Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add agentlas-ai/agentlas-desktop --skill markitdowngit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/markitdown)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/markitdown"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/markitdown/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/markitdown"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/markitdown.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00061 | $0.02745 |
| Opus 5.5 | $0.00024 | $0.01098 |
| Sonnet 5.5 | $0.00012 | $0.00549 |
| Haiku 4.5 | $0.00006 | $0.00275 |
Grade A, and why
markitdown scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to markitdown — 39 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 282 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MarkItDown
Overview
MarkItDown is Microsoft's lightweight Python utility for turning common documents into structure-preserving Markdown. Its output is designed primarily for indexing, text analysis, search, and LLM ingestion—not high-fidelity visual reproduction.
This skill targets MarkItDown 0.1.6, released May 26, 2026. New code should use result.markdown; result.text_content remains only as a soft-deprecated compatibility alias.
Choose the Right Path
| Need | Recommended path |
|---|---|
| Trusted local PDF, Office, HTML, CSV, EPUB, or ZIP | Built-in converter with convert_local() |
| Uploaded bytes or an already-open file | convert_stream() with StreamInfo hints |
| Remote HTTP(S) input | Validate and fetch it yourself, then call convert_response() |
| Scanned PDF or text inside embedded images | Official markitdown-ocr vision plugin, Azure Document Intelligence, or Azure Content Understanding |
| Video, structured fields, or custom multimodal extraction | Azure Content Understanding |
| Local agent integration | Official markitdown-mcp server over STDIO or localhost |
| Bounding boxes, page coordinates, or screenshots | Use a layout-aware parser such as LiteParse instead |
| PDF merge/split/forms/watermarks | Use the pdf skill instead |
Installation
Create an isolated environment:
uv venv --python 3.12 .venv
source .venv/bin/activate
Install every built-in feature:
uv pip install "markitdown[all]==0.1.6"
Or install only the converters required by the task:
uv pip install "markitdown[pdf,docx,pptx,xlsx]==0.1.6"
Available extras in 0.1.6 are:
pptx,docx,xlsx,xls,pdf, andoutlookaudio-transcriptionandyoutube-transcriptionaz-doc-intelandaz-content-understandingall
Verify the installation:
markitdown --version
python scripts/inspect_installation.py
The [all] extra does not install the separate markitdown-ocr plugin or an OpenAI-compatible client.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/api_reference.md 11 KB
- references/cloud_and_ocr.md 9.5 KB
- references/file_formats.md 10 KB
- references/mcp_and_plugins.md 6.5 KB
- references/migration.md 8.1 KB
- references/security.md 9.8 KB
- references/workflows.md 8.0 KB
- scripts/batch_convert.py 10 KB runs code
- scripts/convert_literature.py 12 KB runs code
- scripts/inspect_installation.py 4.8 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 282 lines · 61 tokens per session scan A 732ca5ec26a4
markitdown is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 61 tokens to every session and 2,745 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to markitdown, differing in 39 lines, and is treated as a copy.
Other skills, from other repositories
markitdown
Converts heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server.
azure-ai
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
doc-parse
A document parser that converts PDFs, PowerPoint files, spreadsheets, and Word files into structured Markdown with metadata and a confidence score.
ingest
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. Activate even if the user says document processing, chunking, Docling, PDF processing, or chunk quality evaluation. For cloud-scale ingestion via OSIS…
edgeparse
Extract structured content from any PDF for AI agents, RAG pipelines, and Copilot Skills. Use this skill whenever the user wants to read, analyze, or reason about a PDF document; needs to feed document content to an LLM; mentions PDF extraction, parsing, or conversion; wants tables, headings, or bounding boxes from a…
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Guides elizaOS document ingestion, retrieval/RAG, provenance, tenant-safe knowledge sharing, embedding migrations, and evidence-based retrieval evaluation. Use when importing approved documents, debugging missing or irrelevant knowledge, verifying citations and access isolation, changing embedding providers or…