Add Microsoft Foundry Python SDK Guide and resource creation documentation

- Introduced a comprehensive guide for the Microsoft Foundry Python SDK, detailing prerequisites, model deployment, agent creation, and evaluation processes.
- Created documentation for creating Azure AI Services multi-service resources using Azure CLI, including workflows, troubleshooting, and common patterns.
- Added detailed workflows for resource group creation, Foundry resource creation, and resource provider registration.
- Included troubleshooting steps for common errors encountered during resource creation.

Author: jongio

cli/counts.json

@@ -1,4 +1,4 @@
 {
   "agents": 16,
-  "skills": 29
+  "skills": 28
 }

cli/extension.yaml

@@ -6,7 +6,7 @@ displayName: Azure Copilot CLI
 description: |
   AI-powered Azure development assistant. Describe what you want to build,
   and Copilot builds and deploys it to Azure. Includes 16 specialized agents
-  and 29 Azure skills.
+  and 28 Azure skills.
 usage: azd copilot <command> [options]
 version: 0.1.6
 entryPoint: copilot

cli/src/internal/assets/ghcp4a-skills/appinsights-instrumentation/SKILL.md

@@ -1,15 +1,33 @@
 ---
 name: appinsights-instrumentation
-description: "Instrument web applications to send telemetry data to Azure Application Insights for observability and monitoring. USE FOR: instrument app with app insights, add appinsights instrumentation, configure application insights, set up telemetry monitoring, enable app insights auto-instrumentation, add observability to azure web app, instrument webapp to send data to app insights, configure telemetry for app service. DO NOT USE FOR: non-Azure monitoring (use CloudWatch for AWS, Datadog for third-party), log analysis (use azure-kusto), cost monitoring (use azure-cost-optimization), security monitoring (use azure-security)."
+description: |
+  Guidance for instrumenting webapps with Azure Application Insights. Provides telemetry patterns, SDK setup, and configuration references.
+  USE FOR: how to instrument app, App Insights SDK, telemetry patterns, what is App Insights, Application Insights guidance, instrumentation examples, APM best practices.
+  DO NOT USE FOR: adding App Insights to my app (use azure-prepare), add telemetry to my project (use azure-prepare), add monitoring (use azure-prepare). This skill provides guidance—azure-prepare orchestrates component changes.
 ---
 
-# AppInsights instrumentation
+# AppInsights Instrumentation Guide
 
-This skill enables sending telemetry data of a webapp to Azure App Insights for better observability of the app's health.
+This skill provides **guidance and reference material** for instrumenting webapps with Azure Application Insights.
 
-## When to use this skill
+> **⛔ ADDING COMPONENTS?**
+>
+> If the user wants to **add App Insights to their app**, invoke **azure-prepare** instead.
+> This skill provides reference material—azure-prepare orchestrates the actual changes.
 
-Use this skill when the user wants to enable telemetry for their webapp.
+## When to Use This Skill
+
+- User asks **how** to instrument (guidance, patterns, examples)
+- User needs SDK setup instructions
+- azure-prepare invokes this skill during research phase
+- User wants to understand App Insights concepts
+
+## When to Use azure-prepare Instead
+
+- User says "add telemetry to my app"
+- User says "add App Insights" 
+- User wants to modify their project
+- Any request to change/add components
 
 ## Prerequisites
 
@@ -46,3 +64,8 @@ No matter which option you choose, recommend the user to create the App Insights
 - If the app is an ASP.NET Core app, see [ASPNETCORE guide](references/aspnetcore.md) for how to modify the C# code.
 - If the app is a Node.js app, see [NODEJS guide](references/nodejs.md) for how to modify the JavaScript/TypeScript code.
 - If the app is a Python app, see [PYTHON guide](references/python.md) for how to modify the Python code.
+
+## SDK Quick References
+
+- **OpenTelemetry Distro**: [Python](references/sdk/azure-monitor-opentelemetry-py.md) | [TypeScript](references/sdk/azure-monitor-opentelemetry-ts.md)
+- **OpenTelemetry Exporter**: [Python](references/sdk/azure-monitor-opentelemetry-exporter-py.md) | [Java](references/sdk/azure-monitor-opentelemetry-exporter-java.md)

cli/src/internal/assets/ghcp4a-skills/appinsights-instrumentation/references/aspnetcore.md

@@ -16,14 +16,14 @@ An ASP.NET Core app typically has a Program.cs file that "builds" the app. Find
 
 ## Configure App Insights connection string
 
-The App Insights resource has a connection string. Add the connection string as an environment variable of the running app. You can use Azure CLI to query the connection string of the App Insights resource. See [scripts/appinsights.ps1](scripts/appinsights.ps1) for what Azure CLI command to execute for querying the connection string.
+The App Insights resource has a connection string. Add the connection string as an environment variable of the running app. You can use Azure CLI to query the connection string of the App Insights resource. See [scripts/appinsights.ps1](../scripts/appinsights.ps1) for what Azure CLI command to execute for querying the connection string.
 
 After getting the connection string, set this environment variable with its value.
 
 ```
 "APPLICATIONINSIGHTS_CONNECTION_STRING={your_application_insights_connection_string}"
 ```
 
-If the app has IaC template such as Bicep or terraform files representing its cloud instance, this environment variable should be added to the IaC template to be applied in each deployment. Otherwise, use Azure CLI to manually apply the environment variable to the cloud instance of the app. See [scripts/appinsights.ps1](scripts/appinsights.ps1) for what Azure CLI command to execute for setting this environment variable.
+If the app has IaC template such as Bicep or terraform files representing its cloud instance, this environment variable should be added to the IaC template to be applied in each deployment. Otherwise, use Azure CLI to manually apply the environment variable to the cloud instance of the app. See [scripts/appinsights.ps1](../scripts/appinsights.ps1) for what Azure CLI command to execute for setting this environment variable.
 
 > Important: Don't modify appsettings.json. It was a deprecated way to configure App Insights. The environment variable is the new recommended way.

cli/src/internal/assets/ghcp4a-skills/appinsights-instrumentation/references/sdk/azure-monitor-opentelemetry-exporter-java.md

@@ -0,0 +1,31 @@
+# Azure Monitor OpenTelemetry Exporter — Java SDK Quick Reference
+
+> Condensed from **azure-monitor-opentelemetry-exporter-java**. Full patterns
+> (trace/metric/log export, spans, semantic conventions)
+> in the **azure-monitor-opentelemetry-exporter-java** plugin skill if installed.
+
+## Install
+```xml
+<dependency>
+    <groupId>com.azure</groupId>
+    <artifactId>azure-monitor-opentelemetry-exporter</artifactId>
+    <version>1.0.0-beta.x</version>
+</dependency>
+```
+
+> **DEPRECATED**: Migrate to `azure-monitor-opentelemetry-autoconfigure`.
+
+## Quick Start
+```java
+// Prefer autoconfigure instead:
+// <artifactId>azure-monitor-opentelemetry-autoconfigure</artifactId>
+```
+
+## Best Practices
+- Use autoconfigure — migrate to `azure-monitor-opentelemetry-autoconfigure`
+- Set meaningful span names — use descriptive operation names
+- Add relevant attributes — include contextual data for debugging
+- Handle exceptions — always record exceptions on spans
+- Use semantic conventions — follow OpenTelemetry semantic conventions
+- End spans in finally — ensure spans are always ended
+- Use try-with-resources — scope management with try-with-resources pattern

cli/src/internal/assets/ghcp4a-skills/appinsights-instrumentation/references/sdk/azure-monitor-opentelemetry-exporter-py.md

@@ -0,0 +1,22 @@
+# Azure Monitor OpenTelemetry Exporter — Python SDK Quick Reference
+
+> Condensed from **azure-monitor-opentelemetry-exporter-py**. Full patterns
+> (metric exporter, log exporter, offline storage, sovereign clouds)
+> in the **azure-monitor-opentelemetry-exporter-py** plugin skill if installed.
+
+## Install
+pip install azure-monitor-opentelemetry-exporter
+
+## Quick Start
+```python
+from azure.monitor.opentelemetry.exporter import AzureMonitorTraceExporter
+exporter = AzureMonitorTraceExporter()  # reads APPLICATIONINSIGHTS_CONNECTION_STRING
+```
+
+## Best Practices
+- Use BatchSpanProcessor for production (not SimpleSpanProcessor)
+- Use ApplicationInsightsSampler for consistent sampling across services
+- Enable offline storage for reliability in production
+- Use AAD authentication instead of instrumentation keys
+- Set export intervals appropriate for your workload
+- Use the distro (azure-monitor-opentelemetry) unless you need custom pipelines

cli/src/internal/assets/ghcp4a-skills/appinsights-instrumentation/references/sdk/azure-monitor-opentelemetry-py.md

@@ -0,0 +1,23 @@
+# Azure Monitor OpenTelemetry — Python SDK Quick Reference
+
+> Condensed from **azure-monitor-opentelemetry-py**. Full patterns
+> (Flask/Django/FastAPI, custom metrics, sampling, live metrics)
+> in the **azure-monitor-opentelemetry-py** plugin skill if installed.
+
+## Install
+pip install azure-monitor-opentelemetry
+
+## Quick Start
+```python
+from azure.monitor.opentelemetry import configure_azure_monitor
+configure_azure_monitor()
+```
+
+## Best Practices
+- Call configure_azure_monitor() early — before importing instrumented libraries
+- Use environment variables for connection string in production
+- Set cloud role name for multi-service Application Map
+- Enable sampling in high-traffic applications
+- Use structured logging for better log analytics queries
+- Add custom attributes to spans for better debugging
+- Use AAD authentication for production workloads

cli/src/internal/assets/ghcp4a-skills/appinsights-instrumentation/references/sdk/azure-monitor-opentelemetry-ts.md

@@ -0,0 +1,26 @@
+# Azure Monitor OpenTelemetry — TypeScript SDK Quick Reference
+
+> Condensed from **azure-monitor-opentelemetry-ts**. Full patterns
+> (ESM loader, custom span processors, manual exporters, live metrics)
+> in the **azure-monitor-opentelemetry-ts** plugin skill if installed.
+
+## Install
+npm install @azure/monitor-opentelemetry
+
+## Quick Start
+```typescript
+import { useAzureMonitor } from "@azure/monitor-opentelemetry";
+useAzureMonitor({
+  azureMonitorExporterOptions: {
+    connectionString: process.env.APPLICATIONINSIGHTS_CONNECTION_STRING
+  }
+});
+```
+
+## Best Practices
+- Call useAzureMonitor() first — before importing other modules
+- Use ESM loader for ESM projects — `--import @azure/monitor-opentelemetry/loader`
+- Enable offline storage for reliable telemetry in disconnected scenarios
+- Set sampling ratio for high-traffic applications
+- Add custom dimensions — use span processors for enrichment
+- Graceful shutdown — call shutdownAzureMonitor() to flush telemetry

cli/src/internal/assets/ghcp4a-skills/azure-ai/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: azure-ai
-description: "Use for Azure AI: Search, Speech, Foundry, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, AI agents, prompt flows, OCR. USE FOR: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, AI agent, prompt flow, Foundry, OCR, convert text to speech. DO NOT USE FOR: Function apps/Functions (use azure-functions), databases (azure-postgres/azure-kusto), resources."
+description: "Use for Azure AI: Search, Speech, OpenAI, Document Intelligence. Helps with search, vector/hybrid search, speech-to-text, text-to-speech, transcription, OCR. USE FOR: AI Search, query search, vector search, hybrid search, semantic search, speech-to-text, text-to-speech, transcribe, OCR, convert text to speech. DO NOT USE FOR: Function apps/Functions (use azure-functions), databases (azure-postgres/azure-kusto), general Azure resources."
 ---
 
 # Azure AI Services
@@ -11,7 +11,6 @@ description: "Use for Azure AI: Search, Speech, Foundry, OpenAI, Document Intell
 |---------|----------|-----------|-----|
 | AI Search | Full-text, vector, hybrid search | `azure__search` | `az search` |
 | Speech | Speech-to-text, text-to-speech | `azure__speech` | - |
-| Foundry | AI models, agents, prompt flows | `azure__foundry` | `az ml` |
 | OpenAI | GPT models, embeddings, DALL-E | - | `az cognitiveservices` |
 | Document Intelligence | Form extraction, OCR | - | - |
 
@@ -28,11 +27,6 @@ When Azure MCP is enabled:
 - `azure__speech` with command `speech_transcribe` - Speech to text
 - `azure__speech` with command `speech_synthesize` - Text to speech
 
-### Foundry
-- `azure__foundry` with command `foundry_model_list` - List AI models
-- `azure__foundry` with command `foundry_deployment_list` - List deployments
-- `azure__foundry` with command `foundry_agent_list` - List AI agents
-
 **If Azure MCP is not enabled:** Run `/azure:setup` or enable via `/mcp`.
 
 ## AI Search Capabilities
@@ -53,19 +47,21 @@ When Azure MCP is enabled:
 | Speaker diarization | Identify who spoke when |
 | Custom models | Domain-specific vocabulary |
 
-## Foundry Capabilities
+## SDK Quick References
 
-| Feature | Description |
-|---------|-------------|
-| Model catalog | GPT-4, Llama, Mistral, custom |
-| AI agents | Multi-turn, tool calling, RAG |
-| Prompt flow | Orchestration, evaluation |
-| Fine-tuning | Custom model training |
+For programmatic access to these services, see the condensed SDK guides:
+
+- **AI Search**: [Python](references/sdk/azure-search-documents-py.md) | [TypeScript](references/sdk/azure-search-documents-ts.md) | [.NET](references/sdk/azure-search-documents-dotnet.md)
+- **OpenAI**: [.NET](references/sdk/azure-ai-openai-dotnet.md)
+- **Vision**: [Python](references/sdk/azure-ai-vision-imageanalysis-py.md) | [Java](references/sdk/azure-ai-vision-imageanalysis-java.md)
+- **Transcription**: [Python](references/sdk/azure-ai-transcription-py.md)
+- **Translation**: [Python](references/sdk/azure-ai-translation-text-py.md) | [TypeScript](references/sdk/azure-ai-translation-ts.md)
+- **Document Intelligence**: [.NET](references/sdk/azure-ai-document-intelligence-dotnet.md) | [TypeScript](references/sdk/azure-ai-document-intelligence-ts.md)
+- **Content Safety**: [Python](references/sdk/azure-ai-contentsafety-py.md) | [TypeScript](references/sdk/azure-ai-contentsafety-ts.md) | [Java](references/sdk/azure-ai-contentsafety-java.md)
 
 ## Service Details
 
 For deep documentation on specific services:
 
 - AI Search indexing and queries -> [Azure AI Search documentation](https://learn.microsoft.com/azure/search/search-what-is-azure-search)
 - Speech transcription patterns -> [Azure AI Speech documentation](https://learn.microsoft.com/azure/ai-services/speech-service/overview)
-- Foundry agents and flows -> [Azure AI Foundry documentation](https://learn.microsoft.com/azure/ai-studio/what-is-ai-studio)

cli/src/internal/assets/ghcp4a-skills/azure-ai/references/sdk/azure-ai-contentsafety-java.md

@@ -0,0 +1,36 @@
+# Azure AI Content Safety — Java SDK Quick Reference
+
+> Condensed from **azure-ai-contentsafety-java**. Full patterns (blocklist management, image moderation, 8-severity)
+> in the **azure-ai-contentsafety-java** plugin skill if installed.
+
+## Install
+```xml
+<dependency>
+  <groupId>com.azure</groupId>
+  <artifactId>azure-ai-contentsafety</artifactId>
+  <version>1.1.0-beta.1</version>
+</dependency>
+```
+
+## Quick Start
+```java
+import com.azure.ai.contentsafety.ContentSafetyClient;
+import com.azure.ai.contentsafety.ContentSafetyClientBuilder;
+import com.azure.ai.contentsafety.BlocklistClient;
+import com.azure.ai.contentsafety.BlocklistClientBuilder;
+ContentSafetyClient client = new ContentSafetyClientBuilder()
+    .endpoint(endpoint).credential(credential).buildClient();
+```
+
+## Non-Obvious Patterns
+- Two separate builders: `ContentSafetyClientBuilder` and `BlocklistClientBuilder`
+- Image from file: `new ContentSafetyImageData().setContent(BinaryData.fromBytes(bytes))`
+- Image from URL: `new ContentSafetyImageData().setBlobUrl(url)`
+- Blocklist create uses raw `BinaryData` + `RequestOptions` (not typed model)
+
+## Best Practices
+1. Blocklist changes take ~5 minutes to take effect
+2. Only request needed categories to reduce latency
+3. Typically block severity >= 4 for strict moderation
+4. Process multiple items in parallel for throughput
+5. Cache blocklist results where appropriate