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Evaluates machine learning models using metrics like accuracy, precision, recall, F1-score via model-evaluation-suite plugin. Useful for performance analysis, validation, model comparison, and optimization.
How this skill is triggered — by the user, by Claude, or both
Slash command
/model-evaluation-suite:evaluating-machine-learning-modelsThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Evaluate machine learning models using a comprehensive suite of metrics including accuracy, precision, recall, F1-score, and custom KPIs.
Evaluate machine learning models using a comprehensive suite of metrics including accuracy, precision, recall, F1-score, and custom KPIs.
This skill empowers Claude to perform thorough evaluations of machine learning models, providing detailed performance insights. It leverages the model-evaluation-suite plugin to generate a range of metrics, enabling informed decisions about model selection and optimization.
/eval-model command to initiate the model evaluation process within the model-evaluation-suite plugin.This skill activates when you need to:
User request: "Evaluate the accuracy of my image classification model."
The skill will:
/eval-model command.User request: "Compare the F1-score of model A and model B."
The skill will:
/eval-model command for both models.This skill integrates seamlessly with the model-evaluation-suite plugin, providing a comprehensive solution for model evaluation within the Claude Code environment. It can be combined with other skills to build automated machine learning workflows.
The skill produces structured output relevant to the task.
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin model-evaluation-suiteEvaluates ML model performance: runs static LLM usage analysis, detects stack, compares metrics to baseline, checks data drift and error patterns.
Evaluate model performance — check for accuracy drops, data drift, and error patterns. Use when asked about "model accuracy dropped", "evaluate the model", "check for drift", or "model performance".
Builds structured evaluation suites for LLM and AI system performance using reproducible metrics. Use when testing model quality, prompt changes, or regression detection.