AI Agent Skills

Pure Rust PDF library for AI/RAG: structure-aware chunking, no ML, no C deps.

Open-source toolkit for reliable RAG pipelines: convert PDFs to Markdown, clean documents, inspect chunks, compare chunking strategies, and enrich metadata for LLM appli…

PDFStract - Extract, Chunking and Embedding Layer in Your RAG Pipeline - Available as CLI - WEBUI - API

Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for languag…

A Python CLI to test, benchmark, and find the best RAG chunking strategy for your Markdown documents.

Open-source GEO content engineering and multi-site distribution system with AI tasks, RAG/semantic chunking, analytics, GEOFlow Agent and WordPress target publishing.

Enhances Tesseract OCR output using LLMs (local or API) for error correction, smart chunking, and markdown formatting of scanned PDFs

Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG application…

Use dspy.RLM (Recursive Language Model) for reasoning over contexts too large to fit in an LLM's working window — entire codebases, long logs, massive documents, or mult…

Fast, AI-agent-native code search in Rust — hybrid BM25 + semantic, Tree-sitter AST chunking, dependency & impact analysis. Drop-in replacement for grep/cat/read/ls in C…

Advanced document extraction and chunking techniques for retrieval augmented generation that is aware of the layout of documents. Increases knowledge retrieval accuracy…

Diagnose and optimize Rspack `optimization.splitChunks` configuration. Use this when a user wants better production chunking, safer `chunks: "all"` defaults, fe

Completely local RAG. Chat with your PDF documents (with open LLM) and UI to that uses LangChain, Streamlit, Ollama (Llama 3.1), Qdrant and advanced methods like reranki…

The compiler from ARR-SPEC to any generation backend (编译层). One compiler, many backends - the backend differences live in backends.yaml as data, never as separate skills…

Process unstructured documents into search-ready chunks. Use this skill when the user wants to process PDFs or unstructured documents into JSONL chunks using Docling. Ac…

Guides designing retrieval-augmented generation (RAG) pipelines: document chunking, embedding, indexing, retrieval, and grounding LLM output in retrieved content. Use wh…