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Trigger when users need to (1) Parse documents (PDFs, images, spreadsheets, presentations) into structured Markdown with layout understanding, (2) Extract specific structured data from documents using schemas (invoice fields, form data, table data, etc.), (3) Classify and separate multi-document batches by type (invoices vs receipts, statements vs forms, etc.), (4) Process large documents asynchronously (up to 1GB/1000 pages), (5) Get visual grounding (bounding boxes, page numbers) for extracted content — use when users mention bounding boxes, word locations, grounding, highlighting extracted content, or showing where data appears in a document. Use this skill when the task involves understanding document content for a set of documents. In particular this skill can help you write code that run on sets of documents. This will increase speed, and reduce the cost of loading the documents on the Agent context window because you can use a single script to extract the information needed.", "source": "community", "tags": [], "path": "landingai/skills/ade/document-extraction", "files": [ "SKILL.md", ".gitignore", ".env-sample" ], "size": 34223, "lastUpdated": "2026-03-30", "contentHash": "ff91d6eac78f50f952ac811689a2733e5304e85c796f84e876ed65aa8b058877" }, { "id": "landingai/document-workflows", "name": "document-workflows", "description": "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.", "source": "community", "tags": [], "path": "landingai/skills/ade/document-workflows", "files": [ "SKILL.md" ], "size": 33126, "lastUpdated": "2026-03-30", "contentHash": "64f7e9058cfd5901ba480eec726dc6a880325b1efaecc5eaca72935afed986f1" }, { "id": "chub/Chub Workflow", "name": "Chub Workflow", "description": "Complete workflow for AI agents using Chub: how to query docs efficiently and how to write structured annotations back to build persistent team knowledge", "source": "maintainer", "tags": [ "chub", "docs", "annotations", "workflow", "knowledge-management" ], "path": "chub/skills/chub-workflow", "files": [ "SKILL.md" ], "size": 5935, "lastUpdated": "2026-03-30", "contentHash": "1282483e0f50be5cb1d0b9fd629c4ddba03f8b0cbcbe9e8cdcbbb49d4e777067" }, { "id": "olakai/integrate", "name": "integrate", "description": "Add Olakai monitoring to existing AI code — wrap your LLM client, configure custom KPIs, and validate the integration end-to-end", "source": "maintainer", "tags": [ "olakai", "integration", "monitoring", "sdk", "kpi", "governance" ], "path": "olakai/skills/integrate", "files": [ "SKILL.md" ], "size": 18235, "lastUpdated": "2026-03-10", "revision": "1", "contentHash": "afdfd996e77ec4eaa13cb819d81050092e9ed6edea77ad0bdb36359702eb3246" }, { "id": "olakai/new-project", "name": "new-project", "description": "Build a new AI agent with Olakai monitoring from scratch — project setup, SDK integration, KPI configuration, and end-to-end validation", "source": "maintainer", "tags": [ "olakai", "new-project", "agent", "monitoring", "kpi", "governance" ], "path": "olakai/skills/new-project", "files": [ "SKILL.md" ], "size": 20058, "lastUpdated": "2026-03-10", "revision": "1", "contentHash": "7122b3a9acae4b3a354d22c35e119edf9d7ed787e46bf022827f2fd6c9bde2b4" }, { "id": "sourceparts/electronics-sourcing", "name": "electronics-sourcing", "description": "Guide for AI agents to source electronic components using parts-mcp — tool sequencing, decision patterns, and multi-step workflows", "source": "maintainer", "tags": [ "electronics", "sourcing", "bom", "components", "pcb", "manufacturing", "workflow" ], "path": "sourceparts/skills/electronics-sourcing", "files": [ "SKILL.md" ], "size": 7092, "lastUpdated": "2026-03-15", "revision": "1", "contentHash": "ca56883c65b71e958e54df4caa14c8e470ac24a9a57b21dc3a78be782614b24b" } ], "base_url": "https://cdn.chub.nrl.ai/v1" }