mcpField
A
azure-face

azure-face

github.com/microsoftdocsDevelopment79665 installsUpdated 3d ago
Repository

About azure-face

azure-face is a guidance skill for developers and agents working with Azure AI Face. It combines local references with remote documentation on API use, security, scaling, and troubleshooting.

Key features

  1. 01Troubleshooting API errors
  2. 02Face enrollment guidance
  3. 03Detection and recognition model selection
  4. 04Quota and scaling guidance
  5. 05Security and access controls
  6. 06API integration patterns

Use cases

  1. 01Diagnosing Face API failures
  2. 02Planning large-scale face enrollment
  3. 03Selecting detection and recognition models
  4. 04Reviewing liveness security controls

MCP support

The skill can fetch remote documentation through mcp_microsoftdocs:microsoft_docs_fetch; it also documents a fetch_webpage fallback.

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/microsoftdocs/agent-skills --skill azure-face

After installing, type/azure-facein Claude Code to confirm it loaded.

Details

From the project README

Azure AI Face Skill

This skill provides expert guidance for Azure AI Face. Covers troubleshooting, best practices, decision making, limits & quotas, security, and integrations & coding patterns. 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
TroubleshootingL34-L38Diagnosing and fixing Azure Face API failures by interpreting error codes, understanding causes (quota, auth, input issues), and applying recommended resolutions.
Best PracticesL39-L47Guidance on enrolling faces, scaling PersonGroup/PersonDirectory, optimizing performance/latency, and building consent-aware, high-capacity Azure Face enrollment workflows.
Decision MakingL48-L53Guidance on choosing, configuring, and tuning Azure Face detection and recognition models, including model types, capabilities, parameters, and selection trade-offs.
Limits & QuotasL54-L59Scaling PersonGroup for large face datasets and understanding Face API quotas, rate limits, and maximum sizes for persons, faces, and training operations.
SecurityL60-L69Security and compliance for Face and liveness: abuse monitoring, token-based access control, network isolation, encryption/CMK, shared responsibility, and secure SDK version management.
Integrations & Coding PatternsL70-L73How to call Azure Face API endpoints, use key operations (detect, identify, verify, find similar), and structure requests/responses in your applications.
Troubleshooting
TopicURL
Resolve Azure Face API errors using error codeshttps://learn.microsoft.com/en-us/azure/ai-services/face/reference-face-error-codes
Best Practices
TopicURL
Apply best practices for Azure Face enrollmenthttps://learn.microsoft.com/en-us/azure/ai-services/face/enrollment-overview
Add many faces to PersonGroup efficientlyhttps://learn.microsoft.com/en-us/azure/ai-services/face/how-to/add-faces
Optimize Azure Face performance and reduce latencyhttps://learn.microsoft.com/en-us/azure/ai-services/face/how-to/mitigate-latency
Use PersonDirectory for high-capacity face storagehttps://learn.microsoft.com/en-us/azure/ai-services/face/how-to/use-persondirectory
Implement consent-focused Face enrollment apphttps://learn.microsoft.com/en-us/azure/ai-services/face/tutorials/build-enrollment-app
Decision Making
TopicURL
Choose and specify Azure Face detection modelshttps://learn.microsoft.com/en-us/azure/ai-services/face/how-to/specify-detection-model
Select and configure Azure Face recognition modelshttps://learn.microsoft.com/en-us/azure/ai-services/face/how-to/specify-recognition-model
Limits & Quotas
TopicURL
Scale PersonGroup objects to large Face datasetshttps://learn.microsoft.com/en-us/azure/ai-services/face/how-to/use-large-scale
Review Azure Face service quotas and limitshttps://learn.microsoft.com/en-us/azure/ai-services/face/identity-quotas-limits
Security
TopicURL
Configure abuse monitoring for Face liveness detectionhttps://learn.microsoft.com/en-us/azure/ai-services/face/concept-liveness-abuse-monitoring
Manage Face API access with limited tokenshttps://learn.microsoft.com/en-us/azure/ai-services/face/how-to/identity-access-token
Use Face liveness detection with network isolationhttps://learn.microsoft.com/en-us/azure/ai-services/face/how-to/liveness-use-network-isolation
Configure encryption and CMK for Azure Face datahttps://learn.microsoft.com/en-us/azure/ai-services/face/identity-encrypt-data-at-rest
Secure Face liveness solutions with shared responsibilityhttps://learn.microsoft.com/en-us/azure/ai-services/face/liveness-detection-shared-responsibility
Manage liveness client SDK versions for securityhttps://learn.microsoft.com/en-us/azure/ai-services/face/sdk/understand-the-liveness-sdk-versions
Integrations & Coding Patterns
TopicURL
Use Azure Face API endpoints and operationshttps://learn.microsoft.com/en-us/azure/ai-services/face/identity-api-reference

Comments

Markdown supported