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 agentlas-ai/agentlas-desktop --skill liteparsegit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/liteparse)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/liteparse"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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/agentlas-ai/agentlas-desktop/liteparse"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/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 | $0.00086 | $0.02642 |
| Opus 5.5 | $0.00034 | $0.01057 |
| Sonnet 5.5 | $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 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
100% identical to liteparse — 64 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 — 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.
- 1mo ago First seen · 313 lines · 86 tokens per session scan A 63a10198d017
liteparse is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 86 tokens to every session and 2,642 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to liteparse, differing in 64 lines, and is treated as a copy.
Other skills, from other repositories
markitdown
Converts heterogeneous documents and selected URIs to Markdown with Microsoft MarkItDown for text analysis, search, and LLM/RAG ingestion. Covers safe local conversion, streams, Office/PDF/data formats, batch workflows, plugins, vision OCR, Azure extraction, and the official MCP server.
azure-ai
Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. WHEN: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech.
doc-parse
A document parser that converts PDFs, PowerPoint files, spreadsheets, and Word files into structured Markdown with metadata and a confidence score.
ingest
Process unstructured documents into search-ready chunks. Use this skill when the user wants to process PDFs or unstructured documents into JSONL chunks using Docling. Activate even if the user says document processing, chunking, Docling, PDF processing, or chunk quality evaluation. For cloud-scale ingestion via OSIS…
edgeparse
Extract structured content from any PDF for AI agents, RAG pipelines, and Copilot Skills. Use this skill whenever the user wants to read, analyze, or reason about a PDF document; needs to feed document content to an LLM; mentions PDF extraction, parsing, or conversion; wants tables, headings, or bounding boxes from a…
elizaos-knowledge-workflows
Guides elizaOS document ingestion, retrieval/RAG, provenance, tenant-safe knowledge sharing, embedding migrations, and evidence-based retrieval evaluation. Use when importing approved documents, debugging missing or irrelevant knowledge, verifying citations and access isolation, changing embedding providers or…