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 agentmods add skills/githubxsy/agent-skills/markitdownnpx skills add GitHubxsy/agent-skills --skill markitdowngit clone --depth 1 https://github.com/GitHubxsy/agent-skillsWhat 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 | $0.00206 | $0.01775 |
| Opus 5 | $0.00103 | $0.00888 |
| Sonnet 5 | $0.00041 | $0.00355 |
| Haiku 4.5 | $0.00021 | $0.00178 |
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 2d 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.
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MarkItDown
Convert source material into Markdown optimized for language-model and text-analysis workflows. Preserve useful structure such as headings, lists, tables, links, and metadata; do not promise page-faithful visual reproduction.
Choose a workflow
- Convert one local file or supported URL: use the
markitdownCLI. - Convert several local files reproducibly: use
scripts/convert.py. - Integrate conversion into an application or pass streams: use the Python API.
- Read standalone images or images embedded in PPTX, DOCX, PDF, or XLSX: run the bundled script with an OpenAI-compatible vision model.
- Reject audio and video inputs; this Skill does not transcribe them.
Install safely
Require Python 3.10 or newer. Prefer an isolated environment:
python3 -m venv .venv
source .venv/bin/activate
python -m pip install 'markitdown[pdf,docx,pptx,xlsx,xls,outlook]' markitdown-ocr openai
Install only required extras when dependency size matters, for example:
python -m pip install 'markitdown[pdf,docx,pptx,xlsx]'
Do not install markitdown[all] for this Skill because it includes audio-transcription dependencies. Install only the document extras required by the task.
Do not alter a user's project environment without permission. Reuse an existing compatible environment when present; otherwise explain or create an isolated one within the task scope.
Convert content
For one input:
markitdown report.pdf -o report.md
markitdown https://example.com/page -o page.md
cat report.pdf | markitdown > report.md
For multiple local inputs, run:
python scripts/convert.py file.pdf slides.pptx notes.docx --output-dir markdown
python scripts/convert.py source-folder --output-dir markdown
The script recursively converts directories and preserves their relative structure. It preserves each source extension in the output name (file.pdf.md) to avoid collisions. Add --overwrite only when replacing existing outputs is intended.
What ships with it
2 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.
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.
- 2d ago First seen · 159 lines · 206 tokens per session scan A 7d733eb658c5
markitdown is a skill published in the GitHub repository GitHubxsy/agent-skills (21 stars, last pushed 4d ago), licensed Apache-2.0. It adds 206 tokens to every session and 1,775 once invoked, about $0.0010 per session on Opus 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
babysit-pr
Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…
imagegen
Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…