.github/plugins/azure-sdk-java/skills/azure-ai-projects-java/SKILL.md
Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations. Triggers: "AIProjectClient java", "azure ai projects java", "Foundry project java", "ConnectionsClient", "DatasetsClient", "IndexesClient".
npx skillsauth add microsoft/skills azure-ai-projects-javaInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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High-level SDK for Azure AI Foundry project management with access to connections, datasets, indexes, and evaluations.
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-projects</artifactId>
<version>1.0.0-beta.1</version>
</dependency>
PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
import com.azure.ai.projects.AIProjectClientBuilder;
import com.azure.identity.DefaultAzureCredentialBuilder;
AIProjectClientBuilder builder = new AIProjectClientBuilder()
.endpoint(System.getenv("PROJECT_ENDPOINT"))
.credential(new DefaultAzureCredentialBuilder().build());
The SDK provides multiple sub-clients for different operations:
| Client | Purpose |
|--------|---------|
| ConnectionsClient | Enumerate connected Azure resources |
| DatasetsClient | Upload documents and manage datasets |
| DeploymentsClient | Enumerate AI model deployments |
| IndexesClient | Create and manage search indexes |
| EvaluationsClient | Run AI model evaluations |
| EvaluatorsClient | Manage evaluator configurations |
| SchedulesClient | Manage scheduled operations |
// Build sub-clients from builder
ConnectionsClient connectionsClient = builder.buildConnectionsClient();
DatasetsClient datasetsClient = builder.buildDatasetsClient();
DeploymentsClient deploymentsClient = builder.buildDeploymentsClient();
IndexesClient indexesClient = builder.buildIndexesClient();
EvaluationsClient evaluationsClient = builder.buildEvaluationsClient();
import com.azure.ai.projects.models.Connection;
import com.azure.core.http.rest.PagedIterable;
PagedIterable<Connection> connections = connectionsClient.listConnections();
for (Connection connection : connections) {
System.out.println("Name: " + connection.getName());
System.out.println("Type: " + connection.getType());
System.out.println("Credential Type: " + connection.getCredentials().getType());
}
indexesClient.listLatest().forEach(index -> {
System.out.println("Index name: " + index.getName());
System.out.println("Version: " + index.getVersion());
System.out.println("Description: " + index.getDescription());
});
import com.azure.ai.projects.models.AzureAISearchIndex;
import com.azure.ai.projects.models.Index;
String indexName = "my-index";
String indexVersion = "1.0";
String searchConnectionName = System.getenv("AI_SEARCH_CONNECTION_NAME");
String searchIndexName = System.getenv("AI_SEARCH_INDEX_NAME");
Index index = indexesClient.createOrUpdate(
indexName,
indexVersion,
new AzureAISearchIndex()
.setConnectionName(searchConnectionName)
.setIndexName(searchIndexName)
);
System.out.println("Created index: " + index.getName());
The SDK exposes OpenAI's official SDK for evaluations:
import com.openai.services.EvalService;
EvalService evalService = evaluationsClient.getOpenAIClient();
// Use OpenAI evaluation APIs directly
PagedIterableimport com.azure.core.exception.HttpResponseException;
import com.azure.core.exception.ResourceNotFoundException;
try {
Index index = indexesClient.get(indexName, version);
} catch (ResourceNotFoundException e) {
System.err.println("Index not found: " + indexName);
} catch (HttpResponseException e) {
System.err.println("Error: " + e.getResponse().getStatusCode());
}
| Resource | URL | |----------|-----| | Product Docs | https://learn.microsoft.com/azure/ai-studio/ | | API Reference | https://learn.microsoft.com/rest/api/aifoundry/aiprojects/ | | GitHub Source | https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-projects | | Samples | https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-projects/src/samples |
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development
Deploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt agent create, container start, batch eval, prompt optimization, prompt optimizer workflows, agent.yaml, dataset curation from traces. USE FOR: deploy agent to Foundry, hosted agent, create agent, invoke agent, evaluate agent, run batch eval, optimize prompt, improve prompt, prompt optimization, prompt optimizer, improve agent instructions, optimize agent instructions, optimize system prompt, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, create dataset from traces, dataset versioning, eval trending, create AI Services, Cognitive Services, create Foundry resource, provision resource, knowledge index, agent monitoring, customize deployment, onboard, availability. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).
testing
Pre-deployment validation for Azure readiness. Run deep checks on configuration, infrastructure (Bicep or Terraform), RBAC role assignments, managed identity permissions, and prerequisites before deploying. WHEN: validate my app, check deployment readiness, run preflight checks, verify configuration, check if ready to deploy, validate azure.yaml, validate Bicep, test before deploying, troubleshoot deployment errors, validate Azure Functions, validate function app, validate serverless deployment, verify RBAC roles, check role assignments, review managed identity permissions, what-if analysis, validate Container Apps deployment.
testing
Check/manage Azure quotas and usage across providers. For deployment planning, capacity validation, region selection. WHEN: "check quotas", "service limits", "current usage", "request quota increase", "quota exceeded", "validate capacity", "regional availability", "provisioning limits", "vCPU limit", "how many vCPUs available in my subscription".