claude-code-guide
An advanced development guide for Claude Code, covering REPL environments, MCP integration, development workflows, and best practices for AI-assisted coding.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
136 skills found
An advanced development guide for Claude Code, covering REPL environments, MCP integration, development workflows, and best practices for AI-assisted coding.
Build production-grade RAG systems using vector databases, semantic search, and LangGraph to ground LLMs in external knowledge.
Python coding assistant providing best practices, PEP 8 enforcement, automated testing with pytest, and dependency management using uv.
Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.
Build RAG systems to ground LLMs in proprietary data. Includes vector database integration, embedding strategies, hybrid search, and advanced retrieval patterns for FastAPI backends.
Official MoonBit language reference, syntax guide, and coding conventions for building high-performance software projects.
Process and generate multimedia with Google Gemini. Analyze audio, images, videos, and PDFs with high-context windows. Supports transcription, visual QA, OCR, and AI-driven image creation.
Nonlinear optimization toolkit using CasADi and IPOPT. Ideal for building complex NLP models, defining symbolic variables, constraints, and solvers, with specialized support for power systems optimization patterns.
Generate high-quality visual content, characters, and scenes using structured JSON prompts and automated Python execution for guided image synthesis.
Perform comprehensive code reviews with a focus on security vulnerabilities, performance optimization, maintainability, and code correctness.
Guidance on React Server Components (RSC) in Next.js, covering server/client component boundaries, data fetching, and composition patterns.
Bayesian modeling and probabilistic programming with PyMC. Build hierarchical models, perform MCMC sampling (NUTS), variational inference, and conduct rigorous model comparison using LOO and WAIC.