Mimeo is a Python CLI application that transforms a named expert's public body of work (essays, talks, interviews, podcasts, papers, etc.) into a structured, source-verified Agent Skill file (SKILL.md) and/or AGENTS.md, designed for AI coding agents to adopt the expert's reasoning style. The pipeline orchestrates identity resolution, source discovery via the Parallel API, content fetching (web, YouTube transcripts, optional audio transcription), LLM-based distillation and clustering (via OpenRouter), quote verification against source text, structured skill authoring, critique/refinement loops, and optional avatar generation. The output is a curated set of markdown files (principles, frameworks, mental models, anti-patterns, heuristics, quotes) organized per expert in the `output/` directory. The codebase is well-architected with async stages, a fingerprint-based caching layer, SSRF-safe HTTP helpers, prompt injection mitigations, and extensive offline tests using fake LLM/Parallel clients. While the primary purpose is the application tool, the generated output forms a reusable knowledge base of expert reasoning patterns, giving the repo a hybrid character.
SurveyX is an academic survey automation system that leverages Large Language Models (LLMs) to generate domain-specific academic papers from user-provided references. The system implements a modular pipeline that processes markdown-formatted reference documents through stages of preprocessing, outline generation, content drafting, post-refinement (citation correction, figure/table insertion), and LaTeX/PDF output generation. Designed for offline use in its open-source version, it accepts survey titles, keywords, and local reference paths to produce structured surveys with watermarked PDF outputs. The architecture emphasizes separation of concerns with distinct modules for LLM interaction, content refinement, and document generation, supported by monitoring utilities for tracking token usage and pipeline timing.
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.