Skill UI

Automates creation of A/B test configurations for ML deployment, guiding users through best practices, code samples, and validation steps so production models are served with monitoring and rollout control.

Generates production-ready Claude-powered agents with Google’s ADK, bundling React-style loops, tool-augmented capabilities, multi-agent orchestration, workflow automation, and test scaffolding.

Deploys and orchestrates Vertex AI ADK agents via the A2A protocol, covering AgentCard discovery, sandboxed code execution, memory banks, orchestration patterns, and Vertex AI Agent Engine rollout for CI/CD-style automation.

A comprehensive toolkit for designing, architecting, and evaluating complex multi-agent systems. It provides structured patterns—including Supervisor, Swarm, and Pipeline—for system architecture, defines agent roles (Coordinator, Specialist), and covers best practices for communication (Event Sourcing, Message Passing), tool design, and safety guardrails, ensuring robust and scalable AI workflows.

This framework addresses the gap between academic benchmarks and real-world production performance for LLM agents. It teaches advanced quality engineering practices, such as behavioral contract testing, adversarial testing, and statistical analysis, ensuring agents maintain reliability and robustness when deployed. It helps developers move beyond simple pass/fail rates to comprehensive capability assessment.

Build persistent agents on Azure AI Foundry through the Microsoft Agent Framework Python SDK, hooking into AzureAIAgentsProvider for tools, hosted services, streaming, and threaded conversations.

This skill guides the design and implementation of robust memory architectures for advanced AI agents. It moves beyond simple data storage, focusing on the critical art of memory retrieval. Learn strategies for chunking, selecting appropriate vector stores, and integrating various memory types (episodic, semantic, working) to ensure agents maintain long-term consistency and intelligence across millions of interactions. Ideal for building sophisticated, stateful autonomous agents.

This skill provides expertise in designing the critical interface between Large Language Models (LLMs) and external tools. It focuses on tool schema design, function calling best practices, and robust error handling to prevent common failures like hallucination or silent loops. It ensures that the tool's documentation and schema are crystal clear, allowing the LLM to use complex external APIs reliably and predictably.

Guide for automating Agentql tasks through Composio’s Rube MCP toolkit, covering prerequisites, setup, discovery, execution, and error avoidance so you can safely run workflows using the latest schemas.

Designs and implements robust, autonomous AI agent systems that maintain strict control and graceful degradation. This skill covers multi-agent orchestration, advanced planning (like ReAct and Plan-and-Execute), tool management, and robust error handling. It ensures that complex AI workflows can proceed independently while providing clear oversight and failure modes.

Designs and implements robust, autonomous AI agent systems that maintain strict control and graceful degradation. This skill covers multi-agent orchestration, advanced planning (like ReAct and Plan-and-Execute), tool management, and robust error handling. It ensures that complex AI workflows can proceed independently while providing clear oversight and failure modes.

Deliver production-ready LLM applications, RAG systems, and multi-agent orchestration with vector databases, embedding strategies, safety controls, and observability for enterprise deployments.