ade-document-processing-skills

ADE Document Processing Skills

Agent skills for LandingAI's Agentic Document Extraction (ADE) — production-ready document AI that converts complex, real-world documents into accurate, structured data with full auditability and traceability.

These skills teach agentic coding assistants (Claude, Cursor, Roo Code, or any agent supporting the Agent Skills convention) how to write Python scripts that parse, extract, classify, and build processing pipelines for documents — without templates or ML training.

Why ADE?

• Vision-first — Proprietary models that understand layout, not just text. Handles complex tables, dense forms, multi-column pages, and scanned documents
• Accurate — 99.16% on DocVQA, proven on 1B+ documents processed
• Traceable — Every extracted value includes bounding boxes, page coordinates, and confidence scores traceable back to the source document
• Agentic by design — Adapts to each document autonomously, planning and verifying until quality thresholds are met
• 20+ file formats — PDF, images, spreadsheets, presentations, and more — no templates required

Skills

document-extraction

Core ADE SDK operations for parsing and extracting document content.

• Parse documents into structured Markdown with layout awareness and hierarchical JSON
• Extract specific fields using JSON schemas or Pydantic models (invoices, forms, tables)
• Split and classify multi-document batches by type (invoices vs receipts, statements vs forms)
• Process large files asynchronously (up to 1 GB / 6,000 pages)
• Visual grounding — precise bounding boxes, page numbers, and confidence scores for every element

document-workflows

End-to-end pipeline patterns that compose ADE operations into production workflows.

• Batch process documents in parallel (sync with ThreadPoolExecutor or async)
• Classify-then-extract pipelines for mixed document types
• RAG preparation — semantic chunking, embeddings, ChromaDB/FAISS ingestion
• Export structured results to DataFrames, CSV, or Snowflake
• Visualize extractions — bounding box overlays, chunk cropping, page annotations
• Word-level grounding — find and highlight specific terms within document sections
• Build Streamlit UIs for interactive document processing

Prerequisites

• A VISION_AGENT_API_KEY from LandingAI (free trial, no credit card required)
• A Python environment (e.g. uv or venv) — the skills install landingai-ade and other dependencies as needed

Installation

For Claude Code agents

# Step 1: Add the marketplace 
/plugin marketplace add landing-ai/ade-document-processing-skills

# Step 2: Install the plugin
/plugin install ade-document-processing@ade-document-processing-skills

Manual installation

1. Clone the repository:

   git clone https://github.com/landing-ai/ade-document-processing-skills.git
   

2. Copy the skills into your project or home directory:

   # Project-level (skills apply to this project only)
   cp -R ade-document-processing-skills/plugins/ade-document-processing/skills/document-extraction  YOUR_PROJECT/.claude/skills/
   cp -R ade-document-processing-skills/plugins/ade-document-processing/skills/document-workflows   YOUR_PROJECT/.claude/skills/
   
   # Global (skills available in all projects)
   cp -R ade-document-processing-skills/plugins/ade-document-processing/skills/document-extraction  ~/.claude/skills/
   cp -R ade-document-processing-skills/plugins/ade-document-processing/skills/document-workflows   ~/.claude/skills/
   

Set up your API key

Create a .env file in your project root:

echo 'VISION_AGENT_API_KEY=your-key-here' > .env

Get your key at va.landing.ai/settings/api-key.

Usage

The skills guide your agent to write Python scripts that process documents using ADE. Ask your agentic assistant:

"Write a Python script that reads all invoices under ./documents/ and extracts the line items, descriptions, and prices as a CSV file"

"Write a script that extracts all figures from this scientific paper as individual PNG files"

"Write a script that reads account statements and extracts all transactions across pages into a single CSV file"

"Write a script that extracts the introduction section from this PDF and highlights every occurrence of a specific term with a translucent red overlay"

"Write a Python script that reads all PDFs in a folder, extracts the abstract and introduction sections, and saves them as plain text files"