Skill UI

Automates guidance for model export helper tasks across ML deployment, delivering step-by-step best practices, production-ready code/configuration, and validation against common standards.

Automates onnx converter work for ML deployment, providing best-practice guidance on serving, pipelines, monitoring, and optimization while generating production-ready code and configs.

Prediction Monitor Assistant automates machine learning deployment monitoring tasks, offering step-by-step guidance, best-practice patterns, production-ready code and configuration generation, and validation to keep serving and inference workflows reliable.

Automates PyTorch training workflows by guiding data preparation, model configuration, hyperparameter tuning, and experiment tracking while following industry best practices for production readiness.

This workflow guides users through building advanced AI voice agents using the Retell AI SDK. It demonstrates how to implement complex call flows, integrate function calling for tasks like booking appointments, and configure voice behavior for professional telephony applications.

This comprehensive guide demonstrates how to build and test a voice agent using the Retell AI SDK in TypeScript. It walks through configuring the underlying LLM with a custom persona, creating the voice agent instance, and finally initiating both outbound phone calls and web calls through the API. This is essential for integrating AI-powered voice communication into customer service platforms, call centers, or telephony automation systems.

This resource provides production-ready, structured patterns for building robust voice agent applications using the Retell AI SDK. It covers best practices such as implementing singleton client management, defining typed agent configurations with integrated LLMs, adding automatic retry logic for call failures, and managing large-scale outbound campaigns. Use this when building stable, scalable, and reliable voice automation systems.

Auto-activating assistant that guides AWS Sagemaker endpoint deployment, from best practices to production-ready code, configurations, and validation for inference serving and monitoring workflows.

A comprehensive, model-agnostic toolkit for advanced LLM development. It enables iterative prompt optimization through static analysis (token usage, clarity), measures critical RAG metrics (relevance, faithfulness, coverage), and validates complex agent workflows and configurations. Essential for rigorous LLM quality assurance and cost management.

Automates the setup of ML experiment tracking with MLflow or W&B, configures the environment, initializes servers, and supplies snippets for logging parameters, metrics, and artifacts so runs stay reproducible and easy to compare.

Automates SNS topic configuration tasks within AWS, offering guidance, best practices, code generation, and validation for compute, storage, networking, and serverless scenarios.

This guide details how to set up the Speak language learning platform integration. It covers installing necessary SDKs (Speak SDK, OpenAI), configuring API keys, and establishing the connection required for sophisticated AI-powered tutoring. Use this when building applications that require real-time speech recognition, pronunciation feedback, and multilingual language support.