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 Docker, text/image/prompt shield APIs, blocklists, provenance, or groundedness checks, and other Azure AI Content Safety related development tasks. Not for Azure Security (use azure-security), Azure Defender For Cloud (use azure-defender-for-cloud), Azure Sentinel (use azure-sentinel).
npx skills add https://github.com/MicrosoftDocs/Agent-Skills --skill azure-content-safety
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.
> 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), 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:
mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.| 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-L81 | Calling Content Safety APIs for provenance detection and groundedness checks, including request/response patterns, parameters, and integration examples for detecting source and factual alignment. |
| Deployment | L82-L87 | How to install, configure, and run Azure AI Content Safety Docker containers for text, image, and prompt shield analysis in your own environment. |
| Topic | URL |
|-------|-----|
| Resolve Azure AI Content Safety API error codes | https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/response-codes |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| Topic | URL |
|-------|-----|
| Call Azure Content Safety Provenance Detect API | https://learn.microsoft.com/en-us/azure/ai-services/content-safety/how-to/how-to-provenance-detection |
| Use Azure AI Content Safety groundedness detection API | https://learn.microsoft.com/en-us/azure/ai-services/content-safety/quickstart-groundedness |
| 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 |
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take microsoftdocs/azure-content-safety from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.