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azure-document-intelligence

Expert knowledge for Azure AI Document Intelligence development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AnalyzeDocument APIs, custom/classification/template models, batch analysis, containers, or v4.0 migration, and other Azure AI Document Intelligence related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI Custom Vision (use azure-custom-vision), Azure AI Search (use azure-cognitive-search), Azure AI Video Indexer (use azure-video-indexer).

Published by MicrosoftDocs ↗MicrosoftDocs/Agent-Skills

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SKILL.md

name: azure-document-intelligencedescription: Expert knowledge for Azure AI Document Intelligence development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using AnalyzeDocument APIs, custom/classification/template models, batch analysis, containers, or v4.0 migration, and other Azure AI Document Intelligence related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI Custom Vision (use azure-custom-vision), Azure AI Search (use azure-cognitive-search), Azure AI Video Indexer (use azure-video-indexer).compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.metadata:   generated_at: "2026-10-25"  generator: "docs2skills/1.0.0"

Azure AI Document Intelligence Skill

This skill provides expert guidance for Azure AI Document Intelligence. Covers troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. It combines local quick-reference content with remote documentation fetching capabilities.

How to Use This Skill

IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g., L35-L120), use read_file with the specified lines. For categories with file links (e.g., [security.md](security.md)), use read_file on the linked reference file

IMPORTANT for Agent: If metadata.generated_at is more than 3 months old, suggest the user pull the latest version from the repository. If mcp_microsoftdocs tools are not available, suggest the user install it: Installation Guide

This skill requires network access to fetch documentation content:

  • Preferred: Use mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.
  • Fallback: Use fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.

Category Index

CategoryLinesDescription
TroubleshootingL36-L42Diagnosing latency, understanding and fixing Document Intelligence API error codes, and handling known service issues and limitations.
Best PracticesL43-L54Guidance on designing, training, labeling, composing, and maintaining custom/classification/template models to maximize Document Intelligence accuracy and confidence
Decision MakingL55-L61Choosing the right Document Intelligence model, estimating and optimizing usage/costs, and planning or executing migration to v4.0 from earlier versions.
Limits & QuotasL62-L72Docs on capacity limits, quotas, supported languages/locales, scaling with batch analysis and add-ons, and container image tags for deploying Azure Document Intelligence at different tiers.
SecurityL73-L80Securing Document Intelligence resources: creating SAS tokens, configuring data-at-rest encryption with customer-managed keys, and using managed identities and VNETs for secure access.
ConfigurationL81-L85How to configure and run Azure Document Intelligence in containers, including environment settings, networking, storage, licensing, and deployment options for on-premises or hybrid scenarios.
Integrations & Coding PatternsL86-L95Using Document Intelligence APIs/SDKs, interpreting AnalyzeDocument/Markdown outputs, and integrating with Azure Functions or Logic Apps for end-to-end document processing workflows
DeploymentL96-L102Running Document Intelligence in Docker/offline, deploying the labeling tool, and setting up resilient, disaster‑ready deployments for models and services

Troubleshooting

Best Practices

Decision Making

Limits & Quotas

Security

Configuration

Integrations & Coding Patterns

Deployment

azure-document-intelligence by MicrosoftDocs · The Skill Leaderboard