[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CD5PS5STX4ESN3YVXP66HK":3,"skill-same:01M6CD5PS5STX4ESN3YVXP66HK":141},{"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":34,"linkOnly":35,"markdownContent":36,"markdownBody":37,"frontMatter":38,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":6,"ownerHtmlUrl":45,"ownerBlogUrl":46,"ownerDescription":47,"updatedAt":48,"install":49,"resources":62},"01M6CD5PS5STX4ESN3YVXP66HK","building-classification-models",null,"Build and evaluate classification models for supervised learning tasks with labeled data. Use when requesting \"build a classifier\", \"create classification model\", or \"train classifier\".","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\u002Fclassification-model-builder\u002Fskills\u002Fclassification-model-builder\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"classification","models","Building Classification Models","tasks","create","data","Bash","learning","model","grep","read","write","edit","use","classifier","2026-11-10 10:12:30",false,"---\nname: building-classification-models\ndescription: Build and evaluate classification models for supervised learning tasks\n  with labeled data. Use when requesting \"build a classifier\", \"create classification\n  model\", or \"train classifier\".\nallowed-tools: Read, Write, Edit, Grep, Glob, Bash\nlicense: MIT\n---\n## Overview\n\nThis skill empowers Claude to efficiently build and deploy classification models. It automates the process of model selection, training, and evaluation, providing users with a robust and reliable classification solution. The skill also provides insights into model performance and suggests potential improvements.\n\n## How It Works\n\n1. **Context Analysis**: Claude analyzes the user's request, identifying the dataset, target variable, and any specific requirements for the classification model.\n2. **Model Generation**: The skill utilizes the classification-model-builder plugin to generate code for training a classification model based on the identified dataset and requirements. This includes data preprocessing, feature selection, model selection, and hyperparameter tuning.\n3. **Evaluation and Reporting**: The generated model is trained and evaluated using appropriate metrics (e.g., accuracy, precision, recall, F1-score). Performance metrics and insights are then provided to the user.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Build a classification model from a given dataset.\n- Train a classifier to predict categorical outcomes.\n- Evaluate the performance of a classification model.\n\n## Examples\n\n### Example 1: Building a Spam Classifier\n\nUser request: \"Build a classifier to detect spam emails using this dataset.\"\n\nThe skill will:\n1. Analyze the provided email dataset to identify features and the target variable (spam\u002Fnot spam).\n2. Generate Python code using the classification-model-builder plugin to train a spam classification model, including data cleaning, feature extraction, and model selection.\n\n### Example 2: Predicting Customer Churn\n\nUser request: \"Create a classification model to predict customer churn using customer data.\"\n\nThe skill will:\n1. Analyze the customer data to identify relevant features and the churn status.\n2. Generate code to build a classification model for churn prediction, including data validation, model training, and performance reporting.\n\n## Best Practices\n\n- **Data Quality**: Ensure the input data is clean and preprocessed before training the model.\n- **Model Selection**: Choose the appropriate classification algorithm based on the characteristics of the data and the specific requirements of the task.\n- **Hyperparameter Tuning**: Optimize the model's hyperparameters to achieve the best possible performance.\n\n## Integration\n\nThis skill integrates with the classification-model-builder plugin to automate the model building process. It can also be used in conjunction with other plugins for data analysis and visualization.","## Overview\n\nThis skill empowers Claude to efficiently build and deploy classification models. It automates the process of model selection, training, and evaluation, providing users with a robust and reliable classification solution. The skill also provides insights into model performance and suggests potential improvements.\n\n## How It Works\n\n1. **Context Analysis**: Claude analyzes the user's request, identifying the dataset, target variable, and any specific requirements for the classification model.\n2. **Model Generation**: The skill utilizes the classification-model-builder plugin to generate code for training a classification model based on the identified dataset and requirements. This includes data preprocessing, feature selection, model selection, and hyperparameter tuning.\n3. **Evaluation and Reporting**: The generated model is trained and evaluated using appropriate metrics (e.g., accuracy, precision, recall, F1-score). Performance metrics and insights are then provided to the user.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Build a classification model from a given dataset.\n- Train a classifier to predict categorical outcomes.\n- Evaluate the performance of a classification model.\n\n## Examples\n\n### Example 1: Building a Spam Classifier\n\nUser request: \"Build a classifier to detect spam emails using this dataset.\"\n\nThe skill will:\n1. Analyze the provided email dataset to identify features and the target variable (spam\u002Fnot spam).\n2. Generate Python code using the classification-model-builder plugin to train a spam classification model, including data cleaning, feature extraction, and model selection.\n\n### Example 2: Predicting Customer Churn\n\nUser request: \"Create a classification model to predict customer churn using customer data.\"\n\nThe skill will:\n1. Analyze the customer data to identify relevant features and the churn status.\n2. Generate code to build a classification model for churn prediction, including data validation, model training, and performance reporting.\n\n## Best Practices\n\n- **Data Quality**: Ensure the input data is clean and preprocessed before training the model.\n- **Model Selection**: Choose the appropriate classification algorithm based on the characteristics of the data and the specific requirements of the task.\n- **Hyperparameter Tuning**: Optimize the model's hyperparameters to achieve the best possible performance.\n\n## Integration\n\nThis skill integrates with the classification-model-builder plugin to automate the model building process. It can also be used in conjunction with other plugins for data analysis and visualization.",{"name":5,"license":15,"description":7,"allowed-tools":39},"Read, Write, Edit, Grep, Glob, Bash","main","01d3fafeca1133f841956b8b90b939fee92d71e7","plugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Fai-ml\u002Fclassification-model-builder\u002Fskills\u002Fclassification-model-builder",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":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add BbgnsurfTech\u002Fclaude-skills-collection --skill building-classification-models",{"tool":55,"command":56},"bunx","bunx skills add BbgnsurfTech\u002Fclaude-skills-collection --skill building-classification-models",{"tool":58,"command":59},"pnpm","pnpm dlx skills add BbgnsurfTech\u002Fclaude-skills-collection --skill 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coverage\".","https:\u002F\u002Fgithub.com\u002FBbgnsurfTech\u002Fclaude-skills-collection\u002Fblob\u002Fmain\u002Fplugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Ftesting\u002Funit-test-generator\u002Fskills\u002Funit-test-generator\u002FSKILL.md",[168],{"categoryNo":149,"categoryName":150,"taxonomyType":151,"taxonomyTypeDesc":152},[170,171,172,23,173,158,31,174,175,176,32,177,178,30,29,28,25,179],"unit tests","Generating Unit Tests","generate","add test","functions","modules","test coverage","coverage","code","edge",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":192,"publishedAt":34},"01M6CDVPQKGQ450Q50ZC84FPMR","generating-test-reports","Generate comprehensive test reports with metrics, coverage, and visualizations.\nUse 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",[186,187],{"categoryNo":149,"categoryName":150,"taxonomyType":151,"taxonomyTypeDesc":152},{"categoryNo":188,"categoryName":189,"taxonomyType":190,"taxonomyTypeDesc":191},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[158,193,194,195,196,32,177,31,197,30,29,198,25,199,172,23,200,155,28],"reports","Generating Test Reports","test report","specialized","report","visualizations","metrics","documentation",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":205,"categories":206,"keywords":208,"publishedAt":34},"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 execution\".","https:\u002F\u002Fgithub.com\u002FBbgnsurfTech\u002Fclaude-skills-collection\u002Fblob\u002Fmain\u002Fplugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Ftesting\u002Ftest-orchestrator\u002Fskills\u002Ftest-orchestrator\u002FSKILL.md",[207],{"categoryNo":149,"categoryName":150,"taxonomyType":151,"taxonomyTypeDesc":152},[209,210,158,29,32,31,30,196,28,211,25,212,213,155],"orchestrating test execution","execution","parallel","orchestrate","run",{"skillNo":215,"name":216,"nameZh":6,"description":217,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":218,"categories":219,"keywords":221,"publishedAt":34},"01M6CDVFTCBQ65TGB1YJCZB90G","managing-test-environments","Provision and manage isolated test environments with configuration and data.\nUse when performing specialized testing.\nTrigger with phrases like \"manage test 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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",[233],{"categoryNo":149,"categoryName":150,"taxonomyType":151,"taxonomyTypeDesc":152},[158,235,236,32,31,30,29,28,25,172,23,224],"Generating Test Doubles","dependency"]