running-clustering-algorithms
SolidThis 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.
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Quality Score: 93/100
Skill Content
Details
- Author
- jeremylongshore
- Repository
- jeremylongshore/claude-code-plugins-plus-skills
- Created
- 7 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Integrates with
Similar Skills
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running-clustering-algorithms
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.
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training-machine-learning-models
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This skill empowers Claude to perform regression analysis and modeling using the regression-analysis-tool plugin. It analyzes datasets, generates appropriate regression models (linear, polynomial, etc.), validates the models, and provides performance metrics. Use this skill when the user explicitly requests regression analysis, prediction based on data, or mentions terms like "linear regression," "polynomial regression," "regression model," or "predictive modeling." This skill is also helpful when the user needs to understand the relationship between variables in a dataset.
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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.