claude-code-toolkit

Initialize Claude Code framework infrastructure for any project

Simplification and readability techniques. Use when writing for broad audiences or simplifying complex content. Covers active voice, short sentences, jargon elimination, and accessibility principles from the Plain Language Movement.

Hierarchical content structure - answer first, then supporting arguments, then details

Narrative structure for complex topics - Situation, Complication, Question, Answer

Shared configuration patterns for project setup commands. Provides security hooks, Claude framework structure templates, and framework detection patterns used across multiple setup commands.

API authentication patterns including JWT, OAuth 2.0, API keys, and session-based auth. Covers token generation, validation, refresh strategies, security best practices, and when to use each pattern. Use when implementing API authentication, choosing auth strategy, securing endpoints, or debugging auth issues. Prevents common vulnerabilities like token theft, replay attacks, and insecure storage.

Docker image optimization patterns including multi-stage builds, layer caching, security hardening, and size reduction techniques. Use when building Docker images, optimizing container size, improving build performance, or implementing Docker security best practices. Reduces image sizes by 70-90% and build times by 50-80%.

SQL query optimization patterns including EXPLAIN plan analysis, index strategies, query rewriting, and N+1 query prevention. Use when optimizing slow database queries, analyzing query performance, designing indexes, or debugging database bottlenecks. Works with PostgreSQL, MySQL, SQLite, and other SQL databases. Typical improvements: 10x-1000x query speedup.

Hugging Face Transformers best practices including model loading, tokenization, fine-tuning workflows, and inference optimization. Use when working with transformer models, fine-tuning LLMs, implementing NLP tasks, or optimizing transformer inference.

LLM evaluation and testing patterns including prompt testing, hallucination detection, benchmark creation, and quality metrics. Use when testing LLM applications, validating prompt quality, implementing systematic evaluation, or measuring LLM performance.

Comprehensive guide to implementing RAG systems including vector database selection, chunking strategies, embedding models, and retrieval optimization. Use when building RAG systems, implementing semantic search, optimizing retrieval quality, or debugging RAG performance issues.