markitdown
Convert diverse file formats like PDFs, Office docs, images, audio, and web content into clean Markdown, specifically optimized for LLM ingestion, RAG pipelines, and automated text analysis workflows.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
160 skills found
Convert diverse file formats like PDFs, Office docs, images, audio, and web content into clean Markdown, specifically optimized for LLM ingestion, RAG pipelines, and automated text analysis workflows.
Extract plain text from EPUB, MOBI, and PDF files for analysis or processing. Includes local support for all common ebook formats.
Build production-grade AI agents using LangGraph, Anthropic/OpenAI/vLLM, and structured outputs. Features streaming, A2A protocol, Pydantic validation, vector memory, and guardrails for resilient, multi-agent workflows.
A systematic workflow to instrument, evaluate, and monitor LLM applications using TruLens, supporting frameworks like LangChain, LangGraph, and LlamaIndex.
Architect production-grade LLM applications using LangChain 1.x and LangGraph. Implement stateful AI agents, multi-step workflows, and custom memory systems for complex conversational and automation tasks.
Standardized detective skill integration for agent roles. Maps agents to code-analysis skills and enforces claudemem usage for memory-indexed code investigation.
A versatile data analysis assistant for loading datasets, performing statistical calculations, visualizing trends, and generating professional summary reports.
Crawl websites to extract content as clean markdown files. Ideal for documentation, research, and offline knowledge management.
Expert guidance for Neo4j Cypher queries and MCP server tools, focusing on schema introspection, graph operations, and efficient database development workflows.
Control and monitor Xiaomi Mijia smart home devices including status switching, device discovery, automation scenes, and environmental statistics.
Semantic code analysis guide for Serena MCP. Automatically prioritizes Serena tools for symbols, references, and code memory to optimize context and efficiency.
Orchestrate complex multi-agent swarms with topologies like mesh, hierarchical, and star for research, development, and testing workflows.