Skill UI

This skill guides users to configure and orchestrate parallel AI agents using the standalone Antigravity 2.0 Agent Manager alongside the Antigravity IDE. It covers installation, dual-workspace setup, and coordinating agent execution for front-end and back-end projects. Best practices include enforcing folder-level boundaries to prevent conflicts and using Git for synchronization.

A meta-skill that evaluates task complexity and dynamically selects appropriate specialized skills for AI agents. It prevents overuse of tools on simple tasks, tracks successful skill combinations via memory, and coordinates execution across multiple domains to solve complex problems efficiently.

Build container-based hosted agents with Azure AI Projects SDK (HostedAgentDefinition). Use when creating hosted agents with custom container images in Azure AI Foundry.

Design patterns for building autonomous coding agents, covering tool and function calling APIs, permission and approval systems, browser automation, and human-in-the-loop workflows inspired by Cline and OpenAI Codex.

Autonomous agents are AI systems capable of independently decomposing goals, planning actions, executing tools, and self-correcting without constant human guidance. This skill focuses on ensuring reliability in agent loops like ReAct and Plan-Execute, emphasizing goal decomposition and reflection patterns. It highlights that compounding error rates can kill autonomy, advocating for constrained, domain-specific agents over general autonomy. Build for reliability first.

Comprehensive expert for deploying and managing AI agents at scale using AWS Bedrock AgentCore. Covers all nine core services including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, and Evaluations. Use for agent deployment, MCP integration, credential management, and cross-service workflows with AWS CLI.

Configure AWS MCP servers for documentation search and AWS API access with AI agents. Covers checking existing config, choosing between the full server with credentials or the no-auth docs-only server, and verifying tools.

This low-level SDK enables .NET developers to create and manage persistent AI agents on Azure. It supports threads, messages, runs, and tools integration. Includes features for function calling, vector store management, and file search. Ideal for building complex AI applications with C# and Azure services.

Low-level Java SDK for creating and managing persistent Azure AI agents with threads, messages, runs, tools, authentication, polling, and resource cleanup.

High-level .NET SDK for Azure AI Foundry projects, enabling agents, connections, datasets, deployments, evaluations, and indexes workflows with authentication and cleanup guidance.

Build AI applications on Microsoft Foundry with the azure-ai-projects Python SDK: project connections, model deployments, versioned agents, and cloud batch evaluation workflows with safe, scoped operations.

A high-level TypeScript SDK for managing Azure AI Foundry projects, enabling creation of AI agents, model deployments, datasets, and evaluations. Supports various tools like code interpreter, search, and MCP for building intelligent applications.