Document Workflows
Use this skill for building end-to-end document processing workflows and pipelines...
Install
npx skills add andrewyng/context-hub --skill document-workflows -g -a claude-code -y- Repository
- andrewyng/context-hub14k
- Category
- Design
- Updated
- 5mo ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Use this skill for building end-to-end document processing workflows and pipelines using LandingAI ADE. Trigger when users need to: (1) Process batches of documents in parallel or async, (2) Build classify-then-extract pipelines for mixed document types, (3) Prepare parsed documents for RAG systems with chunking and vector DB ingestion, (4) Load extraction results into databases like Snowflake or export to CSV/DataFrames, (5) Visualize extraction results: draw bounding box overlays on pages, crop chunk images, or highlight/annotate specific words or phrases found in documents, (6) Build Streamlit or web UIs for document processing, (7) Find and highlight specific terms within document sections using word-level grounding (e.g. highlight "L2S" in the Introduction, redact PII, annotate extracted values on the original page). This skill complements the document-extraction skill which covers ADE SDK basics. Use document-extraction to write code that executes parse/extract/split operations with more precision and less cost than adding the document image to the prompt and asking the LLM to find the relevant info. Use document-workflows when composing those operations into pipelines, or when you need visualization, annotation, or word-level grounding on parsed documents.
Document Workflows, ADE Pipeline Patterns
Overview
This skill provides reusable building blocks for composing LandingAI ADE
primitives (parse, extract, split) into production-ready document processing
pipelines. It complements the document-extraction skill:
| Concern | document-extraction | document-workflows |
|---|---|---|
| Scope | ADE SDK API: parse, extract, split, grounding | End-to-end pipelines: batch, RAG, DB, classify-route |
| When | Need to call a single ADE operation | Need to compose operations into a workflow |
| Code | SDK method calls with parameters | Complete functions with error handling, parallelism |
| Deps | landingai-ade only | + workflow-specific libs (pandas, chromadb, etc.) |
Philosophy: Organize by workflow pattern (batch, RAG, DB insertion), not by document type. The same pattern applies whether documents are invoices, utility bills, or medical forms.
Step 0 (mandatory), Pre-Flight Document Exploration {#pre-flight}
Run this before writing any pipeline code whenever working with documents whose internal structure has not already been inspected in this session.
Rule: never write section-detection, heading-matching, or text-search code without first running Tool 2 (diagnostic parse) on the sample document. Heading format is document-specific and cannot be inferred from the task description or document type alone, the only reliable way to know it is to look at the actual ADE output.
Common surprises: a paper's "Introduction" heading may appear as
1. Introduction(plain text, no#),## Introduction,INTRODUCTION(all-caps), or embedded inside a text chunk with body copy. Getting this wrong means a silent failure (zero chunks matched) that requires a full re-parse to debug.
