This repository showcases a production-tested infrastructure for enhancing Claude Code, an AI-assisted development tool. It provides a system for automatically suggesting and enforcing development best practices via hooks that trigger on user prompts and file operations. The core innovation is a skill auto-activation mechanism that uses contextual cues (keywords, intent, file paths, content) to recommend relevant guidelines, with a two-try blocking model for mandatory skills to ensure compliance without excessive nagging. The infrastructure is built around modular skills (each under 500 lines with progressive disclosure), specialized agents for complex tasks, and a dev docs pattern that maintains context across sessions using vector search. It also includes an AI provider abstraction layer supporting multiple LLMs (Gemini, OpenAI, Anthropic, Ollama) for flexible integration. Designed as a reference library, users are encouraged to copy and adapt the hooks, skills, and configurations into their own Claude Code setups, with setup and verification scripts to streamline installation. While not a standalone application, this collection of patterns and tools represents a sophisticated approach to scaling AI-assisted development practices in enterprise environments. Its strength lies in the reusable components: the hook-based skill system, modular skill documentation pattern, and provider abstraction layer, all demonstrating non-obvious craftsmanship in balancing automation with developer autonomy.