Skill UI

Automates guidance for configuring test retries in automation suites, walking through setup, best practices, and frameworks such as Jest or pytest so teams can stabilize flaky runs.

This skill automates the creation of comprehensive test suites for any codebase. Utilizing advanced AI capabilities, it analyzes function signatures, automatically identifies potential edge cases, generates meaningful assertions, and produces necessary mock data and fixture files. It is essential for developers needing robust unit tests, increasing code coverage, and adopting Test-Driven Development (TDD) workflows across various frameworks like Jest or pytest.

This tool helps developers run comprehensive test suites using pytest and generates detailed code coverage reports. It identifies specific lines of code that have not been covered by tests, guiding developers to improve test quality until 100% coverage is achieved. Use it to ensure robust and reliable software.

Applies proven testing patterns (AAA, Given-When-Then, builders, spies, doubles) to help write maintainable unit, integration, and end-to-end suites, covering structuring, naming, TDD/BDD, and mocks across frameworks like Jest, Vitest, or pytest.

This guide provides a comprehensive setup for Continuous Integration (CI) and Continuous Deployment (CD) for SerpApi integrations. It outlines using fixture-based unit tests via GitHub Actions for Pull Requests (preventing API credit consumption) and controlled live integration tests on the main branch. This ensures robust and reliable search result parsing validation.

This skill automatically generates comprehensive unit tests based on provided source code. It supports multiple programming languages (Python, JavaScript, Java, Go, Rust) and popular testing frameworks (pytest, Jest, JUnit). Use it to improve code coverage, test boundary conditions, and validate error handling efficiently, saving manual testing time.

This meta-skill executes three crucial quality gates—ruff (linting), mypy (type checking), and pytest (unit testing)—in parallel over the staged file differences. It aggregates the results to provide a single, definitive BLOCK or APPROVE verdict, ensuring code readiness and mirroring the strict checks enforced by CI/CD pipelines before local commits.

An internal utility designed for deep analysis of Python tracebacks. It performs Python-specific root-cause checks, provides guidance for minimal reproducible examples using pytest, and identifies precise targets for implementing defensive coding practices (like guard clauses or schema validation). Ideal for advanced debugging scenarios.

This skill generates comprehensive, production-grade unit and integration tests using the Python pytest framework. It covers advanced patterns such as fixtures (with scope and teardown), parametrization, custom markers, mocking external dependencies, and best practices for writing robust and maintainable test suites. Use this when implementing detailed test coverage for Python code.

A comprehensive guide to implementing Test-Driven Development (TDD) for Django projects. This skill covers the full Red-Green-Refactor cycle, utilizing pytest, pytest-django, and factory_boy to write robust unit and integration tests for Django models, views, and DRF APIs. Essential for developers aiming to build large-scale, high-quality Python applications.

A comprehensive, multi-phase checklist for ensuring the quality, security, and deployment readiness of Django applications. It covers linting (ruff, mypy), unit testing (pytest), database migrations, security audits (bandit, pip-audit), and performance checks, making it an essential quality gate for CI/CD pipelines before merging or deploying to production.

Master professional Python testing practices using the pytest framework. This guide covers Test-Driven Development (TDD) methodology, advanced features like fixtures (with different scopes), parametrization, assertion techniques, and achieving high code coverage, ensuring robust and maintainable software.