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This skill enables Claude to analyze the sentiment of text data. It identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. Use this skill when a user requests sentiment analysis, opinion mining, or emotion detection on any text, such as customer reviews, social media posts, or survey responses. Trigger words include "sentiment analysis", "analyze sentiment", "opinion mining", "emotion detection", and "polarity".

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 provide interpretability and explainability for machine learning models. It is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior. The skill leverages techniques like SHAP and LIME to generate explanations. It is useful when debugging model performance, ensuring fairness, or communicating model insights to stakeholders. Use this skill when the user mentions "explain model", "interpret model", "feature importance", "SHAP values", or "LIME explanations".

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.

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 enables Claude to generate data visualizations, plots, charts, and graphs from provided data. It analyzes the data, selects the most appropriate visualization type, and creates a visually appealing and informative graphic. Use this skill when the user requests a visualization, plot, chart, or graph; when data needs to be presented visually; or when exploring data patterns. The skill is triggered by requests for "visualization", "plot", "chart", or "graph".

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.

This skill enables Claude to analyze logs for performance insights and issue detection. It is triggered when the user requests log analysis, performance troubleshooting, or debugging assistance. The skill identifies slow requests, error patterns, resource warnings, and other key performance indicators within log files. Use this skill when the user mentions "analyze logs", "performance issues", "error patterns in logs", "slow requests", or requests help with "log aggregation". It helps identify performance bottlenecks and improve application stability by analyzing log data.

Fix accessibility issues.

This skill enables Claude to analyze logs for performance insights and issue detection. It is triggered when the user requests log analysis, performance troubleshooting, or debugging assistance. The skill identifies slow requests, error patterns, resource warnings, and other key performance indicators within log files. Use this skill when the user mentions "analyze logs", "performance issues", "error patterns in logs", "slow requests", or requests help with "log aggregation". It helps identify performance bottlenecks and improve application stability by analyzing log data.

Provides comprehensive technical analysis for stocks and ETFs using RSI, MACD, Bollinger Bands, and other indicators. Activates when user requests stock analysis, technical indicators, trading signals, or market data for specific ticker symbols.

This skill should be used when the user asks to "build background agent", "create hosted coding agent", "set up sandboxed execution", "implement multiplayer agent", or mentions background agents, sandboxed VMs, agent infrastructure, Modal sandboxes, self-spawning agents, or remote coding environments.