Skill UI

Automates guidance on identifying and responding to model drift within ML deployment workflows, offering best practices, production-ready code, and validation support so you can keep inference systems stable.

Automates guidance for evaluating machine learning models, covering best practices, sample code, and validation patterns triggered by mentions of model evaluation metrics.

Automated guidance for model explainability tooling within ML training workflows, giving step-by-step best practices, production-ready code, and validation for experiments whenever users mention explainability tool needs.

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.

Guides ML deployment workflows by surfacing best practices, templated code, and validation steps whenever model pruning helper patterns are mentioned.

Automates guidance for deploying machine learning models with quantization, covering tooling, best practices, and production readiness for inference, MLops, and monitoring tasks.

This skill provides production-ready patterns for integrating with the Perplexity Sonar API in both TypeScript and Python. It covers best practices for creating robust wrappers around the standard OpenAI client, including dedicated handling for citations, search results, and related questions. It also includes essential utilities like implementing exponential backoff for reliable retries. Use this when setting up complex search-augmented generation workflows or standardizing team coding practices around Perplexity usage.

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

This skill provides architectural patterns and implementation guides for developing advanced voice agents and automating phone calls using the Retell AI platform. It covers SDK integration, API best practices, and troubleshooting common telephony errors (like 401/429), enabling developers to build robust, real-world voice interaction systems.

This skill provides comprehensive patterns and best practices for integrating Retell AI into voice agents and automated phone call systems. It covers the SDK usage, common error handling (like 401 Unauthorized or 429 Rate Limited), and necessary implementation steps to ensure robust and reliable telephony automation solutions.

This skill provides implementation patterns for building robust and reliable AI voice agents and telephony automation systems using the Retell AI SDK. It covers best practices for handling common communication errors (like rate limiting or invalid API keys) and ensures stable interaction flow for voice-based applications and phone call automation scenarios.

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.