Skill UI

Specializes in the expert deployment and orchestration of production-grade multi-agent systems using Google's Agent Development Kit (ADK). This skill manages the complex lifecycle, including AgentCard discovery, A2A protocol communication, state management via Memory Bank, and secure execution within the Code Execution Sandbox. Ideal for setting up robust, enterprise-level AI workflows on the Vertex AI Agent Engine, especially during CI/CD pipelines.

This SDK provides Java developers with access to advanced image analysis features from Azure AI Vision. It allows applications to perform various visual tasks, including OCR text extraction, generating detailed image captions, detecting specific objects with bounding boxes, recognizing content tags, and performing smart cropping. Use it when building applications that require deep visual understanding of images.

This SDK provides a comprehensive Python client for Azure AI Vision 4.0, enabling advanced image understanding tasks. It supports generating descriptive captions, extracting content tags, detecting objects and people with bounding boxes, performing OCR (text recognition), and suggesting smart crop regions. It is ideal for computer vision applications and detailed image content analysis.

Empowers building AI agents that perceive screens, plan via vision-language reasoning, and control mouse/keyboard for desktop automation, with attention to sandboxing and safety across Anthropic, OpenAI, and open-source paradigms.

This skill guides the installation and configuration of the Juicebox PeopleGPT API. It enables developers to access AI-powered people search capabilities across millions of professional profiles. Use this when setting up the initial connection or performing deep people search functionality within your application.

This skill thoroughly inspects and validates Vertex AI Agent Engine deployments. It assesses critical components such as the Code Execution Sandbox, Memory Bank, A2A protocol compliance, security posture, and performance metrics. The tool generates weighted production-readiness scores (0-100%) and provides detailed, actionable recommendations to ensure the deployment is robust, secure, and ready for production use.

Generates minimal macOS Seatbelt sandbox profiles with allowlist-based resource rules so apps can be isolated by permissions, useful for restricting file, network, and IPC access when defending against high-risk execution or supply chain threats.

Guides using SHAP to explain ML predictions, compute feature importance, generate diagnostic plots, debug models, analyze bias, and compare models across tree, deep, linear, or black-box architectures.

AgentMail provides a comprehensive REST API email infrastructure specifically designed for AI agents. Agents can provision dedicated inboxes, send and receive emails, handle external service verifications (e.g., GitHub signups), and manage real-time inbound webhooks. The system includes a 'karma' mechanism to ensure high domain reputation and prevent abuse, making it reliable for complex automation workflows.

This infrastructure provides a robust solution for running powerful AI agents in remote, sandboxed environments. It overcomes the limitations of local execution by ensuring unlimited concurrency, consistent environments, and collaborative features. Use this to build complex background coding agents, multi-client interfaces (e.g., Slack, Web), or scale agent workloads far beyond local machine constraints.

Evolver is a comprehensive self-evolution engine designed for autonomous AI agents. It analyzes complex runtime history, identifies performance bottlenecks, and autonomously generates and applies improvements. Communication is architecturally confined to a local Proxy Mailbox, ensuring secure and reliable interaction with the EvoMap Hub for asset management and task acquisition. Ideal for developing highly adaptive, continuously improving intelligent systems.

This guide provides comprehensive, version-aware documentation for using PufferLib in reinforcement learning pipelines. It details best practices for adapting Gymnasium/PettingZoo environments, managing different PufferLib versions (3.0.0 vs 4.0 source), and executing safe, reproducible training and evaluation routines. Key focus areas include environment contract validation, sandboxing native builds, and maintaining data provenance.