Machine Learning Skills

| Automatic activation for ALL Google Cloud Agent Development Kit (ADK) and Agent Starter Pack operations - multi-agent systems, containerized deployment, RAG agents, and production orchestration.

| This skill enables Claude to validate the ethical implications and fairness of AI/ML models and datasets. It is triggered when the user requests an ethics review, fairness assessment, or bias detection for an AI system. The skill uses the ai-ethics-validator plugin to analyze models, datasets, and code for potential biases and ethical concerns. It provides reports and recommendations for mitigating identified issues, ensuring responsible AI development and deployment. Use this skill when the user mentions "ethics validation", "fairness assessment", "bias detection", "responsible AI", or related terms in the context of AI/ML.

| This skill empowers Claude to identify anomalies and outliers within datasets. It leverages the anomaly-detection-system plugin to analyze data, apply appropriate machine learning algorithms, and highlight unusual data points. Use this skill when the user requests anomaly detection, outlier analysis, or identification of unusual patterns in data. Trigger this skill when the user mentions "anomaly detection," "outlier analysis," "unusual data," or requests insights into data irregularities.

| Manage Apify datasets, key-value stores, and request queues programmatically. Use when reading/writing datasets, exporting data, managing Actor storage, or orchestrating multi-Actor pipelines. "export apify data", "apify pipeline", "apify request queue".

| Batch Inference Pipeline - Auto-activating skill for ML Deployment. Part of the ML Deployment skill category.

| Benchmark Suite Creator - Auto-activating skill for Performance Testing. Part of the Performance Testing skill category.

| Build and evaluate classification models for supervised learning tasks with labeled data. Use when requesting "build a classifier", "create classification model", or "train classifier". Trigger with relevant phrases based on skill purpose.

| Execute this skill allows AI assistant 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... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

| Build different types of Claude-powered applications — chatbots, RAG systems, Use when working with architecture-variants patterns. agents, content pipelines, and code generation tools. Trigger with "claude architecture", "anthropic rag", "build with claude", "claude agent pattern", "anthropic app design".

| Redirect to claude-model-inference for Messages API streaming, vision, and structured output patterns. Use when looking for the primary Anthropic workflow. Trigger with "anthropic workflow", "claude main workflow".

| Redirect to claude-embeddings-search for tool use (function calling) and agentic loop patterns with Claude. Use when looking for the secondary Anthropic workflow. Trigger with "anthropic tools", "claude function calling".

| Implement tool use (function calling) with Claude to let it execute actions, Use when working with embeddings-search patterns. query databases, call APIs, and interact with external systems. Trigger with "anthropic tool use", "claude function calling", "claude tools", "anthropic structured output with tools".

| Stream Claude responses, use system prompts, handle multi-turn conversations, Use when working with model-inference patterns. and process structured output with the Messages API. Trigger with "anthropic streaming", "claude messages api", "claude inference", "stream claude response".

| This skill enables Claude to construct and evaluate classification models using provided datasets or specifications. It leverages the classification-model-builder plugin to automate model creation, optimization, and reporting. Use this skill when the user requests to "build a classifier", "create a classification model", "train a classification model", or needs help with supervised learning tasks involving labeled data. The skill ensures best practices are followed, including data validation, error handling, and performance metric reporting.

| This skill enables Claude to execute clustering algorithms on datasets. It is used when the user requests to perform clustering, identify groups within data, or analyze data structure. The skill supports algorithms like K-means, DBSCAN, and hierarchical clustering. Claude should use this skill when the user explicitly asks to "run clustering," "perform a cluster analysis," or "group data points" and provides a dataset or a way to access one. The skill also handles data validation, error handling, performance metrics, and artifact saving.

| Use when fine-tuning review quality, training CodeRabbit with team preferences, adding code guidelines, or reducing false positives. Trigger with phrases like "coderabbit tune reviews", "coderabbit learnings", "coderabbit guidelines", "reduce coderabbit noise", "coderabbit false positives".

| Build a complete RAG pipeline with Cohere Chat, Embed, and Rerank. Use when implementing retrieval-augmented generation, building grounded Q&A systems, or combining search with LLM generation. Trigger with phrases like "cohere RAG", "cohere retrieval", "cohere grounded generation", "cohere search and answer".

| Migrate from OpenAI/Anthropic/other LLM providers to Cohere, or vice versa. Use when switching LLM providers, migrating embeddings between models, or re-platforming existing AI integrations to Cohere API v2. Trigger with phrases like "migrate to cohere", "switch from openai to cohere", "cohere migration", "replace openai with cohere", "cohere replatform".

| Implement Cohere reference architecture with layered project layout for RAG and agents. Use when designing new Cohere integrations, reviewing project structure, or establishing architecture standards for Cohere API v2 applications. Trigger with phrases like "cohere architecture", "cohere project structure", "cohere layout", "organize cohere app", "cohere design pattern".

| Implement Cohere streaming event handling, SSE patterns, and connector webhooks. Use when building streaming UIs, handling chat/tool events, or registering Cohere connectors for RAG. Trigger with phrases like "cohere streaming", "cohere events", "cohere SSE", "cohere connectors", "cohere webhook".

| Confusion Matrix Generator - Auto-activating skill for ML Training. Part of the ML Training skill category.

| 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.