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Provides Django testing strategies combining pytest-django, factory_boy, DRF helpers, mocking, and coverage so teams can adopt TDD workflows for models, views, serializers, and APIs.

A comprehensive expert resource for building high-performance, async-first APIs using FastAPI, SQLAlchemy 2.0, and Pydantic V2. Covers advanced topics like microservices architecture, WebSockets, advanced security (OAuth2, RBAC), robust testing strategies (pytest), performance optimization, and modern DevOps practices (Docker, Kubernetes). Ideal for mastering scalable, production-ready Python web services.

Automatically analyzes source files, detects or honors the requested testing framework, and produces comprehensive unit tests with mocks, edge cases, and assertions for Jest, pytest, JUnit, and other frameworks whenever users ask to generate tests.

Guides setting up modern Python projects with uv, ruff, ty, pytest, prek, and security checks, covering initialization, pyproject configuration, dependency and tool management, quick-start scripts, full project layout, and migrations from legacy tooling.

This skill coordinates the complex parallel execution of end-to-end, integration, and unit tests across multiple frameworks (e.g., Jest, Playwright, pytest) and CI/CD environments. It manages test splitting, worker allocation, tiered execution strategies, and aggregates results into unified reports. Ideal for optimizing the testing lifecycle and ensuring robust quality assurance in large-scale, multi-component applications. Key functions include intelligent retry logic for flaky tests and building CI pipelines (e.g., GitHub Actions matrix).

This skill auto-activates when someone mentions pytest test generator, offering guided test automation workflows, producing ready-to-use code/configuration, and validating outputs for unit, integration, and mocking scenarios.

Showcases modern Python practices, covering strict 3.12 type hints, dataclasses vs Pydantic decisions, asyncio/httpx patterns, src-layout projects, pyproject tool configs, virtual env workflows, and pytest guidance for reliable codebases.

A comprehensive skill set for building robust, type-safe, and highly performant Python 3.11+ applications. This specialist enforces best practices by generating type-annotated code, utilizing async/await patterns, structured dataclasses, and comprehensive error handling. The workflow mandates rigorous validation using tools like mypy (strict mode), black, ruff, and extensive pytest test suites to ensure code quality and reliability.

Provides a comprehensive pytest-focused testing strategy for Python applications, covering TDD cycles, fixtures, parametrization, coverage tools, and test infrastructure setup for reliable suites.

This comprehensive guide provides robust strategies for testing Python code using industry best practices. It covers essential techniques like pytest, fixtures, mocking, parameterization, and implementing Test-Driven Development (TDD). Use this skill when writing unit tests, setting up complex test suites, performing integration testing, or establishing continuous testing infrastructure in CI/CD pipelines.

This comprehensive guide assists developers in adopting Test-Driven Development (TDD) practices. It covers generating unit tests for various languages (Python, JS, Java), analyzing test coverage gaps across multiple frameworks (Jest, Pytest, JUnit), and guiding the iterative Red-Green-Refactor workflow. Use this guide when generating stubs, improving test coverage, or adhering to spec-first development methodologies.

Comprehensive TDD workflow guidance covering the Red/Green/Refactor rhythm, Arrange-Act-Assert test structure, Jest/Vitest, pytest, and Go patterns, coverage rules, mocking advice, and anti-pattern warnings to keep teams disciplined across languages.