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 enables developers to build robust content moderation applications in Java using the Azure AI Content Safety services. It provides comprehensive capabilities to analyze both text and images for harmful content, detecting categories such as hate speech, violence, and sexual material. Furthermore, the SDK allows for custom blocklist management, enabling users to filter specific undesirable keywords or phrases to ensure strict content compliance and safety across various enterprise applications.

This SDK enables developers to build robust content moderation applications in Java using the Azure AI Content Safety services. It provides comprehensive capabilities to analyze both text and images for harmful content, detecting categories such as hate speech, violence, and sexual material. Furthermore, the SDK allows for custom blocklist management, enabling users to filter specific undesirable keywords or phrases to ensure strict content compliance and safety across various enterprise applications.

A comprehensive checklist for ensuring LangChain applications are robust, secure, and scalable for production environments. It covers critical areas including configuration management, advanced error handling, observability setup (e.g., LangSmith), performance optimization, security best practices (like prompt injection prevention and PII handling), rigorous testing, and zero-downtime deployment strategies. Use this guide when preparing for a major launch or auditing existing LLM systems.

This skill provides deep integration with Google NotebookLM, enabling users to query uploaded documentation using Gemini. It ensures source-grounded, citation-backed answers, significantly reducing hallucinations. Features include managing the notebook library (add, list, search), handling authentication, and implementing a structured follow-up mechanism for comprehensive research.

This skill provides comprehensive management capabilities for AI Integrations within the Arize platform. Users can create, list, retrieve, and update credentials for various Large Language Model (LLM) providers, including OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, and Vertex AI. These integrations are crucial for enabling Arize's advanced monitoring and evaluation features that require invoking external LLMs.

This skill is designed to manage complex, multi-step tasks by coordinating the efforts of multiple specialized AI agents. It provides structured methods for listing available agents, initiating real-time conversations, submitting complex background tasks (such as data analysis or report generation), tracking task status, and maintaining conversational context using session IDs. It is essential when a core task requires diverse expert knowledge or sequential collaboration.

A comprehensive readiness checklist for deploying clinical AI systems, such as Abridge, within regulated healthcare environments. It covers critical aspects including HIPAA compliance, Business Associate Agreements (BAA), EHR integration validation (FHIR R4), robust infrastructure security (TLS 1.3, secrets management), and provides structured scripts for pre-launch validation and post-launch rollback planning.

A comprehensive checklist for deploying Claude API integrations to a production environment. It covers essential best practices for reliability, observability, and cost control, including authentication procedures, advanced error handling, rate limit management, health check implementation, and infrastructure deployment strategies (e.g., canary releases) to ensure a smooth and stable launch.

This comprehensive checklist guides the safe and robust deployment of Cohere API v2 integrations from staging to production. It covers critical steps including API key management, code quality standards, performance tuning, implementing health checks and circuit breakers, and defining full rollback procedures, ensuring mission-critical AI features are reliable for launch.

This comprehensive checklist guides the operational setup (MLOps) for AI applications using Together AI's OpenAI-compatible API. It covers critical production concerns, including secure API key management, implementing robust rate limit handling, building error resilience features (circuit breakers, exponential backoff), and setting up comprehensive monitoring for latency, cost control, and model availability. Essential for reliable, scalable LLM deployment.

Build content moderation applications using the Azure AI Content Safety SDK for Java. Covers text and image analysis, harm categories, severity levels, and blocklist management.