Skill UI

Guides teams through verifying prerequisites, reviewing examples, and adapting configuration to manage OpenRouter context windows efficiently, trimming tokens and avoiding limits while keeping production monitoring in place.

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.

Comprehensive skill covering the full lifecycle of visual AI systems. Expertise includes object detection (YOLO, Faster R-CNN), instance and semantic segmentation (SAM, Mask R-CNN), and video analysis. The workflow guides users through dataset preparation, selecting optimal architectures, training using frameworks like PyTorch, and achieving production readiness via model optimization (ONNX, TensorRT) and deployment on various edge or cloud targets.

SimPO (Simple Preference Optimization) is a state-of-the-art, reference-free method designed for aligning Large Language Models (LLMs) using human preference data. It is an efficient alternative to DPO and PPO, notably outperforming DPO without requiring a separate reference model. It is ideal for practitioners who need faster, simpler, and more resource-efficient fine-tuning for preference alignment.

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.

Guides Claude to automatically tune machine learning model hyperparameters via grid, random, or Bayesian search, generating runnable Python code, executing validations, and reporting best configurations plus performance metrics for accuracy or other goals.

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.

Provides a generative engine optimization framework to tune content for AI Overviews, ChatGPT, Perplexity, and similar experiences, covering citation scoring, structural readability, media signals, authority cues, crawler allowances, and licensing readiness.

Geo-first SEO analysis tool that audits websites for AI-powered search visibility, performs technical, content, schema, and platform optimization, and generates client-ready GEO reports with citability scoring and llms.txt guidance.

Analyzes draft prompts, identifies intent and missing pieces, matches ECC components, and outputs a ready-to-use optimized prompt while explicitly advising against taking implementation actions.

Use production trace data and the ax CLI to locate LLM prompts, collect annotations/evals, and iterate on prompt templates for data-driven improvements.

Guides optimizing content for AI Overviews, ChatGPT, Perplexity, and similar AI search systems by improving GEO visibility, llms.txt readiness, crawler access, and passage-level citability.