Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/beita6969/ScienceClawnpx agentmods add skills/beita6969/scienceclaw/pdf-processing-proWrote 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/beita6969/scienceclaw/pdf-processing-pro)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/pdf-processing-pro"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/pdf-processing-pro.svg" alt="Measured on agentmods" height="20"></a>- 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 | $0.00046 | $0.01649 |
| Opus 5 | $0.00023 | $0.00825 |
| Sonnet 5 | $0.00009 | $0.00330 |
| Haiku 4.5 | $0.00005 | $0.00165 |
Grade A, and why
pdf-processing-pro 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 5d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run([ Copies of this mod
3 near-identical copies found in the catalogue:
- PDF Processing Pro — 100% identical, 2 lines differ
- PDF Processing Pro — 100% identical, 2 lines differ
- PDF Processing Pro — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 297 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Processing Pro
Production-ready PDF processing toolkit with pre-built scripts, comprehensive error handling, and support for complex workflows.
Quick start
Extract text from PDF
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
text = pdf.pages[0].extract_text()
print(text)
Analyze PDF form (using included script)
python scripts/analyze_form.py input.pdf --output fields.json
# Returns: JSON with all form fields, types, and positions
Fill PDF form with validation
python scripts/fill_form.py input.pdf data.json output.pdf
# Validates all fields before filling, includes error reporting
Extract tables from PDF
python scripts/extract_tables.py report.pdf --output tables.csv
# Extracts all tables with automatic column detection
Features
✅ Production-ready scripts
All scripts include:
- Error handling: Graceful failures with detailed error messages
- Validation: Input validation and type checking
- Logging: Configurable logging with timestamps
- Type hints: Full type annotations for IDE support
- CLI interface:
--helpflag for all scripts - Exit codes: Proper exit codes for automation
✅ Comprehensive workflows
- PDF Forms: Complete form processing pipeline
- Table Extraction: Advanced table detection and extraction
- OCR Processing: Scanned PDF text extraction
- Batch Operations: Process multiple PDFs efficiently
- Validation: Pre and post-processing validation
Advanced topics
PDF Form Processing
For complete form workflows including:
- Field analysis and detection
- Dynamic form filling
- Validation rules
- Multi-page forms
- Checkbox and radio button handling
See FORMS.md
Table Extraction
For complex table extraction:
- Multi-page tables
- Merged cells
- Nested tables
- Custom table detection
- Export to CSV/Excel
See TABLES.md
OCR Processing
For scanned PDFs and image-based documents:
- Tesseract integration
- Language support
- Image preprocessing
- Confidence scoring
- Batch OCR
What ships with it
4 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.
- 5d ago First seen · 297 lines · 46 tokens per session scan A 35570753464a
pdf-processing-pro is a skill published in the GitHub repository beita6969/ScienceClaw (896 stars, last pushed 3mo ago), licensed MIT. It adds 46 tokens to every session and 1,649 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). 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 files: create, read, merge, fill, OCR, edit text.
pydicom
Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.
liteparse
Local document and PDF parsing that returns spatial text with bounding boxes. Use for extracting text from PDFs, DOCX, Office files, and images; running OCR on scans; producing layout-preserved JSON for RAG; batch-ingesting folders of papers; or rendering pages to PNG for multimodal agents. Distinguishing capabilities…
markitdown
Convert 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.
open-notebook
Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker…
pdf-toolkit
Structured .pdf operations: extract text/tables, merge pages from multiple PDFs, split a PDF by page ranges, fill PDF form fields, and generate fresh PDFs from JSON. Trigger when the user wants programmatic PDF work without natural-language rewriting — examples: pull tables from a report, combine three PDFs, extract…