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.
AutoSkill is a comprehensive framework for extracting, managing, and evolving reusable 'skills' from conversational logs and documents. It operates as a modular application with multiple entry points: a web UI for interactive chat, a CLI for batch processing, and an OpenAI-compatible proxy server that injects relevant skills into LLM context. The system employs LLM-driven and heuristic pipelines to extract skill candidates, deduplicate and merge them, and persist them in a skill store with vector and BM25 retrieval. It also includes a self-evolution subsystem (SkillEvo) that replays experiences, evaluates performance, and mutates skills for improvement. The codebase is hybrid, containing both application components (web server, proxy, libraries) and data/research pipelines (document ingestion, trajectory extraction), but the dominant character is an application SDK designed for integration with agentic workflows. Notable craftsmanship is seen in standalone utilities like a CJK-aware text unit counter, an AST-based prompt patcher, a persistent BM25 index, and a LibreOffice socket shim, though the repo also suffers from significant code duplication (e.g., identical validators across docx/pptx/xlsx) and a large surface area of environment variables and external dependencies.