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Found 40 Skills
Testing use cases and application services: use case testing with mocked gateways, DTO testing, application exception testing, orchestration testing, mocking at adapter boundaries. Coverage target: 85-90%. Use when: Testing use cases, testing application services, testing DTOs and data transformation, testing error handling in use cases, mocking external dependencies at layer boundaries.
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".
Эксперт Python разработки. Используй для Python best practices, async, typing и ecosystem.
Python development with ruff, mypy, pytest - TDD and type safety
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.
Python/pytest TDD specialist for test-driven development workflows. Use when writing tests, auditing test quality, running pytest, or generating test reports. Integrates with uv and pyproject.toml configuration.
pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.
Extract raw price dataframe for a test case
pytest, data validation, Great Expectations, and quality assurance for data systems
Python 开发规范，包含 PEP 8 风格、类型注解、异常处理、测试规范等
Sets up Python development environment using UV for fast dependency management. Configures virtual environment, dependencies, testing (pytest), linting/formatting (ruff), and type checking (mypy). ALWAYS use UV - NEVER use pip directly. Use when starting work on Python projects, after cloning Python repositories, setting up CI/CD for Python, or troubleshooting Python environment issues.
Python development guidance with code quality standards, error handling, testing practices, and environment management. Use when writing, reviewing, or modifying Python code (.py files) or Jupyter notebooks (.ipynb files).