Skill UI

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

Automates TensorFlow SavedModel deployment support with step-by-step guidance, best-practice patterns, production-ready code/config generation, and validation for serving, monitoring, and optimization scenarios.

Assists with torchscript exporter tasks for ML deployment, offering automated guidance, code/config generation, and validation against industry standards whenever torchscript exporters are mentioned.

Automates guidance on train/test split workflows inside ML training, surfacing best-practice patterns, code snippets, and validation tips for PyTorch, TensorFlow, and sklearn projects.

Automates Vertex AI deployment tasks, offering step-by-step guidance, production-ready code, and validation for model serving, MLOps pipelines, monitoring, and optimization when you mention Vertex AI Deployer.

Automates Vertex AI endpoint configuration guidance, follows GCP best practices, and helps generate validated production-ready code and setup steps whenever you ask about endpoint config patterns.

Automates wandb experiment logger tasks within ML training, providing guidance, best practices, and code or configuration examples plus validation to keep data prep, training, hyperparameter tuning, and experiment tracking consistent.

This skill bridges idea discovery and auto review workflows by reading experiment plans, implementing code, deploying to GPU, and collecting results. It supports code review before deployment, parallel execution, and sanity checks. Use it when you have a ready experiment plan or need to execute defined experiments automatically.

PyTorch Lightning is a deep learning framework that organizes PyTorch code to eliminate boilerplate while maintaining flexibility. It automates training workflows, supports multi-GPU/TPU orchestration, and implements best practices for neural network training. Features include LightningModules, Trainers, data pipelines, callbacks, logging (W&B, TensorBoard), and distributed training strategies like DDP and FSDP for scalable training.

Provides comprehensive guidelines for authors preparing supplementary materials for conference submissions (AAMAS). It covers structuring full proofs, extended game definitions, extra experiments, and anonymized code assets, ensuring the main paper remains comprehensible while maximizing the impact of supporting evidence.

This comprehensive guide outlines best practices for packaging and releasing scientific artifacts—including code, training configurations, demonstration data, and policy checkpoints—for robotics research papers. It details how to prepare materials for anonymous reviewer assessment and for durable public release, ensuring the work is fully reproducible by the academic community.

A comprehensive guide detailing the submission rules and best practices for supplementary materials, including videos, appendices, code, and data, for top-tier robotics conferences. It outlines distinct submission channels, strict technical requirements (e.g., 250MB video cap, anonymity), and the necessary transition steps from the review phase to the final camera-ready publication format.