[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CD78PN4V273SXTJ4TY77MP":3,"skill-same:01M6CD78PN4V273SXTJ4TY77MP":116},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":37,"linkOnly":38,"markdownContent":39,"markdownBody":40,"frontMatter":41,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":49,"ownerDescription":50,"updatedAt":51,"install":52,"resources":65},"01M6CD78PN4V273SXTJ4TY77MP","training-machine-learning-models",null,"Train machine learning models with automated workflows. Analyzes datasets, selects model types (classification, regression), configures parameters, trains with cross-validation, and saves model artifacts. Use when asked to \"train model\" or \"evalua...","BbgnsurfTech\u002Fclaude-skills-collection","BbgnsurfTech","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F59598605?v=4","01M6C5ZT4G3G4QPQB64QDVW438","Bbgnsurf Technologies","01M6C5ZT8JN4VWSZZH3DD8J4EG",29,"MIT","https:\u002F\u002Fgithub.com\u002FBbgnsurfTech\u002Fclaude-skills-collection\u002Fblob\u002Fmain\u002Fplugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Fai-ml\u002Fml-model-trainer\u002Fskills\u002Fml-model-trainer\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"Training Machine Learning Models","training","models","learning","machine learning","regression","datasets","use","edit","write","read","grep","model","Bash","classification","artifacts","validation","workflows","2026-11-10 10:12:30",false,"---\nname: training-machine-learning-models\ndescription: Train machine learning models with automated workflows. Analyzes datasets,\n  selects model types (classification, regression), configures parameters, trains\n  with cross-validation, and saves model artifacts. Use when asked to \"train model\"\n  or \"evalua...\nallowed-tools: Read, Write, Edit, Grep, Glob, Bash\nlicense: MIT\n---\n## Overview\n\nThis skill empowers Claude to automatically train and evaluate machine learning models. It streamlines the model development process by handling data analysis, model selection, training, and evaluation, ultimately providing a persisted model artifact.\n\n## How It Works\n\n1. **Data Analysis and Preparation**: The skill analyzes the provided dataset and identifies the target variable, determining the appropriate model type (classification, regression, etc.).\n2. **Model Selection and Training**: Based on the data analysis, the skill selects a suitable machine learning model and configures the training parameters. It then trains the model using cross-validation techniques.\n3. **Performance Evaluation and Persistence**: After training, the skill generates performance metrics to evaluate the model's effectiveness. Finally, it saves the trained model artifact for future use.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Train a machine learning model on a given dataset.\n- Evaluate the performance of a machine learning model.\n- Automate the machine learning model training process.\n\n## Examples\n\n### Example 1: Training a Classification Model\n\nUser request: \"Train a classification model on this dataset of customer churn data.\"\n\nThe skill will:\n1. Analyze the customer churn data, identify the churn status as the target variable, and determine that a classification model is appropriate.\n2. Select a suitable classification algorithm (e.g., Logistic Regression, Random Forest), train the model using cross-validation, and generate performance metrics such as accuracy, precision, and recall.\n\n### Example 2: Training a Regression Model\n\nUser request: \"Train a regression model to predict house prices based on features like size, location, and number of bedrooms.\"\n\nThe skill will:\n1. Analyze the house price data, identify the price as the target variable, and determine that a regression model is appropriate.\n2. Select a suitable regression algorithm (e.g., Linear Regression, Support Vector Regression), train the model using cross-validation, and generate performance metrics such as Mean Squared Error (MSE) and R-squared.\n\n## Best Practices\n\n- **Data Quality**: Ensure the dataset is clean and properly formatted before training the model.\n- **Feature Engineering**: Consider feature engineering techniques to improve model performance.\n- **Hyperparameter Tuning**: Experiment with different hyperparameter settings to optimize model performance.\n\n## Integration\n\nThis skill can be used in conjunction with other data analysis and manipulation tools to prepare data for training. It can also integrate with model deployment tools to deploy the trained model to production.","## Overview\n\nThis skill empowers Claude to automatically train and evaluate machine learning models. It streamlines the model development process by handling data analysis, model selection, training, and evaluation, ultimately providing a persisted model artifact.\n\n## How It Works\n\n1. **Data Analysis and Preparation**: The skill analyzes the provided dataset and identifies the target variable, determining the appropriate model type (classification, regression, etc.).\n2. **Model Selection and Training**: Based on the data analysis, the skill selects a suitable machine learning model and configures the training parameters. It then trains the model using cross-validation techniques.\n3. **Performance Evaluation and Persistence**: After training, the skill generates performance metrics to evaluate the model's effectiveness. Finally, it saves the trained model artifact for future use.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Train a machine learning model on a given dataset.\n- Evaluate the performance of a machine learning model.\n- Automate the machine learning model training process.\n\n## Examples\n\n### Example 1: Training a Classification Model\n\nUser request: \"Train a classification model on this dataset of customer churn data.\"\n\nThe skill will:\n1. Analyze the customer churn data, identify the churn status as the target variable, and determine that a classification model is appropriate.\n2. Select a suitable classification algorithm (e.g., Logistic Regression, Random Forest), train the model using cross-validation, and generate performance metrics such as accuracy, precision, and recall.\n\n### Example 2: Training a Regression Model\n\nUser request: \"Train a regression model to predict house prices based on features like size, location, and number of bedrooms.\"\n\nThe skill will:\n1. Analyze the house price data, identify the price as the target variable, and determine that a regression model is appropriate.\n2. Select a suitable regression algorithm (e.g., Linear Regression, Support Vector Regression), train the model using cross-validation, and generate performance metrics such as Mean Squared Error (MSE) and R-squared.\n\n## Best Practices\n\n- **Data Quality**: Ensure the dataset is clean and properly formatted before training the model.\n- **Feature Engineering**: Consider feature engineering techniques to improve model performance.\n- **Hyperparameter Tuning**: Experiment with different hyperparameter settings to optimize model performance.\n\n## Integration\n\nThis skill can be used in conjunction with other data analysis and manipulation tools to prepare data for training. It can also integrate with model deployment tools to deploy the trained model to production.",{"name":5,"license":15,"description":7,"allowed-tools":42},"Read, Write, Edit, Grep, Glob, Bash","main","01d3fafeca1133f841956b8b90b939fee92d71e7","plugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Fai-ml\u002Fml-model-trainer\u002Fskills\u002Fml-model-trainer",9,"https:\u002F\u002Fgithub.com\u002FBbgnsurfTech\u002Fclaude-skills-collection","https:\u002F\u002Fgithub.com\u002FBbgnsurfTech","www.bbgnsurf.com","An ICT solutions provider and software development company","2025-12-18 20:19:58",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add BbgnsurfTech\u002Fclaude-skills-collection --skill training-machine-learning-models",{"tool":58,"command":59},"bunx","bunx skills add BbgnsurfTech\u002Fclaude-skills-collection --skill training-machine-learning-models",{"tool":61,"command":62},"pnpm","pnpm dlx skills add BbgnsurfTech\u002Fclaude-skills-collection --skill 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visual changes in UI components using screenshot comparison.\nUse when detecting unintended UI changes or pixel differences.\nTrigger with phrases like \"test visual changes\", \"compare screenshots\", or \"detect UI regressions\".","https:\u002F\u002Fgithub.com\u002FBbgnsurfTech\u002Fclaude-skills-collection\u002Fblob\u002Fmain\u002Fplugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Ftesting\u002Fvisual-regression-tester\u002Fskills\u002Fvisual-regression-tester\u002FSKILL.md",[123],{"categoryNo":124,"categoryName":125,"taxonomyType":126,"taxonomyTypeDesc":127},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[129,130,24,27,30,29,28,26,131,132,133,134,135,32],"testing visual 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when performing specialized testing.\nTrigger with phrases like \"generate test report\", \"create test documentation\", or \"show test metrics\".","https:\u002F\u002Fgithub.com\u002FBbgnsurfTech\u002Fclaude-skills-collection\u002Fblob\u002Fmain\u002Fplugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Ftesting\u002Ftest-report-generator\u002Fskills\u002Ftest-report-generator\u002FSKILL.md",[161,162],{"categoryNo":124,"categoryName":125,"taxonomyType":126,"taxonomyTypeDesc":127},{"categoryNo":163,"categoryName":164,"taxonomyType":165,"taxonomyTypeDesc":166},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[132,168,169,170,171,26,152,27,172,28,29,173,32,174,146,147,175,130,30],"reports","Generating Test Reports","test report","specialized","report","visualizations","metrics","documentation",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":183,"publishedAt":37},"01M6CDVK97VFZ53CEYEGQ4QFZ6","orchestrating-test-execution","Coordinate parallel test execution across multiple environments and frameworks.\nUse when performing specialized testing.\nTrigger with phrases like \"orchestrate tests\", \"run parallel tests\", or \"coordinate test 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environment\", \"provision test env\", or \"setup test infrastructure\".","https:\u002F\u002Fgithub.com\u002FBbgnsurfTech\u002Fclaude-skills-collection\u002Fblob\u002Fmain\u002Fplugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Ftesting\u002Ftest-environment-manager\u002Fskills\u002Ftest-environment-manager\u002FSKILL.md",[195],{"categoryNo":124,"categoryName":125,"taxonomyType":126,"taxonomyTypeDesc":127},[197,132,27,28,29,171,30,32,198,199,200,130,201,202,26],"Managing Test Environments","data","infrastructure","setup","environment","manage",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":210,"publishedAt":37},"01M6CDVD0NPFXVEQGT9CMF02H0","generating-test-doubles","Generate mocks, stubs, spies, and fakes for dependency isolation.\nUse when creating mocks, stubs, or test isolation fixtures.\nTrigger with phrases like \"generate mocks\", \"create test doubles\", or \"setup stubs\".","https:\u002F\u002Fgithub.com\u002FBbgnsurfTech\u002Fclaude-skills-collection\u002Fblob\u002Fmain\u002Fplugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Ftesting\u002Ftest-doubles-generator\u002Fskills\u002Ftest-doubles-generator\u002FSKILL.md",[209],{"categoryNo":124,"categoryName":125,"taxonomyType":126,"taxonomyTypeDesc":127},[132,211,212,26,27,28,29,30,32,146,147,200],"Generating Test Doubles","dependency"]