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MicrosoftDocs/azure-content-safety

MicrosoftDocs

azure-content-safety

Expert knowledge for Azure AI Content Safety development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Content Safety APIs, Docker containers, text blocklists, groundedness detection, or custom safety categories, and other Azure AI Content Safety related development tasks. Not for Azure Information Protection (use azure-information-protection), Azure Security (use azure-security), Azure Defender For Cloud (use azure-defender-for-cloud), Azure Sentinel (use azure-sentinel).

v1.0Latest
New~1.6kUpdated Jun 26, 2026

Azure AI Content Safety Skill

This skill provides expert guidance for Azure AI Content Safety. Covers troubleshooting, best practices, decision making, architecture & design patterns, 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 L37-L41 Diagnosing and resolving Azure AI Content Safety API errors, including HTTP status codes, common failure causes, and recommended fixes or retries.
Best Practices L42-L46 Tuning Content Safety thresholds, categories, and prompts to reduce misclassifications, plus strategies to balance safety, recall, and user experience.
Decision Making L47-L52 Guidance on migrating apps from Content Safety preview to GA and deciding when and how to use limited-access Content Safety features and models.
Architecture & Design Patterns L53-L57 Architectural guidance for combining cloud, hybrid, and on-device Azure AI Content Safety, including design patterns, deployment options, and integration strategies.
Limits & Quotas L58-L64 Language coverage, building and training custom safety categories, and detecting protected/third‑party code in model outputs.
Security L65-L69 Details on how Azure AI Content Safety encrypts data at rest, including encryption models, key management options, and compliance/security considerations.
Configuration L70-L75 Configuring Content Safety runtime via Docker containers and setting up/managing text blocklists to customize and enforce content filtering rules
Integrations & Coding Patterns L76-L80 Using the groundedness detection API to check if AI responses are supported by source content, with request/response formats, parameters, and integration patterns
Deployment L81-L86 How to install, configure, and run Azure AI Content Safety Docker containers for text, image, and prompt shield analysis in your own environment.

Troubleshooting

Topic URL
Resolve Azure AI Content Safety API error codes https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/response-codes

Best Practices

Topic URL
Reduce false positives and negatives in Content Safety https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/improve-performance

Decision Making

Topic URL
Migrate apps from Content Safety preview to GA https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/migrate-to-general-availability
Decide when to use limited access Content Safety features https://learn.microsoft.com/en-us/azure/ai-services/content-safety/limited-access

Architecture & Design Patterns

Topic URL
Design hybrid and on-device Content Safety solutions https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/embedded-content-safety

Limits & Quotas

Topic URL
Check language support for Azure AI Content Safety https://learn.microsoft.com/en-us/azure/ai-services/content-safety/language-support
Create and train custom categories with Content Safety https://learn.microsoft.com/en-us/azure/ai-services/content-safety/quickstart-custom-categories
Use protected material detection for code outputs https://learn.microsoft.com/en-us/azure/ai-services/content-safety/quickstart-protected-material-code

Security

Topic URL
Understand data-at-rest encryption in Content Safety https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/encrypt-data-at-rest

Configuration

Topic URL
Configure and run Azure AI Content Safety Docker containers https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/containers/install-run-container
Configure and use text blocklists in Content Safety https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/use-blocklist

Integrations & Coding Patterns

Topic URL
Use Azure AI Content Safety groundedness detection API https://learn.microsoft.com/en-us/azure/ai-services/content-safety/quickstart-groundedness

Deployment

Topic URL
Deploy image analysis Content Safety container with Docker https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/containers/image-container
Run Prompt Shields Content Safety container for prompt attacks https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/containers/prompt-shields-container
Deploy text analysis Content Safety container with Docker https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/containers/text-container
Files1
1 files · 18.2 KB

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Overall Score

78/100

Grade

B

Good

Safety

82

Quality

77

Clarity

81

Completeness

70

Summary

This skill provides expert guidance for Azure AI Content Safety development, covering troubleshooting, best practices, architecture patterns, security, configuration, integrations, and deployment. It functions as a curated knowledge index that directs agents to fetch detailed documentation from Microsoft Learn via network requests and includes local quick-reference content structured by category.

Detected Capabilities

network requestdocumentation fetchfile readurl reference

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

azure content safetycontent moderation apidocker containerstext blocklistsgroundedness detectionprompt shieldcustom categoriesazure ai safety

Risk Signals

INFO

Network requests to learn.microsoft.com for documentation

SKILL.md: How to Use This Skill section
INFO

Dependency on external mcp_microsoftdocs tool availability

SKILL.md: compatibility field and How to Use section
INFO

Reference to third-party GitHub repository for tool installation

SKILL.md: How to Use This Skill section

Referenced Domains

External domains referenced in skill content, detected by static analysis.

github.comlearn.microsoft.com

Use Cases

  • Diagnose and resolve Azure AI Content Safety API errors
  • Optimize content moderation thresholds and categories
  • Design hybrid and on-device content filtering solutions
  • Configure Docker containers for text and image analysis
  • Implement custom safety categories and blocklists
  • Integrate groundedness detection into AI applications
  • Deploy prompt shield containers for attack detection
  • Understand data encryption and compliance requirements

Quality Notes

  • Well-structured category index with line ranges enabling efficient content discovery
  • Clear separation of concerns — local reference content vs. remote documentation fetching
  • Explicit fallback mechanism documented (mcp_microsoftdocs → fetch_webpage)
  • Helpful guidance for agents on currency of content (3-month staleness check) and tool availability
  • Comprehensive scope covering 8 major topic areas with relevant URLs
  • Good use of tables to organize topics and URLs
  • Potential friction: skill relies on external tool availability with no offline fallback
  • Metadata timestamp allows skill freshness validation
  • Instructions for excluding out-of-scope Azure services (Information Protection, Security, Defender, Sentinel)
Model: claude-haiku-4-5-20251001Analyzed: Jun 26, 2026

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