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 thangchung/agent-engineering-experiment --skill markitdowngit clone --depth 1 https://github.com/thangchung/agent-engineering-experimentWrote 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/thangchung/agent-engineering-experiment/markitdown)<a href="https://agentmods.dev/skills/thangchung/agent-engineering-experiment/markitdown"><img src="https://agentmods.dev/badge/skills/thangchung/agent-engineering-experiment/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/thangchung/agent-engineering-experiment/markitdown"><img src="https://agentmods.dev/badge/skills/thangchung/agent-engineering-experiment/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 | $0.00045 | $0.00397 |
| Opus 5.5 | $0.00018 | $0.00159 |
| Sonnet 5.5 | $0.00009 | $0.00079 |
| Haiku 4.5 | $0.00005 | $0.00040 |
Grade A, and why
pdf-to-markdown 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
97% identical to pdf-to-markdown — 5 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.
What it actually says
PDF to Markdown Conversion
This skill converts PDF files to Markdown format using Microsoft's markitdown package.
When to use
- User asks to convert a PDF to Markdown
- User wants to extract text content from a PDF
- User needs to read or parse a PDF document
- User asks to summarize or analyze a PDF file
How to use
Use uvx to run markitdown directly. Pick the dependency group matching the file type:
| File type | Dependency group |
|---|---|
pdf |
|
| PowerPoint | pptx |
| Word | docx |
| Excel (.xlsx) | xlsx |
| Excel (.xls) | xls |
uvx 'markitdown[pdf]' <path-to-file> -o output.md
Or install all optional dependencies at once:
uvx 'markitdown[all]' <path-to-file> -o output.md
Examples
uvx 'markitdown[pdf]' report.pdf -o report.md
uvx 'markitdown[pptx]' slides.pptx -o slides.md
uvx 'markitdown[docx]' document.docx -o document.md
Output
- If you were asked to save the output to a specific file, save it to the requested file using
-o. - If no output file was specified, use the source filename with a
.mdsuffix.
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 · 51 lines · 45 tokens per session scan A 4fc3b312ce25
pdf-to-markdown is a skill published in the GitHub repository thangchung/agent-engineering-experiment (27 stars, last pushed 5d ago), licensed MIT. It adds 45 tokens to every session and 397 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to pdf-to-markdown, differing in 5 lines, and is treated as a copy.
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