Verify a CVlization training pipeline example is properly structured, can build, trains successfully, and logs appropriate metrics. Use when validating example implementations or debugging training issues.
Analyze ML model architecture from papers and code. Use when understanding model structure for implementation.
End-to-end ML system design for production. Use when designing ML pipelines, feature stores, model training infrastructure, or serving systems. Covers the complete lifecycle from data ingestion to model deployment and monitoring.
Identify and document model hyperparameters from papers. Use when setting up training configurations.
Verify a CVlization inference example is properly structured, builds successfully, and runs inference correctly. Use when validating inference example implementations or debugging inference issues.
Use when you have implemented an equivariant model and need to verify it correctly respects the intended symmetries. Invoke when user mentions testing model equivariance, debugging symmetry bugs, verifying implementation correctness, checking if model is actually equivariant, or diagnosing why equivariant model isn't working. Provides verification tests and debugging guidance.
Execute model training with optimization algorithms. Use when running training loops on datasets.
Loading and using pretrained models with Hugging Face Transformers. Use when working with pretrained models from the Hub, running inference with Pipeline API, fine-tuning models with Trainer, or handling text, vision, audio, and multimodal tasks.
Expert prompt optimization system for building production-ready AI features. Use when users request help improving prompts, want to create system prompts, need prompt review/critique, ask for prompt optimization strategies, want to analyze prompt effectiveness, mention prompt engineering best practices, request prompt templates, or need guidance on structuring AI instructions. Also use when users provide prompts and want suggestions for improvement.
World-class ML engineering skill for productionizing ML models, MLOps, and building scalable ML systems. Expertise in PyTorch, TensorFlow, model deployment, feature stores, model monitoring, and ML infrastructure. Includes LLM integration, fine-tuning, RAG systems, and agentic AI. Use when deploying ML models, building ML platforms, implementing MLOps, or integrating LLMs into production systems.
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
World-class computer vision skill for image/video processing, object detection, segmentation, and visual AI systems. Expertise in PyTorch, OpenCV, YOLO, SAM, diffusion models, and vision transformers. Includes 3D vision, video analysis, real-time processing, and production deployment. Use when building vision AI systems, implementing object detection, training custom vision models, or optimizing inference pipelines.
AI orchestration and multi-model collaboration MCP server. Use when you need multi-model AI collaboration, code reviews, debugging assistance, planning, consensus building, or advanced reasoning across multiple AI providers (Gemini, OpenAI, Grok, Azure, Ollama, etc.).
Labels a coordinate with a waypoint name for reasoning about spatial relationships. Use to mark important locations for navigation and planning
Enable or disable automatic training data capture (partial observation grid + optional ground truth).
Deep learning with PyTorch - model training, GPU acceleration, and data science workflows
Compare LLM models using the Artificial Analysis API. This skill should be used when the user asks to compare AI models, benchmark LLMs, evaluate model performance, compare pricing between models, or find the best model for a specific use case (coding, math, speed, cost). Triggers on requests like "compare GPT-5 and Claude", "which model is fastest", "cheapest model for coding", "benchmark comparison", "model performance analysis", or "artificial analysis". (user)
Multi-model ensemble consultation. Runs 3 models in parallel for diverse perspectives.
