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 warn
- 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 | $0.00087 | $0.03023 |
| Opus 5.5 | $0.00035 | $0.01209 |
| Sonnet 5.5 | $0.00017 | $0.00605 |
| Haiku 4.5 | $0.00009 | $0.00302 |
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 25d 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 — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LiteParse — Local Document Parsing
Overview
LiteParse is an open-source document parser (Rust core, Python/Node bindings) for local, layout-aware text extraction. It produces layout text, structured JSON, or heuristic Markdown. Spatial text items may span several words; Python emit_word_boxes=True adds word boxes when needed.
Verified release: Python liteparse 2.15.0 (September 29, 2026); CLI and synthetic local PDF/image fixtures tested on Python 3.13. Node/Rust examples below are source-checked and illustrative. Images convert through bundled Rust libraries, not ImageMagick. No cloud account is needed, but missing Tesseract language data can download from GitHub and an explicitly configured HTTP OCR service receives document images.
For parser selection vs MarkItDown, PDF manipulation libraries, 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 | A PDF manipulation library such as pypdf |
| Dense tables, handwriting, production cloud pipelines | LlamaParse (cloud; sign up separately) |
Installation
uv pip install "liteparse==2.15.0"
This installs the Python bindings and the lit CLI. Verify:
lit --help
python -c "import liteparse; print(liteparse.__version__)"
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.
- 25d ago Changed · +10 lines · +1 tokens per session 02a789c79596
- 26d ago Changed · +2 lines 9ee549faf0c7
- 1mo 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 (54,777 stars, last pushed today), licensed MIT. It adds 87 tokens to every session and 3,023 once invoked, about $0.0003 per session on Opus 5.5. 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.
pdf-processing-pro
Production-ready PDF processing with forms, tables, OCR, validation, and batch operations. Use when working with complex PDF workflows in production environments, processing large volumes of PDFs, or requiring robust error handling and validation.
google-apps-script
Build Google Apps Script automation for Sheets and Workspace. Custom menus, triggers (onEdit / time-driven / form submit), dialogs, sidebars, email batches, PDF export, external API. Use whenever the user wants to automate a Google Sheet, build a Sheets menu / sidebar / dialog, hit a Sheets row from email or a…
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.
save-to-brain
Shared step of the marketing playbooks (research-market, plan-campaign, make-content, analyze-performance): saves a finished file to the brand and makes it searchable. It either replaces older versions or keeps dated history, then checks that the file can be found. Loaded by those playbooks; not for direct use.
fulltext-retrieval
Use when you need full-text PDFs for a list of DOIs, such as a meta-analysis screening set. Batch-downloads open-access copies via Unpaywall, PMC, OpenAlex and Crossref, lists paywalled papers for manual access, and can convert PDFs to Markdown.