Skill UI

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.

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.

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

This guide provides comprehensive steps to install and authenticate the OpenEvidence SDK/API. It covers setting up environment variables, installing required packages (supporting both Node.js and Python), and verifying the connection using sample code. This is essential for developers integrating OpenEvidence's medical AI capabilities for clinical decision support and evidence-based queries into a new application.

Optimizes deep learning models by analyzing architecture, data, and metrics to select optimizers, learning rate schedules, and regularization strategies, then outputs optimized code and evaluates accuracy, time, and resource usage.

Outlines guarantees valid JSON/XML/code outputs by applying CFG/FSM-constrained token filtering, Pydantic schemas, and grammar-level sampling while supporting local Transformers, llama.cpp, and vLLM backends for fast inference.

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.

PyTorch Lightning is a high-level framework built on PyTorch that significantly reduces boilerplate code in deep learning training loops. It automatically handles device management, distributed training (DDP, FSDP, DeepSpeed), mixed precision, and advanced callbacks. It allows researchers to scale models from local machines to supercomputers with minimal code changes, enabling clean, scalable, and best-practice deep learning implementations.

This skill provides implementation patterns and code examples for creating an incident runbook using Retell AI. It is specifically designed for automating complex workflows, handling emergency notifications, and managing interactive phone calls within a voice agent or telephony system environment.

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.