Included skill
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 training custom models, composing templates, using REST/SDK APIs, deploying containers, or planning v4.0 upgrades, and other Azure AI Document Intelligence related development tasks. Not for Azure AI Search (use azure-cognitive-search), Azure AI Custom Vision (use azure-custom-vision), Azure AI Language (use azure-language-service), Azure AI Video Indexer (use azure-video-indexer).
Source: /skills/azure-document-intelligence/SKILL.md
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
| Category |
Lines |
Description |
| Troubleshooting |
L36-L42 |
Diagnosing latency, understanding and fixing Document Intelligence API error codes, and handling known service issues and limitations. |
| Best Practices |
L43-L54 |
Guidance on training, labeling, composing, and managing custom/classification/template models to maximize Document Intelligence accuracy, confidence, and lifecycle quality. |
| Decision Making |
L55-L61 |
Guidance on choosing the right Document Intelligence model, estimating usage/costs, and planning migration and version upgrades (including to v4.0). |
| Limits & Quotas |
L62-L72 |
Capacity add-ons, container image tags, OCR and model language/locale support, batch processing at scale, and service quotas/limits for Azure Document Intelligence. |
| Security |
L73-L80 |
Securing Document Intelligence resources: creating SAS tokens, configuring data-at-rest encryption with customer-managed keys, and using managed identities and VNETs for secure access. |
| Configuration |
L81-L86 |
Configuring and deploying Document Intelligence containers, and managing/sharing custom model projects in Document Intelligence Studio for collaborative use. |
| Integrations & Coding Patterns |
L87-L97 |
How to call Document Intelligence via REST/SDKs, use the sample tool, and integrate outputs (JSON/Markdown) into workflows with Azure Functions and Logic Apps. |
| Deployment |
L98-L105 |
Guides for deploying Document Intelligence: Docker/container setup (including offline), SDK/REST API usage, disaster recovery, and deploying the sample labeling tool. |
Troubleshooting
Best Practices
Decision Making
Limits & Quotas
Security
Configuration
Integrations & Coding Patterns
Deployment