Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill liteparsegit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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/k-dense-ai/scientific-agent-skills/liteparse)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/liteparse"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/liteparse/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/k-dense-ai/scientific-agent-skills/liteparse"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/liteparse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00086 | $0.02642 |
| Opus 5 | $0.00043 | $0.01321 |
| Sonnet 5 | $0.00017 | $0.00528 |
| Haiku 4.5 | $0.00009 | $0.00264 |
Grade A, and why
liteparse scanned grade A 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 8d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sL https://example.com/report.pdf | lit parse - How it starts
The opening of the file, as written. The whole thing — 313 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LiteParse — Local Document Parsing
Overview
LiteParse is a fast, open-source document parser (Rust core, Python/Node bindings) focused on local, layout-aware text extraction with bounding boxes. It does not produce Markdown and does not call cloud LLMs. Outputs are plain text (layout-preserved) or structured JSON with per-page text_items (position, font metadata, optional confidence).
Version note: Examples target liteparse 2.0.0 (PyPI, May 2026). The upstream V1 branch is legacy; this skill documents V2 / main only.
For parser selection vs MarkItDown, the pdf skill, or LlamaParse, see references/choosing_a_parser.md.
When to Use This Skill
Use LiteParse when you need:
- Fast local parsing of PDFs or converted Office/image files without cloud dependencies
- Spatial text with bounding boxes for layout-aware RAG, citation grounding, or figure/table region logic
- OCR on scanned PDFs or images (bundled Tesseract, or a user-run HTTP OCR server)
- Page screenshots (PNG) for multimodal agents that must see charts, figures, or handwriting
- Batch ingestion of literature folders, supplementary PDFs, or protocol libraries
- Page subsets or password-protected PDFs
When Not to Use
| Task | Use instead |
|---|---|
| Markdown for LLM ingestion (EPUB, audio, YouTube, HTML) | markitdown skill |
| Merge/split PDFs, forms, watermarks, rotation | pdf skill |
| Dense tables, handwriting, production cloud pipelines | LlamaParse (cloud; sign up separately) |
Installation
uv pip install "liteparse==2.0.0"
This installs the Python bindings and the lit CLI. Verify:
lit --help
python -c "import liteparse; print(liteparse.__version__)"
Optional system tools (for non-PDF inputs):
- LibreOffice — Word, Excel, PowerPoint, OpenDocument, CSV/TSV
- ImageMagick — PNG, JPEG, TIFF, WebP, SVG, etc.
Install commands are in references/ocr_and_formats.md.
What ships with it
6 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.
- 8d ago First seen · 313 lines · 86 tokens per session scan A 63a10198d017
liteparse is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (44,220 stars, last pushed 4d ago), licensed MIT. It adds 86 tokens to every session and 2,642 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
pdf-to-md
Convert PDFs and office documents to clean Markdown, with structured bundles for scientific papers. Use when extracting article structure, preparing a manuscript for analysis, or creating CSAG input.
slides-as-code
Build research slides with text-first source (Slidev/Marp/Reveal/Quarto) and reproducible export (PDF). Includes structure, figure reuse rules, and quality checklist for top-tier scientific presentations.
pdf-design
Designs PDF reports and proposals from HTML with previews and branding. Use to create, export, or securely upload a PDF.
fulltext-retrieval
Batch download open-access PDFs by DOI using legitimate OA APIs (Unpaywall, PMC, OpenAlex, Crossref). Optional PDF→Markdown conversion for token-efficient LLM analysis.
pdf-text-replace
Replace text in fillable PDF forms by updating form field values. This skill should be used when users need to update names, addresses, dates, or other text in PDF form fields.
discharge-summary-writer
Generate hospital discharge summaries from admission data, hospital course, diagnoses, procedures, and follow-up plans; use when a structured discharge document is required from clinical notes.