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.