synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/synthetic-sciences/opensciencenpx agentmods add skills/synthetic-sciences/openscience/markitdownWrote 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/synthetic-sciences/openscience/markitdown)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/markitdown"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/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/synthetic-sciences/openscience/markitdown"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/markitdown.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 320 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium Privilege Escalation · line 461 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
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 | $0.00053 | $0.03109 |
| Opus 5.5 | $0.00021 | $0.01244 |
| Sonnet 5.5 | $0.00011 | $0.00622 |
| Haiku 4.5 | $0.00005 | $0.00311 |
Grade B, and why
markitdown scanned grade B with 1 finding 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt-get install tesseract-ocr Copies of this mod
8 near-identical copies found in the catalogue:
- markitdown — 98% identical, 25 lines differ
- markitdown — 98% identical, 24 lines differ
- markitdown — 98% identical, 24 lines differ
- markitdown — 98% identical, 23 lines differ
- markitdown — 98% identical, 24 lines differ
- markitdown — 92% identical, 17 lines differ
- markitdown — 92% identical, 17 lines differ
- markitdown — 92% identical, 23 lines differ
How it starts
The opening of the file, as written. The whole thing — 495 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MarkItDown - File to Markdown Conversion
Overview
MarkItDown is a Python tool developed by Microsoft for converting various file formats to Markdown. It's particularly useful for converting documents into LLM-friendly text format, as Markdown is token-efficient and well-understood by modern language models.
Key Benefits:
- Convert documents to clean, structured Markdown
- Token-efficient format for LLM processing
- Supports 15+ file formats
- Optional AI-enhanced image descriptions
- OCR for images and scanned documents
- Speech transcription for audio files
Visual Enhancement with Scientific Schematics
When creating documents with this skill, always consider adding scientific diagrams and schematics to enhance visual communication.
If your document does not already contain schematics or diagrams:
- Use the scientific-schematics skill to generate AI-powered publication-quality diagrams
- Simply describe your desired diagram in natural language
- Nano Banana Pro will automatically generate, review, and refine the schematic
For new documents: Scientific schematics should be generated by default to visually represent key concepts, workflows, architectures, or relationships described in the text.
How to generate schematics:
python skills/visualization/scientific-schematics/scripts/generate_schematic.py "your diagram description" -o figures/output.png
The AI will automatically:
- Create publication-quality images with proper formatting
- Review and refine through multiple iterations
- Ensure accessibility (colorblind-friendly, high contrast)
- Save outputs in the figures/ directory
When to add schematics:
- Document conversion workflow diagrams
- File format architecture illustrations
- OCR processing pipeline diagrams
- Integration workflow visualizations
- System architecture diagrams
- Data flow diagrams
- Any complex concept that benefits from visualization
For detailed guidance on creating schematics, refer to the scientific-schematics skill documentation.
What ships with it
12 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.
- assets/example_usage.md 10 KB
- INSTALLATION_GUIDE.md 6.6 KB
- LICENSE.txt 1.0 KB
- OPENROUTER_INTEGRATION.md 9.4 KB
- QUICK_REFERENCE.md 6.7 KB
- README.md 4.9 KB
- references/api_reference.md 8.8 KB
- references/file_formats.md 8.8 KB
- scripts/batch_convert.py 6.0 KB runs code
- scripts/convert_literature.py 8.3 KB runs code
- scripts/convert_with_ai.py 7.2 KB runs code
- SKILL_SUMMARY.md 8.6 KB
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 Changed · +7 lines ce7970ea2aae
- 1mo ago Changed f9adbe63b212
- 1mo ago Changed d6bda49f9a6c
- 1mo ago First seen · 488 lines · 53 tokens per session scan B 79662d11bc22
markitdown is a skill published in the GitHub repository synthetic-sciences/openscience (4,121 stars, last pushed yesterday), licensed Apache-2.0. It adds 53 tokens to every session and 3,109 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
docutranslate
Use when translating documents locally via LLM — PDF, Word, Excel, Markdown, SRT subtitles with format preservation. DocuTranslate: LLM-powered multi-format local file translation tool with MCP server support.
nano-pdf
Edit PDF text/typos/titles via nano-pdf CLI (NL prompts).
ocr-and-documents
Extract text from PDFs/scans (pymupdf, marker-pdf).
system-prompts
Write system prompts, tool docs, and agent definitions. Project tag conventions + RFC 2119 keywords + dense compression. Use when authoring or editing any prompt the model reads.
tool-prompt-optimization
Optimize the description prompts an AI agent reads to learn its built-in tools (the .md files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool…
pdf-summarizer
Summarize the key points of a PDF document into a short bullet list.