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Introduction:
Traditional OCR and document parsing pipelines are brittle, template-dependent, and collapse the moment a vendor tweaks their invoice layout. Agentic Document Extraction (ADE) flips this paradigm by using vision-first AI models that understand document layout the way a human would—preserving table structures, multi-column reading order, and the relationships between charts and captions. LandingAI’s newly released ADE skills for AI coding agents (Claude Code, Cursor, Roo Code, and any Agent Skills–compatible assistant) empower these agents to write production-grade Python scripts that parse, extract, classify, and chain document operations into full pipelines—without a single manual template.
Learning Objectives:
- Understand how vision-first document AI outperforms traditional OCR and template-based extraction methods
- Learn to install and configure ADE skills for Claude Code, Cursor, and other agentic coding assistants
- Master the two core skill groups: `document-extraction` (parsing, field extraction, classification) and `document-workflows` (batch processing, RAG preparation, export pipelines)
- Implement practical extraction scripts for invoices, scientific papers, account statements, and mixed document batches
- Apply traceability features (bounding boxes, page coordinates, confidence scores) for audit-ready data extraction
- Setting Up ADE Skills: Installation and API Key Configuration
The ADE skills repository provides a plugin-based installation that works across multiple agentic coding environments. Before diving into extraction, you need to set up the skills and obtain an API key from LandingAI.
Step‑by‑step guide:
- Obtain a Vision Agent API Key – Visit va.landing.ai/settings/api-key to sign up for a free trial (no credit card required) and generate your key.
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Install via Claude Code (recommended) – If you’re using Claude Code, add the marketplace and install the plugin with two commands:
/plugin marketplace add landing-ai/ade-document-processing-skills /plugin install ade-document-processing@ade-document-processing-skills
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Manual installation – For other agents or custom setups, clone the repository and copy the skills into your project or global directory:
git clone https://github.com/landing-ai/ade-document-processing-skills.git Project-level installation 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 installation (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/
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Set up your environment variables – Create a `.env` file in your project root:
echo 'VISION_AGENT_API_KEY=your-actual-key-here' > .env
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Verify Python environment – Ensure you have Python 3.8+ and optionally install `uv` for faster dependency management. The skills will automatically install the `landingai-ade` SDK and other dependencies when your agent runs the first extraction script.
2. The Two Core Skills: Document-Extraction and Document-Workflows
The ADE system is composed of two distinct skill groups, each serving a different layer of the document processing stack.
document-extraction – This skill handles core ADE SDK operations for parsing and extracting document content:
– Parse – Converts documents into structured Markdown with layout awareness and hierarchical JSON output
– Extract – Pulls specific fields using JSON schemas or Pydantic models (ideal for invoices, forms, and tables)
– Split – Separates multi-document batches into individual documents by type (e.g., invoices vs. receipts)
– Classify – Routes each page by type before parsing (useful for mixed document streams)
– Generate table of contents – Creates hierarchical section structures from parsed documents
– Process large files – Handles documents up to 1 GB or 6,000 pages asynchronously
– Visual grounding – Returns precise bounding boxes, page numbers, and confidence scores for every extracted element
document-workflows – This skill composes ADE operations into end-to-end production pipelines:
– Batch process – Parallel processing using `ThreadPoolExecutor` or async patterns
– Classify-then-extract – Smart routing for mixed document types
– RAG preparation – Semantic chunking, embeddings generation, and ingestion into ChromaDB or FAISS
– Export – Structured results to DataFrames, CSV, or Snowflake
– Visualize – Bounding box overlays, chunk cropping, and page annotations
– Word-level grounding – Find and highlight specific terms within document sections
– Streamlit UIs – Build interactive document processing dashboards
Step‑by‑step: How to choose between the two skills
| Scenario | Recommended Skill |
|-|-|
| Single document, need structured Markdown or field extraction | `document-extraction` |
| Batch of 100 invoices, need CSV output | `document-workflows` (batch + export) |
| Mixed folder with invoices and receipts, need separate handling | Both – classify first, then extract |
| Building a RAG pipeline with chunking and embeddings | `document-workflows` (RAG preparation) |
| Auditing extraction accuracy with visual overlays | `document-workflows` (visualize) |
- Practical Extraction Scenarios: From Plain English to Production Scripts
The power of ADE skills lies in their natural language interface. You describe what you need in plain English, and your agent writes the complete Python script with dependency installation, API client setup, and error handling baked in.
Example prompts to try with 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”
Behind the scenes: The agent generates Python code that imports the `landingai-ade` SDK, initializes the Vision Agent client with your API key, processes documents using the appropriate ADE methods, and handles output formatting. Every extracted value includes bounding boxes, page coordinates, and confidence scores—making the output fully traceable back to the source document.
Sample Python snippet (conceptual – your agent will generate the actual code):
from landingai_ade import VisionAgent
import os
client = VisionAgent(api_key=os.getenv("VISION_AGENT_API_KEY"))
result = client.parse("invoice.pdf", output_format="markdown")
print(result.markdown)
Access traceability data
for element in result.elements:
print(f"Text: {element.text}, Page: {element.page}, Confidence: {element.confidence}")
- Traceability and Auditability: Bounding Boxes, Confidence Scores, and Visual Grounding
One of ADE’s standout features is its commitment to traceability. Unlike black-box OCR systems that give you text with no provenance, ADE returns every extracted value with precise coordinates and confidence metrics.
What this enables:
- Audit trails – You can point to the exact location in the source document where a value was extracted
- Quality control – Confidence scores allow you to flag low-confidence extractions for human review
- Debugging – When extraction fails, you can visually overlay bounding boxes to understand why
- Compliance – Financial and legal applications require traceable extraction for regulatory purposes
Step‑by‑step: Visualizing extraction results
- Use the `document-workflows` skill’s visualization capabilities to generate bounding box overlays:
"Write a script that processes this document and generates a PDF with bounding boxes overlaid on every extracted field"
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The script will produce annotated outputs showing exactly where each piece of data came from, with page numbers and confidence scores printed alongside.
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For word-level grounding, you can highlight specific terms within document sections with translucent overlays – useful for contract review and compliance checks.
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RAG Preparation: Chunking, Embeddings, and Vector Database Ingestion
Modern AI applications increasingly rely on Retrieval-Augmented Generation (RAG) to ground LLM responses in factual document content. ADE’s `document-workflows` skill includes dedicated RAG preparation capabilities.
Step‑by‑step: Building a RAG pipeline with ADE
- Parse documents – Use `document-extraction` to convert PDFs, images, and spreadsheets into structured Markdown with layout preservation.
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Semantic chunking – The workflow skill intelligently chunks documents based on semantic boundaries (sections, paragraphs, tables) rather than fixed token counts, preserving context.
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Generate embeddings – ADE integrates with embedding models to create vector representations of each chunk.
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Ingest into vector database – The skill supports ChromaDB and FAISS out of the box, with export options for other vector stores.
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Query and retrieve – Your RAG application can now retrieve relevant document chunks based on semantic similarity, complete with page coordinates and confidence scores for citation.
Example prompt:
“Write a script that reads all PDFs in a folder, extracts their content using ADE, chunks them semantically, generates embeddings, and ingests them into a ChromaDB collection for RAG”
6. Batch Processing and Parallel Execution
For enterprise-scale document processing, ADE supports both synchronous and asynchronous batch processing with parallel execution.
Step‑by‑step: Processing a folder of 1,000 documents
- Your agent generates a script using `ThreadPoolExecutor` for parallel processing (or async patterns for I/O-bound operations).
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The script iterates through all documents in the folder, respecting file format diversity (PDF, images, spreadsheets, presentations – over 20 formats supported).
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Each document is processed independently, with results aggregated into a single DataFrame or CSV.
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For mixed document batches, the classify-then-extract pipeline automatically detects document types and routes them to the appropriate extraction logic.
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Progress tracking and error handling are built into the generated scripts, so your batch doesn’t fail on a single corrupted file.
Performance considerations:
- ADE handles documents up to 1 GB or 6,000 pages asynchronously
- Parallel processing scales linearly with available CPU cores
- For cloud deployments, consider using serverless functions with the ADE SDK
7. Exporting to DataFrames, CSV, and Snowflake
Production document processing isn’t complete until the extracted data lands in your analytics or reporting systems. ADE’s workflow skill provides native export capabilities.
Step‑by‑step: Exporting invoice data to Snowflake
- Extract fields from invoices using a Pydantic model or JSON schema – the agent generates the schema based on your description.
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The extracted data is structured as a list of dictionaries, ready for DataFrame conversion.
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Use the export function to write to CSV, or directly to Snowflake using the Snowflake Python connector.
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For ongoing pipelines, schedule the script to run daily, processing new documents as they arrive.
Example prompt:
“Write a script that extracts vendor name, invoice number, total amount, and line items from all PDFs in a folder, then exports the results to a Snowflake table called ‘invoices_raw'”
Code pattern (generated by your agent):
import pandas as pd
from landingai_ade import VisionAgent
Extraction logic here...
results = [] list of extracted dictionaries
df = pd.DataFrame(results)
df.to_csv("invoices.csv", index=False)
Or for Snowflake:
df.to_sql("invoices_raw", snowflake_connection, if_exists="append", index=False)
What Undercode Say:
- Key Takeaway 1: Traditional OCR is dead for complex documents – ADE’s vision-first approach preserves layout, tables, and multi-column reading order without brittle templates, achieving 99.16% on DocVQA benchmarks.
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Key Takeaway 2: The agentic paradigm shift means you no longer write extraction code manually – you describe the business need in plain English, and your AI coding assistant generates production-ready Python scripts with full error handling and dependency management.
Analysis: The ADE skills represent a fundamental shift in how document processing pipelines are built. Instead of data scientists spending weeks training custom models or engineers writing fragile regex-based parsers, organizations can now deploy AI agents that write their own extraction code on the fly. The traceability features (bounding boxes, confidence scores) address the “black box” criticism that has plagued AI document processing, making ADE suitable for regulated industries like finance and healthcare. The RAG preparation capabilities also position ADE as a critical tool in the LLM ecosystem – high-quality retrieval starts with high-quality document parsing, and ADE delivers layout-aware Markdown that preserves semantic structure. The only notable limitation is the dependency on LandingAI’s proprietary Vision Agent API, which introduces vendor lock-in and latency considerations for on-premise deployments. However, the free trial and pay-as-you-go pricing model lower the barrier to entry for proof-of-concept work.
Prediction:
- +1 ADE-style agentic document extraction will become the default pattern for enterprise document processing within 24 months, displacing traditional OCR and template-based solutions as AI coding agents proliferate across development teams.
- +1 The integration of extraction with RAG pipelines will accelerate LLM adoption in enterprise settings, as high-quality document grounding becomes accessible without specialized ML expertise.
- +1 The “skills” paradigm (epitomized by the Agent Skills convention) will emerge as the standard distribution mechanism for AI capabilities, similar to how npm transformed JavaScript package management.
- -1 Vendor dependency on LandingAI’s proprietary models creates concentration risk – organizations should evaluate fallback options and request on-premise deployment support for sensitive workloads.
- -1 The quality of extraction still depends on document quality (scanned blurry pages, handwritten text) – ADE’s 99.16% accuracy is impressive but not perfect, and the remaining error margin may require human-in-the-loop validation for mission-critical applications.
- +1 The open-source MIT license for the skills repository will foster a community of contributors building custom workflows and integrations, potentially reducing vendor lock-in over time.
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