Machine Learning Claude Skills
1 780 machine learning skills from 282 authors. They train and fine-tune models, build embeddings, run RAG and measure quality. Half of them fit into 2 252 tokens or less — that is what one costs your context window when the agent loads it. 424 ship runnable scripts rather than instructions alone. 10 of them cannot work without an MCP server, most often rube. We also found 397 copies of these same skills sitting in other people's repositories — counted once here, not 397 times.
1 780 unique 282 authors 5 updated this month 189 from vendors
| Deploy KServe InferenceService on CoreWeave with autoscaling and GPU scheduling. Use when serving ML models with KServe, configuring scale-to-zero, or deploying production inference endpoints on CoreWeave. Trigger with phrases like "coreweave inference service", "coreweave kserve", "coreweave model serving", "deploy model on coreweave".
| Run distributed GPU training jobs on CoreWeave with multi-node PyTorch. Use when training models across multiple GPUs, setting up distributed training, or running fine-tuning jobs on CoreWeave H100 clusters. Trigger with phrases like "coreweave training", "coreweave multi-gpu", "distributed training coreweave", "fine-tune on coreweave".
| Handle training data and model artifacts on CoreWeave persistent storage. Use when managing large datasets, configuring storage classes, or implementing data pipelines for GPU workloads. Trigger with phrases like "coreweave data", "coreweave storage", "coreweave pvc", "coreweave dataset management".
| Incident response runbook for CoreWeave GPU workload failures. Use when inference services are down, GPUs are unavailable, or responding to production incidents on CoreWeave. Trigger with phrases like "coreweave incident", "coreweave outage", "coreweave runbook", "coreweave service down".
| Optimize CoreWeave GPU inference latency and throughput. Use when reducing inference latency, maximizing GPU utilization, or tuning batch sizes and concurrency. Trigger with phrases like "coreweave performance", "coreweave latency", "coreweave throughput", "optimize coreweave inference".
| Production readiness checklist for CoreWeave GPU workloads. Use when launching inference services, preparing GPU training for production, or validating deployment configurations. Trigger with phrases like "coreweave production", "coreweave go-live", "coreweave checklist", "coreweave launch".
| Handle CoreWeave API and GPU quota limits. Use when hitting quota limits, managing GPU resource allocation, or implementing request queuing for inference endpoints. Trigger with phrases like "coreweave quota", "coreweave limits", "coreweave gpu allocation", "coreweave throttle".
| Upgrade CoreWeave deployments and migrate between GPU types. Use when migrating from A100 to H100, upgrading CUDA versions, or updating inference server versions. Trigger with phrases like "upgrade coreweave", "coreweave gpu migration", "coreweave cuda upgrade", "migrate coreweave".
| Monitor CoreWeave cluster events and GPU workload status. Use when tracking pod lifecycle events, monitoring GPU utilization, or alerting on inference service health changes. Trigger with phrases like "coreweave events", "coreweave monitoring", "coreweave pod alerts", "coreweave gpu monitoring".
| Cross Validation Setup - Auto-activating skill for ML Training. Part of the ML Training skill category.
| Data Augmentation Pipeline - Auto-activating skill for ML Training. Part of the ML Training skill category.
| Data Normalization Tool - Auto-activating skill for ML Training. Part of the ML Training skill category.
| This skill empowers Claude to preprocess and clean data using automated pipelines. It is designed to streamline data preparation for machine learning tasks, implementing best practices for data validation, transformation, and error handling. Claude should use this skill when the user requests data preprocessing, data cleaning, ETL tasks, or mentions the need for automated pipelines for data preparation. Trigger terms include "preprocess data", "clean data", "ETL pipeline", "data transformation", and "data validation". The skill ensures data quality and prepares it for effective analysis and model training.
| Use when building ML pipelines, training models, or deploying to production. Trigger with phrases like "databricks ML", "mlflow training", "databricks model", "feature store", "model registry".
| Dataset Loader Creator - Auto-activating skill for ML Training. Part of the ML Training skill category.
| This skill enables Claude to split datasets into training, validation, and testing sets. It is useful when preparing data for machine learning model development. Use this skill when the user requests to split a dataset, create train-test splits, or needs data partitioning for model training. The skill is triggered by terms like "split dataset," "train-test split," "validation set," or "data partitioning."
| This skill optimizes deep learning models using various techniques. It is triggered when the user requests improvements to model performance, such as increasing accuracy, reducing training time, or minimizing resource consumption. The skill leverages advanced optimization algorithms like Adam, SGD, and learning rate scheduling. It analyzes the existing model architecture, training data, and performance metrics to identify areas for enhancement. The skill then automatically applies appropriate optimization strategies and generates optimized code. Use this skill when the user mentions "optimize deep learning model", "improve model accuracy", "reduce training time", or "optimize learning rate".
| Process identify anomalies and outliers in datasets using machine learning algorithms. Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior. Trigger with phrases like "detect anomalies", "find outliers", or "identify unusual patterns".
| Distributed Training Setup - Auto-activating skill for ML Training. Part of the ML Training skill category.
| Early Stopping Callback - Auto-activating skill for ML Training. Part of the ML Training skill category.
| Use when designing Exa integrations, choosing between simple search and full RAG, or planning architecture for different traffic volumes. Trigger with phrases like "exa architecture", "exa blueprint", "how to structure exa", "exa RAG design", "exa at scale".
| Execute Exa neural search with contents, date filters, and domain scoping. Use when building search features, implementing RAG context retrieval, or querying the web with semantic understanding. Trigger with phrases like "exa search", "exa neural search", "search with exa", "exa searchAndContents", "exa query".
| Implement Exa search result processing, content extraction, caching, and RAG context management. Use when handling search results, implementing caching, building citation pipelines, or managing content payloads for LLM context windows. Trigger with phrases like "exa data", "exa results processing", "exa cache", "exa RAG context", "exa content extraction".
| Migrate from other search APIs (Google, Bing, Tavily, Serper) to Exa neural search. Use when switching to Exa from another search provider, migrating search pipelines, or evaluating Exa as a replacement for traditional search APIs. Trigger with phrases like "migrate to exa", "switch to exa", "replace google search with exa", "exa vs tavily", "exa migration", "move to exa".
| Implement Exa reference architecture for search pipelines, RAG, and content discovery. Use when designing new Exa integrations, reviewing project structure, or establishing architecture standards for neural search applications. Trigger with phrases like "exa architecture", "exa project structure", "exa RAG pipeline", "exa reference design", "exa search pipeline".
| Feature Engineering Helper - Auto-activating skill for ML Training. Part of the ML Training skill category.
| Feature Importance Analyzer - Auto-activating skill for ML Training. Part of the ML Training skill category.
| 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.