Skill UI

Guides performance engineers through defining, coding, and validating benchmark suites for load, stress, and performance monitoring scenarios, following established best practices and standards.

Blueprint for designing comprehensive test suites, guiding when to add unit, integration, or E2E tests, selecting frameworks, managing data/mocks, and validating coverage and reliability.

A Playwright-powered guide that bundles organized test suites, Page Object Models, CI/CD workflows, artifact capture, and strategies for stabilizing flaky scenarios so teams ship reliable end-to-end coverage.

This skill provides comprehensive guidance on building reliable, fast, and maintainable end-to-end (E2E) test suites. It ensures code quality by validating critical user journeys and catching regressions in complex workflows before deployment. Essential for setting up robust CI/CD pipelines, cross-browser compatibility, and establishing rigorous QA standards.

Auto-activated guidance for flaky test detection within automated testing workflows, covering best practices, mocking, and configuration to stabilize unit and integration suites.

Provides automated guidance and code generation for Jest-based test automation, covering unit, integration, mocking, and configuration patterns to ensure production-ready suites.

Manages snapshot tests for Jest, Vitest, Playwright, and Storybook by analyzing failures, distinguishing intentional changes from regressions, and selectively updating only the affected snapshots to keep suites accurate.

Provides automated guidance for setting up Mocha tests, covering patterns, configurations, mocking, and validation to produce production-ready test suites.

Enables Claude to orchestrate complex test workflows via the test-orchestrator plugin, defining dependencies, parallel execution, and smart selection so only affected suites run during CI/CD or manual optimization requests.

This skill specializes in designing, optimizing, and evaluating prompts for Large Language Models (LLMs). It allows users to generate optimized prompt templates, structured output schemas (e.g., JSON mode, function calling), and comprehensive test suites. Use this skill when building new LLM applications, implementing complex reasoning patterns like Chain-of-Thought, defining system guardrails, or building robust evaluation frameworks to measure and improve model performance.

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.