Skill UI

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).

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.

Orchestrates multiple agents for complex workflows, decomposing feature, review, refactor, test, or documentation requests into parallel subtasks tracked via cc-mirror and TodoWrite for live visibility.

This skill enables the coordination of multiple specialized AI agents within a single session. It is designed for complex, multi-domain tasks such as comprehensive code reviews, security audits, or full-stack feature implementations. It coordinates expertise across various domains (backend, frontend, testing, architecture) to provide holistic analysis and actionable recommendations.

A sophisticated AI tool that coordinates multiple specialized agents (e.g., Security, Architecture, Performance) to provide comprehensive, multi-perspective code assessments. It goes beyond single-point reviews by managing context, running hybrid execution workflows, resolving conflicts, and synthesizing unified, actionable reports for high-quality code assurance.

Guides users through setting up monitoring for data pipelines, covering ETL, transformation, orchestration, streaming, and validation with best-practice code and configuration.

Automated assistance for pyspark transformer work within data pipelines, activating when you request transformer patterns or best practices and covering ETL, data transformation, orchestration, and streaming scenarios.

Ray Train orchestrates distributed PyTorch, TensorFlow, and HuggingFace workloads from laptops to clusters, automating GPU placement, fault tolerance, checkpointing, and hyperparameter sweeps so teams can scale massive model training with minimal code changes.

Automates environment prep, service orchestration, and cleanup for executing integration test suites, covering database setup, migrations, and optional coverage reports.

This skill provides patterns and implementation templates for managing complex, long-running business processes across multiple independent services. It focuses on implementing the Saga pattern, ensuring data consistency in microservices architectures. It defines clear states, handles step execution, and crucially includes compensation logic to reliably roll back changes when any part of the workflow fails.

Automates schema validation within data pipelines, providing guidance on best practices, workflow orchestration, and streaming ETL scenarios while producing production-ready code and configurations.

Toolkit for prompt engineering experts to optimize prompts, evaluate LLM outputs, design agentic workflows, assess RAG quality, and curate few-shot examples across evaluation and orchestration tasks.