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 yanjumlinnb-boop/scientific-agent-skills --skill liteparsegit clone --depth 1 https://github.com/yanjumlinnb-boop/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/yanjumlinnb-boop/scientific-agent-skills/liteparse)<a href="https://agentmods.dev/skills/yanjumlinnb-boop/scientific-agent-skills/liteparse"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/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/yanjumlinnb-boop/scientific-agent-skills/liteparse"><img src="https://agentmods.dev/badge/skills/yanjumlinnb-boop/scientific-agent-skills/liteparse.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 | $0.00098 | $0.02398 |
| Opus 5.5 | $0.00039 | $0.00959 |
| Sonnet 5.5 | $0.00020 | $0.00480 |
| Haiku 4.5 | $0.00010 | $0.00240 |
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 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.
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 - This is a copy
91% identical to liteparse — 83 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.
How it starts
The opening of the file, as written. The whole thing — 294 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.
- 1mo ago First seen · 294 lines · 98 tokens per session scan A 8990aa277558
liteparse is a skill published in the GitHub repository yanjumlinnb-boop/scientific-agent-skills (4 stars, last pushed 4mo ago), licensed MIT. It adds 98 tokens to every session and 2,398 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 91% identical to liteparse, differing in 83 lines, and is treated as a copy.
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