markitdown-skill_skill

Overview

This skill guides using Microsoft MarkItDown, a Python utility that converts many file types into Markdown optimized for LLM consumption and text-analysis pipelines. It preserves document structure (headings, lists, tables, links) and supports optional AI-assisted transcription and image description. Use it to prepare diverse sources for indexing, prompting, or downstream NLP tasks.

How this skill works

MarkItDown inspects input files or URLs, applies format-specific parsers and optional OCR/transcription, and emits structured Markdown suitable for LLMs. It can call external services (Azure Document Intelligence or an LLM client) for complex PDFs, image descriptions, or audio transcription. The tool exposes both a CLI and a Python API for single-file conversion, batch workflows, and plugin extensions.

When to use it

• Converting PDFs, Word, PowerPoint, Excel, or email files into LLM-ready Markdown
• Extracting text from images (OCR) or getting AI-generated image descriptions
• Transcribing audio files or extracting YouTube transcripts
• Batch-processing directories or archives (ZIP, EPUB, Jupyter notebooks)
• Preparing web content (HTML, RSS, Wikipedia) or structured data (CSV, JSON, XML) for indexing

Best practices

• Install optional extras for the formats you need (e.g., [pdf], [docx], [audio-transcription]) to avoid missing-dependency errors
• Use the Python API for programmatic pipelines and add a source header to track provenance
• For complex PDFs, enable Azure Document Intelligence or chunk large files to avoid timeouts
• Enable an LLM client only when you need descriptions or semantic enrichment to control cost
• Validate converted output by spot-checking headings, tables, and extracted text before indexing

Example use cases

• Batch convert a folder of reports to Markdown for ingestion into a vector database
• Extract a YouTube video transcript via the CLI to create searchable meeting notes
• Convert scanned receipts with OCR and optionally enrich descriptions with an LLM
• Turn Jupyter notebooks into Markdown that retains code cells and outputs for documentation
• Parse an RSS feed or Wikipedia page into Markdown snippets for prompt context