Skill UI

A specialized, comprehensive workflow designed for optimizing PostgreSQL databases. It guides users through performance assessment, advanced query tuning (using EXPLAIN ANALYZE), defining optimal indexing strategies, fine-tuning configuration parameters (like shared_buffers), and setting up robust maintenance and monitoring practices for production environments.

Guides and automates Prefect Flow Builder tasks for data pipelines, offering best-practice patterns, production-ready code, configuration templates, and validation advice whenever you mention the tool or request pipeline help.

This skill builds and executes automated pipelines to clean, validate, and transform data for machine learning, covering ETL workflows, error handling, and quality reporting so Claude can deliver analysis-ready datasets.

An expert assistant providing deep knowledge and best practices for Prisma ORM. It covers the entire lifecycle of database interaction, including optimal schema design, relation modeling, safe migration workflows, and advanced query optimization techniques (addressing N+1 issues and implementing raw queries) across PostgreSQL, MySQL, and SQLite.

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.

An end-to-end workflow for building robust, production-ready Python backend APIs. This guide covers all stages, including project scaffolding, SQLAlchemy database integration, Pydantic validation, implementing JWT/OAuth2 authentication, comprehensive error handling, unit testing, and Docker deployment for modern microservices.

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.

Automates PyTorch training workflows by guiding data preparation, model configuration, hyperparameter tuning, and experiment tracking while following industry best practices for production readiness.

This comprehensive workflow guides the end-to-end implementation of Retrieval-Augmented Generation (RAG) systems. It covers critical stages, including selecting optimal embedding models, setting up vector databases, designing chunking strategies, implementing hybrid retrieval, integrating Large Language Models (LLMs), and performing rigorous evaluation. Use this when building knowledge-grounded AI applications or advanced semantic search systems.

Ray Data offers scalable distributed data processing for ML workloads, streaming across CPU/GPU clusters and integrating with PyTorch, TensorFlow, and Ray Train. Use it for batch inference, ETL, and multi-modal preprocessing from a laptop up to hundreds of nodes.

This comprehensive workflow guides the development of modern, scalable full-stack applications using Next.js 14+, React, and TypeScript. It covers all phases, from project setup and component architecture to advanced data fetching (Server Components, React Query), styling with Tailwind CSS, robust testing (E2E via Playwright), and deployment optimization. Ideal for building complex, production-grade web interfaces.

Provides automated regression analysis guidance, best practices, code snippets, and validation for SQL, BI, visualization, and statistical workflows in data analytics projects.