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Found 130 Skills
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.
Galaxy testing with pytest and run_tests.sh - run/write unit, integration, API, selenium tests. Use for: test execution, test failures, pytest errors, ApiTestCase patterns, test fixtures, writing new tests, debugging test failures, test/integration, lib/galaxy_test/api tests. CRITICAL: Always use ./run_tests.sh, never pytest directly.
Generates pytest test suites with happy path, edge cases, error conditions, fixture scaffolding, mocks, async patterns. Triggers on: "generate tests", "write tests for", "test this function", "create test suite", "pytest for", "unit tests for", "mock strategy for".
Python testing with pytest, coverage, fixtures, parametrization, and mocking. Covers test organization, conftest.py, markers, async testing, and TDD workflows. Use when user mentions pytest, unit tests, test coverage, fixtures, mocking, or writing Python tests.
Create comprehensive unit tests, integration tests, and end-to-end tests using pytest for Python projects. Specializes in FastAPI testing with TestClient, async testing with pytest-asyncio, SQLModel/SQLAlchemy database testing, fixture generation, and test configuration setup. Use when you need test coverage, want to implement TDD/BDD, create test suites for functions or API endpoints, add edge case testing, or improve code quality with automated testing. Triggers include requests like "write tests for this module", "create pytest fixtures", "test this FastAPI endpoint", "setup pytest configuration", or "generate test file".
Guide for selecting and executing the correct pytest suites (unit, integration, redis, R2, routing rules, magic link) with environment setup and coverage expectations.
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.
This skill should be used when the user asks to "write pytest tests", "set up pytest best practices", "configure pytest", "write fixtures", or needs guidance on pytest testing patterns and project structure.
Performance and load testing patterns — k6 load tests, Locust stress tests, pytest execution optimization (xdist parallel, plugins), test type classification, and performance benchmarking. Use when writing load tests, optimizing test execution speed, or setting up pytest infrastructure.
Testing reference for Megatron Bridge — unit and functional test layout, tier semantics (L0/L1/L2/flaky), script conventions, running tests locally, adding/moving/disabling tests, and pytest conventions.
Creates pytest fixtures following project patterns including factory fixtures, async fixtures, and multi-layer organization. Use when setting up test fixtures, creating test data, organizing test utilities, or structuring conftest.py files. Works with Python test files, pytest configuration, and .py test utilities.
Captures quality metrics baseline (tests, coverage, type errors, linting, dead code) by running quality gates and storing results in memory for regression detection. Use at feature start, before refactor work, or after major changes to establish baseline. Triggers on "capture baseline", "establish baseline", or PROACTIVELY at start of any feature/refactor work. Works with pytest output, pyright errors, ruff warnings, vulture results, and memory MCP server for baseline storage.