Azure AI Projects for Java
Guides Java developers in managing Azure AI Foundry project resources.
The skill shows environment-variable configuration and DefaultAzureCredential or Managed Identity authentication, with a production identity recommendation. It does not clearly specify least privilege, user confirmation, sensitive-data non-disclosure, data flows, external-write risks, or rollback for deletion; examples also expose credential types and include create, update, and delete operations, so points are deducted.
Installation, authentication, client coverage, operations, and exception handling are documented, making the happy path plausible. However, environment-variable names differ between the main file and examples, metadata says 1.0.0 while the dependency is 1.0.0-beta.1, client/API coverage is inconsistent, and there are no skill-specific key-path tests or robust abnormal-input diagnostics; static scoring is therefore limited to 5.
The audience, Java SDK scope, and trigger phrases are relatively clear, covering connections, datasets, indexes, evaluations, and related project operations. Non-fit boundaries, input/output contracts, exclusion triggers, Chinese-language support, and endpoint/service reachability are not specified, so the score remains moderate.
The documentation is layered across installation, authentication, client hierarchy, operations, best practices, error handling, and a reference examples file. It includes MIT licensing, author/version metadata, and reference links, but lacks a changelog, explicit maintenance owner and update path, complete dependency guidance, and adequate limitation/troubleshooting disclosure; version inconsistency further reduces maintainability.
The snippets offer directly useful templates for Java client initialization, connections, deployments, datasets, and asynchronous calls. Yet several API, environment-variable, and client examples require manual verification, and there is no demonstrated end-to-end output, execution evidence, or comparison with alternatives; consequently the score stays below the static ceiling.
The skill lists Microsoft Learn, REST API, Azure SDK source, and sample links, while the repository material includes general CI, evaluation workflows, and harness tests. The supplied evidence does not demonstrate successful execution of this skill's key paths or provide skill-specific tests and independent corroboration, so only limited auditability is credited.
- Before use, recheck client names, method signatures, environment variables, and beta-version compatibility against the official Java SDK for the selected version.
- Do not log credentials or credential-related information; require explicit confirmation, scoped permissions, and recoverable backups for dataset or index creation, updates, and deletion.
- Azure endpoint, regional availability, and network reachability are not established in the supplied material and should be verified for the target environment.
What does this skill do, and when should you use it?
This skill from Microsoft’s Agent Skills repository targets Azure AI Foundry project management with the Java SDK. It guides coding agents through project configuration, authentication, and construction of specialized clients. Covered areas include connections, datasets, deployments, indexes, evaluations, evaluator configurations, and schedules. It fits Java developers building Foundry administration or evaluation workflows, with the caveat that the documented SDK version is beta.
Shows how to add com.azure:azure-ai-projects:1.0.0-beta.1 with Maven and configure AIProjectClientBuilder using PROJECT_ENDPOINT and an Azure token credential. It demonstrates building connection, dataset, deployment, index, and evaluation clients; listing connections and indexes; creating or updating an Azure AI Search index; and accessing evaluation APIs through the OpenAI client. It also covers pagination, environment-variable configuration, and handling not-found and HTTP response errors.
- A Java developer needs to enumerate Azure AI Foundry project connections and inspect their credential types.
- A Java application needs to upload documents and manage Foundry datasets.
- A developer needs to list, create, or update AI Search indexes from a Foundry project.
- A team needs to run AI model evaluations from a Java application.
- A production service needs to authenticate to a Foundry project with DefaultAzureCredential or managed identity.
What are this skill's strengths and limitations?
- Covers major Foundry project-management areas including connections, datasets, deployments, indexes, and evaluations.
- Provides copyable examples for Maven setup, authentication, client construction, and error handling.
- Includes guidance on pagination, environment variables, and production credential selection.
- The documented SDK dependency is `1.0.0-beta.1`, a beta release.
- Its scope is Azure AI Foundry project management and should not be confused with the capabilities of sibling skills in the monorepo.
- The supplied material does not show dedicated tests or test results for this individual skill.
How do you install this skill?
Install the collection into an agent environment with npx skills add microsoft/skills, then select azure-ai-projects-java in the installation wizard. The README does not specify the exact destination directory for this skill across every agent. Add the Maven dependency com.azure:azure-ai-projects:1.0.0-beta.1 to the Java project.
How do you use this skill?
Ask the agent using a documented trigger such as AIProjectClient java, azure ai projects java, Foundry project java, ConnectionsClient, DatasetsClient, or IndexesClient. Set PROJECT_ENDPOINT; when using DefaultAzureCredential in production, also set AZURE_TOKEN_CREDENTIALS=prod.