firefrost-gaming/antigravity-skills-reference

Files

Ares 4a5f1234bb fix: harden registry tooling, make tests hermetic, and restore metadata consistency (#168 )

* chore: upgrade maintenance scripts to robust PyYAML parsing

- Replaces fragile regex frontmatter parsing with PyYAML/yaml library
- Ensures multi-line descriptions and complex characters are handled safely
- Normalizes quoting and field ordering across all maintenance scripts
- Updates validator to strictly enforce description quality

* fix: restore and refine truncated skill descriptions

- Recovered 223+ truncated descriptions from git history (6.5.0 regression)
- Refined long descriptions into concise, complete sentences (<200 chars)
- Added missing descriptions for brainstorming and orchestration skills
- Manually fixed imagen skill description
- Resolved dangling links in competitor-alternatives skill

* chore: sync generated registry files and document fixes

- Regenerated skills index with normalized forward-slash paths
- Updated README and CATALOG to reflect restored descriptions
- Documented restoration and script improvements in CHANGELOG.md

* fix: restore missing skill and align metadata for full 955 count

- Renamed SKILL.MD to SKILL.md in andruia-skill-smith to ensure indexing
- Fixed risk level and missing section in andruia-skill-smith
- Synchronized all registry files for final 955 skill count

* chore(scripts): add cross-platform runners and hermetic test orchestration

* fix(scripts): harden utf-8 output and clone target writeability

* fix(skills): add missing date metadata for strict validation

* chore(index): sync generated metadata dates

* fix(catalog): normalize skill paths to prevent CI drift

* chore: sync generated registry files

* fix: enforce LF line endings for generated registry files

2026-03-01 09:38:25 +01:00

3.6 KiB

Raw Blame History

name, description, risk, source, date_added

name	description	risk	source	date_added
azure-ai-agents-persistent-java	Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools.	unknown	community	2026-02-27

Azure AI Agents Persistent SDK for Java

Low-level SDK for creating and managing persistent AI agents with threads, messages, runs, and tools.

Installation

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-agents-persistent</artifactId>
    <version>1.0.0-beta.1</version>
</dependency>

Environment Variables

PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
MODEL_DEPLOYMENT_NAME=gpt-4o-mini

Authentication

import com.azure.ai.agents.persistent.PersistentAgentsClient;
import com.azure.ai.agents.persistent.PersistentAgentsClientBuilder;
import com.azure.identity.DefaultAzureCredentialBuilder;

String endpoint = System.getenv("PROJECT_ENDPOINT");
PersistentAgentsClient client = new PersistentAgentsClientBuilder()
    .endpoint(endpoint)
    .credential(new DefaultAzureCredentialBuilder().build())
    .buildClient();

Key Concepts

The Azure AI Agents Persistent SDK provides a low-level API for managing persistent agents that can be reused across sessions.

Client Hierarchy

Client	Purpose
`PersistentAgentsClient`	Sync client for agent operations
`PersistentAgentsAsyncClient`	Async client for agent operations

Core Workflow

1. Create Agent

// Create agent with tools
PersistentAgent agent = client.createAgent(
    modelDeploymentName,
    "Math Tutor",
    "You are a personal math tutor."
);

2. Create Thread

PersistentAgentThread thread = client.createThread();

3. Add Message

client.createMessage(
    thread.getId(),
    MessageRole.USER,
    "I need help with equations."
);

4. Run Agent

ThreadRun run = client.createRun(thread.getId(), agent.getId());

// Poll for completion
while (run.getStatus() == RunStatus.QUEUED || run.getStatus() == RunStatus.IN_PROGRESS) {
    Thread.sleep(500);
    run = client.getRun(thread.getId(), run.getId());
}

5. Get Response

PagedIterable<PersistentThreadMessage> messages = client.listMessages(thread.getId());
for (PersistentThreadMessage message : messages) {
    System.out.println(message.getRole() + ": " + message.getContent());
}

6. Cleanup

client.deleteThread(thread.getId());
client.deleteAgent(agent.getId());

Best Practices

Use DefaultAzureCredential for production authentication
Poll with appropriate delays — 500ms recommended between status checks
Clean up resources — Delete threads and agents when done
Handle all run statuses — Check for RequiresAction, Failed, Cancelled
Use async client for better throughput in high-concurrency scenarios

Error Handling

import com.azure.core.exception.HttpResponseException;

try {
    PersistentAgent agent = client.createAgent(modelName, name, instructions);
} catch (HttpResponseException e) {
    System.err.println("Error: " + e.getResponse().getStatusCode() + " - " + e.getMessage());
}

Reference Links

Resource	URL
Maven Package	https://central.sonatype.com/artifact/com.azure/azure-ai-agents-persistent
GitHub Source	https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-agents-persistent

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

3.6 KiB Raw Blame History