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 myths-labs/muse --skill markitdowngit clone --depth 1 https://github.com/myths-labs/museWrote 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/myths-labs/muse/markitdown)<a href="https://agentmods.dev/skills/myths-labs/muse/markitdown"><img src="https://agentmods.dev/badge/skills/myths-labs/muse/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/myths-labs/muse/markitdown"><img src="https://agentmods.dev/badge/skills/myths-labs/muse/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 | $0.00041 | $0.00329 |
| Opus 5.5 | $0.00016 | $0.00132 |
| Sonnet 5.5 | $0.00008 | $0.00066 |
| Haiku 4.5 | $0.00004 | $0.00033 |
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.
What it actually says
MarkItDown
MarkItDown is a lightweight Python utility by Microsoft for converting various files to Markdown for use with LLMs.
Capabilities
Converts the following formats to Markdown:
- PowerPoint (.pptx)
- Word (.docx)
- Excel (.xlsx)
- Images (EXIF & OCR)
- Audio (Transcription)
- HTML
- Text formats (CSV, JSON, XML)
- YouTube URLs
- ZIP files
Installation
To use MarkItDown, you first need to install it. It requires Python 3.10+.
pip install "markitdown[all]"
Usage
Command Line
Convert a file and print to stdout:
markitdown path-to-file.pdf
Convert a file and save to output:
markitdown path-to-file.pdf -o document.md
Pipe content:
cat path-to-file.pdf | markitdown
Python API
from markitdown import MarkItDown
md = MarkItDown()
result = md.convert("test.xlsx")
print(result.text_content)
Microsoft Document Intelligence
For better PDF extraction, use Azure Document Intelligence:
markitdown path-to-file.pdf -o document.md -d -e "<endpoint>"
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 · 68 lines · 41 tokens per session scan A 0444e36d4a8f
markitdown is a skill published in the GitHub repository myths-labs/muse (37 stars, last pushed 27d ago), licensed MIT. It adds 41 tokens to every session and 329 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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