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, blocklists, groundedness checks, 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 Sentinel (use azure-sentinel), Azure Defender For Cloud (use azure-defender-for-cloud).
name: azure-content-safety
description: 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, blocklists, groundedness checks, 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 Sentinel (use azure-sentinel), Azure Defender For Cloud (use azure-defender-for-cloud).
compatibility: Requires network access. Uses mcp_microsoftdocs:microsoft_docs_fetch or fetch_webpage to retrieve documentation.
metadata:
generated_at: "2026-04-05"
generator: "docs2skills/1.0.0"
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-L74
Configuring and using text blocklists in Azure AI Content Safety, including creating, managing, and applying custom blocked terms to filter harmful or unwanted content.
Integrations & Coding Patterns
L75-L79
Using the groundedness detection API to check if AI responses are supported by source content, with request/response formats, parameters, and integration patterns
Deployment
L80-L86
How to install, configure, and run Azure AI Content Safety Docker containers for text, image, and prompt shield analysis in your own environment.