use when testing async functions, creating async fixtures, mocking async services, or handling async context managers. Covers @pytest_asyncio.fixture, AsyncMock with side_effect, async generator fixtures (yield), and testing async context managers. Works with Python async/await patterns, pytest-asyncio, and unittest.mock.AsyncMock.
Python 3.13+ development specialist covering FastAPI, Django, async patterns, data science, testing with pytest, and modern Python features. Use when developing Python APIs, web applications, data pipelines, or writing tests.
HTTP API testing for TypeScript (Supertest) and Python (httpx, pytest). Covers REST APIs, GraphQL, request/response validation, authentication, and error handling. Use when user mentions API testing, Supertest, httpx, REST testing, endpoint testing, HTTP response validation, or testing API routes.
Diagnose pytest or CI failures, identify root cause, and implement the minimal fix. Use when tests fail or CI reports errors.
Create factory fixture patterns for customizable test setup with variations. Use when building reusable test fixtures with multiple configurations, creating parameterizable mocks, or implementing test data builders. Works with pytest fixtures, mock objects, and test utilities. Enables DRY test setup while maintaining flexibility for edge cases.
Auto-activate for polyfactory, ModelFactory, DataclassFactory, MsgspecFactory, AttrsFactory, Use, register_fixture, pytest plugin, __random_seed__, or coverage(). Not for production seeding.