Agent skill · software engineering · aiskillstore
azure-ai-projects-dotnet
Azure AI Projects SDK for .NET. High-level client for Azure AI Foundry projects including agents, connections, datasets, deployments, evaluations, and indexes.
Why this skill is useful
Provides specific SDK commands and workflows for managing Azure AI Foundry projects that the AI wouldn't reliably generate on its own.
What it needs
Requires dotnet installed locally. Requires azure account access. About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Azure.AI.Projects (.NET) High-level SDK for Azure AI Foundry project operations including agents, connections, datasets, deployments, evaluations, and indexes. Installation Current Versions: GA v1.1.0, Preview v1.2.0-beta.5 Environment Variables Authentication Client Hierarchy Core Workflows 1. Get Persistent Agents Client 2. Versioned Agents with Tools (Preview) 3. Connections 4. Deployments 5. Datasets 6. Indexes 7. Evaluations 8. Get Azure OpenAI Chat Client Available Agent Tools Tool Class Purpose ------ ------- --------- Code Interpreter CodeInterpreterToolDefinition Execute Python code File Search FileSearchToolDefinition Search uploaded files Function Calling FunctionToolDefinition Call custom functions Bing Grounding BingGroundingToolDefinition Web search via Bing Azure AI Search AzureAISearchToolDefinition Search Azure AI indexes OpenAPI OpenApiToolDefinition Call external APIs Azure Functions AzureFunctionToolDefinition Invoke Azure Functions MCP MCPToolDefinition Model Context Protocol tools Key Types Reference Type Purpose ------ --------- AIProjectClient Main entry point PersistentAgentsClient Low-level agent operations PromptAgentDefinition Versioned agent definition AgentVersion Versioned agent instance AIProjectConnection Connection to Azure resource AIProjectDeployment Model deployment info AIProjectDataset Dataset metadata AIProjectIndex Search index metadata Evaluation Evaluation configuration and results Best Practices 1. Use DefaultAzureCredential for production authentication 2. Use async methods (Async) for all I/O operations 3. Poll with appropriate delays (500ms recommended) when waiting for runs 4. Clean up resources — delete threads, agents, and files when done 5. Use versioned agents (via Azure.AI.Projects.OpenAI) for production scenarios 6. Store connection IDs rather than names for tool configurations 7. Use includeCredentials: true only when credentials are needed 8. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/azure-ai-projects-dotnet