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 agentmods add skills/sammcj/agentic-coding/liteparsenpx skills add sammcj/agentic-coding --skill liteparsegit clone --depth 1 https://github.com/sammcj/agentic-codingWhat 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 | $0.00053 | $0.01222 |
| Opus 5 | $0.00026 | $0.00611 |
| Sonnet 5 | $0.00011 | $0.00244 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
liteparse scanned grade A with 0 findings 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 yesterday.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LiteParse Skill
Parse unstructured documents (PDF, DOCX, PPTX, XLSX, images, and more) locally with LiteParse: fast, lightweight, no cloud dependencies or LLM required.
Step 0 - Use via npx, or install LiteParse
NOTE: Rather than installing liteparse globally, you can instead run it directly with npx, substituting lit <args> with npx -y @llamaindex/liteparse <args> in the commands below.
npx -y @llamaindex/liteparse
Otherwise if installing globally, use pnpm install -g @llamaindex/liteparse and then run lit <args>.
Step 1 - Produce the CLI Command or Script
Parse a Single File
# Basic text extraction
lit parse document.pdf
# JSON output saved to a file
lit parse document.pdf --format json -o output.json
# Specific page range
lit parse document.pdf --target-pages "1-5,10,15-20"
# Disable OCR (faster, text-only PDFs)
lit parse document.pdf --no-ocr
# Use an external HTTP OCR server for higher accuracy
lit parse document.pdf --ocr-server-url http://localhost:8828/ocr
# Higher DPI for better quality
lit parse document.pdf --dpi 300
Batch Parse a Directory
lit batch-parse ./input-directory ./output-directory
# Only process PDFs, recursively
lit batch-parse ./input ./output --extension .pdf --recursive
Generate Page Screenshots
Screenshots are useful for LLM agents that need to see visual layout.
# All pages
lit screenshot document.pdf -o ./screenshots
# Specific pages
lit screenshot document.pdf --pages "1,3,5" -o ./screenshots
# High-DPI PNG
lit screenshot document.pdf --dpi 300 --format png -o ./screenshots
# Page range
lit screenshot document.pdf --pages "1-10" -o ./screenshots
Step 3 - Key Options Reference
OCR Options
| Option | Description |
|---|---|
| (default) | Tesseract.js - zero setup, built-in |
--ocr-language fra |
Set OCR language (ISO code) |
--ocr-server-url <url> |
Use external HTTP OCR server (EasyOCR, PaddleOCR, custom) |
--no-ocr |
Disable OCR entirely |
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.
- yesterday First seen · 172 lines · 53 tokens per session scan A 35f406b2a2c2
liteparse is a skill published in the GitHub repository sammcj/agentic-coding (158 stars, last pushed 7d ago), licensed Apache-2.0. It adds 53 tokens to every session and 1,222 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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