Machine Learning Skills

| Build production Firebase Genkit applications including RAG systems, multi-step flows, and tool calling for Node.js/Python/Go. Deploy to Firebase Functions or Cloud Run with AI monitoring. Use when asked to "create genkit flow" or "implement RAG". Trigger with relevant phrases based on skill purpose.

| Gradient Clipping Helper - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Hyperparameter Tuner - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Inference Latency Profiler - Auto-activating skill for ML Deployment. Part of the ML Deployment skill category.

| Learning Rate Scheduler - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Mixed Precision Trainer - Auto-activating skill for ML Training. Part of the ML Training skill category.

| This skill trains machine learning models using automated workflows. It analyzes datasets, selects appropriate model types (classification, regression, etc.), configures training parameters, trains the model with cross-validation, generates performance metrics, and saves the trained model artifact. Use this skill when the user requests to "train" a model, needs to evaluate a dataset for machine learning purposes, or wants to optimize model performance. The skill supports common frameworks like scikit-learn.

| Mlflow Tracking Setup - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Model Checkpoint Manager - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Model Evaluation Metrics - Auto-activating skill for ML Training. Part of the ML Training skill category.

| This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".

| Model Explainability Tool - Auto-activating skill for ML Training. Part of the ML Training skill category.

| This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").

| This skill enables Claude to design NoSQL data models. It activates when the user requests assistance with NoSQL database design, including schema creation, data modeling for MongoDB or DynamoDB, or defining document structures. Use this skill when the user mentions "NoSQL data model", "design MongoDB schema", "create DynamoDB table", or similar phrases related to NoSQL database architecture. It assists in understanding NoSQL modeling principles like embedding vs. referencing, access pattern optimization, and sharding key selection.

| Optimize deep learning models using Adam, SGD, and learning rate scheduling to improve accuracy and reduce training time. Use when asked to "optimize deep learning model" or "improve model performance". Trigger with phrases like 'optimize', 'performance', or 'speed up'.

| Optuna Study Creator - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Pytorch Model Trainer - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Roc Curve Plotter - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting "run clustering", "cluster analysis", or "group data points". Trigger with relevant phrases based on skill purpose.

| Security Benchmark Runner - Auto-activating skill for Security Advanced. Part of the Security Advanced skill category.

| Sklearn Pipeline Builder - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Process split datasets into training, validation, and testing sets for ML model development. Use when requesting "split dataset", "train-test split", or "data partitioning". Trigger with relevant phrases based on skill purpose.

| Streaming Inference Setup - Auto-activating skill for ML Deployment. Part of the ML Deployment skill category.

| Tensorboard Visualizer - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Tensorflow Model Trainer - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Train Test Splitter - Auto-activating skill for ML Training. Part of the ML Training skill category.

| Build train machine learning models with automated workflows. Analyzes datasets, selects model types (classification, regression), configures parameters, trains with cross-validation, and saves model artifacts. Use when asked to "train model" or "evalua... Trigger with relevant phrases based on skill purpose.

| This skill automates the adaptation of pre-trained machine learning models using transfer learning techniques. It is triggered when the user requests assistance with fine-tuning a model, adapting a pre-trained model to a new dataset, or performing transfer learning. It analyzes the user's requirements, generates code for adapting the model, includes data validation and error handling, provides performance metrics, and saves artifacts with documentation. Use this skill when you need to leverage existing models for new tasks or datasets, optimizing for performance and efficiency.

| Triton Inference Config - Auto-activating skill for ML Deployment. Part of the ML Deployment skill category.

| Validate AI/ML models and datasets for bias, fairness, and ethical concerns. Use when auditing AI systems for ethical compliance, fairness assessment, or bias detection. Trigger with phrases like "evaluate model fairness", "check for bias", or "validate AI ethics".

Build and deploy production-ready generative AI agents using Vertex AI, Gemini models, and Google Cloud infrastructure with RAG, function calling, and multi-modal capabilities

| Wandb Experiment Logger - Auto-activating skill for ML Training. Part of the ML Training skill category.

Cloudflare Vectorize vector database for semantic search and RAG. Use for vector indexes, embeddings, similarity search, or encountering dimension mismatches, filter errors. nearest neighbor, knn search, ann search, RAG, retrieval augmented generation, chat with data, document search, semantic Q&A, context retrieval, bge-base, @cf/baai/bge-base-en-v1.5, text-embedding-3-small, text-embedding-3-large, Workers AI embeddings, openai embeddings, insert vectors, upsert vectors, query vectors, delete vectors, metadata filtering, namespace filtering, topK search, cosine similarity, euclidean distance, dot product, wrangler vectorize, metadata index, create vectorize index, vectorize dimensions, vectorize metric, vectorize binding

Cloudflare Workers AI for serverless GPU inference. Use for LLMs, text/image generation, embeddings, or encountering AI_ERROR, rate limits, token exceeded errors. ai inference, cloudflare llm, ai streaming, text generation ai, ai embeddings, image generation ai, workers ai rag, ai gateway, llama workers, flux image generation, stable diffusion workers, vision models ai, ai chat completion, AI_ERROR, rate limit ai, model not found, token limit exceeded, neurons exceeded, ai quota exceeded, streaming failed, model unavailable, workers ai hono, ai gateway workers, vercel ai sdk workers, openai compatible workers, workers ai vectorize

ElevenLabs Agents Platform for AI voice agents (React/JS/Native/Swift). Use for voice AI, RAG, tools, or encountering package deprecation, audio cutoff, CSP violations, webhook auth failures.

Google Gemini embeddings API (gemini-embedding-001) for RAG and semantic search. Use for vector search, Vectorize integration, or encountering dimension mismatches, rate limits, text truncation.

Google Gemini File Search for managed RAG with 100+ file formats. Use for document Q&A, knowledge bases, or encountering immutability errors, quota issues, polling failures. Supports Gemini 3 Pro/Flash (Gemini 2.5 legacy).

Type-safe Hono APIs with routing, middleware, RPC. Use for request validation, Zod/Valibot validators, or encountering middleware type inference, validation hook, RPC errors.

| Rates any SKILL.md on a 0-100 scale across 10 dimensions with a SHIP / REWORK / SCRAP verdict. Evaluates trigger precision, instruction clarity, output predictability, edge case coverage, anti-hallucination guardrails, developer experience, composability, open-source readiness, wow factor, and real-world utility. No flattery — calibrated against Anthropic's own skill-creator rubric. Use this skill whenever someone says "score this skill", "rate my skill", "is this skill good", "skill review", "skill audit", "roast my SKILL.md", "grade this", "will this skill work", "evaluate my skill", "how good is this", or pastes a SKILL.md and asks for feedback. Also trigger when comparing two skills, benchmarking a skill collection, or asking "what's wrong with this skill" — even without the word "score".

Analyze Apple Health export ZIP. Run local prepare to generate structured insights, then produce complementary health and training reports with long-term context.

为城市旅行明信片、城市拼贴海报生成可直接用于图像模型的完整提示词。用户提到城市明信片、旅行拼贴、城市视觉海报、生图提示词，或在视觉创作上下文中输入城市、城市+国家、节日、季节、活动时，应使用本 skill；即使用户只给出城市名，也要在上下文明确为视觉创作时使用。不用于旅游攻略、行程规划或普通城市事实问答。

Creates new skills, modifies and improves existing skills, and measures skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

Investigate AI observability clusters — understand usage patterns in AI/LLM traffic, compare cluster behavior, compute cost/latency metrics, and drill into individual traces within clusters.