[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PEACG98MC7EX3KK7VM8S3V":3,"skill-same:01M6PEACG98MC7EX3KK7VM8S3V":88},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6PEACG98MC7EX3KK7VM8S3V","building-classification-models",null,"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.","sandraschi\u002Fdocumentation-mcp","sandraschi","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F34750307?v=4","01M6C42H9WE5EC9CK3N5RGZP8S","Sandra Schipal","01M6PE1X2FZP8KR5GANPPFTHA8",2,"MIT","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fclassification-model-builder\u002Fskills\u002Fclassification-model-builder\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"classification","Building Classification Models","models","use","evaluate","requests","with","error","creation","build","builder","performance","model","learning","Optimization","best practices","error handling","create","tasks","plugin","2026-11-10 10:12:30",false,"---\nname: building-classification-models\ndescription: |\n  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.\n---\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.\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.\n",{"name":5,"description":7},"master","be156301b65e301887f327d08190ed4f17ba08d2","docs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fclassification-model-builder\u002Fskills\u002Fclassification-model-builder",0,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp","https:\u002F\u002Fgithub.com\u002Fsandraschi","2026-11-13 15:00:21",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add sandraschi\u002Fdocumentation-mcp --skill building-classification-models",{"tool":62,"command":63},"bunx","bunx skills add sandraschi\u002Fdocumentation-mcp --skill building-classification-models",{"tool":65,"command":66},"pnpm","pnpm dlx skills add sandraschi\u002Fdocumentation-mcp --skill building-classification-models","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fclassification-model-builder\u002Fskills\u002Fclassification-model-builder\u002FSKILL.md",true,[70,76,82],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"assets\u002FREADME.md","ASSET","资产文件",412,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fclassification-model-builder\u002Fskills\u002Fclassification-model-builder\u002Fassets\u002FREADME.md",{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002FREADME.md","REFERENCE","参考文档",685,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fclassification-model-builder\u002Fskills\u002Fclassification-model-builder\u002Freferences\u002FREADME.md",{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"scripts\u002FREADME.md","SCRIPT","脚本",521,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Fclassification-model-builder\u002Fskills\u002Fclassification-model-builder\u002Fscripts\u002FREADME.md",[89,111,138,148,172,198],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":99,"publishedAt":44},"01M6PFAWAHEJJCKKVNXEWNK87W","Docs Research","Use MCP Central Docs server to search, ask, and retrieve documentation (FastMCP, standards, packaging).","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fsrc\u002Fdocs_mcp\u002Fskills\u002Fdocs-research\u002FSKILL.md",[95,98],{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[100,101,102,103,27,104,105,106,107,108,109,110],"docs research","docs","research","standards","packaging","ask","server","search","fastmcp","documentation","MCP",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":44},"01M6PFAN4DAENMY574Q7Q3CB3H","wrappee-drift","Read-only, single-repo audit of a repo's tool coverage against the software it wraps (its current real API\u002Fdocs, not training-data memory) and against comparable\u002Fcompeting wrappers. Trigger when the user says \"wrappee drift\" followed by a repo name, \"capability audit\" followed by a repo name, or asks whether a repo is missing features the wrapped software or a competitor now has. Always scoped to one named repo per invocation -- never trigger this fleet-wide or unprompted; that is explicitly against the SOP. Do NOT trigger for internal fleet-standard compliance (that is drift-scan) or strategic worth\u002Farchive decisions (that is quality-check) -- this is specifically about the gap between the repo's tool list and the wrapped software's\u002Fcompetitors' actual current capabilities.","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwrappee-drift\u002FSKILL.md",[],[118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137],"drift","wrappee drift","api","data","audit","capabilities","training","memory","features","repo audit","competitors","fleet","now","software","tool list","only","never","coverage","list","read",{"skillNo":139,"name":140,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":147,"publishedAt":44},"01M6PFADA3X031SK3W3T6X0N6H","modules","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwindsurf-mcp-standards\u002Fmodules\u002FSKILL.md",[143,146],{"categoryNo":144,"categoryName":145,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[140],{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":158,"publishedAt":44},"01M6PFA8YA029N68TW0X31FB1W","mcp-standards","Use when developing MCP servers to access comprehensive standards for scaffolding, packaging, sampling, documentation, testing, error handling, monitoring, and CI\u002FCD workflows.","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwindsurf-mcp-standards\u002FSKILL.md",[154,157],{"categoryNo":155,"categoryName":156,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[159,110,103,27,160,161,162,163,164,31,165,166,167,168,169,40,109,170,171,104],"mcp standards","monitoring","sampling","scaffolding","servers","cd","for","CI\u002FCD","access","ci","mcp servers","workflows","testing",{"skillNo":173,"name":174,"nameZh":6,"description":175,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":176,"categories":177,"keywords":180,"publishedAt":44},"01M6PFA1XX344WX57B0DJZY94Q","webapp-cua-test","Run the pre-Tauri CUA click-through test (real browser, dev stack, OCR \"Connected\" wait, sidebar nav walk) for a fleet MCP server repo under D:\\Dev\\repos. Trigger when the user says \"cua webapp test\" or \"webapp clickthrough\" followed by a repo name, asks for an e2e click-through of a webapp, or asks whether a webapp boots and connects in a real browser. Not for the installed NSIS app (that is the nsis-build skill \u002F cua-nsis-test) and not for DOM assertions (Playwright), and not for hunting fake data or dead buttons (fakefind).","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwebapp-cua-test\u002FSKILL.md",[178,179],{"categoryNo":155,"categoryName":156,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[181,182,183,184,185,110,186,187,188,165,189,190,191,192,193,194,195,196,106,197],"webapp","test","webapp cua test","playwright","mcp server","dev","dom","assertions","trigger","click","app","tauri","repo","stack","e2e","ocr","browser",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":205,"publishedAt":44},"01M6PF9WYF6NWJR7WVN38TT0WH","ship","Final release gate for a fleet MCP server repo under D:\\Dev\\repos -- run all quality gates, build the release artifact, verify it, commit, push, and cut a GitHub release. Trigger this whenever the user says \"ship it\", \"ship\" followed by a repo name, \"release\" followed by a repo name, or asks to finalize and publish work that's already been done in a repo. This is the terminal step after assfix\u002Fnsis-build, not a substitute for them -- if the repo hasn't been assessed recently, say so before shipping rather than shipping blind.","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fship\u002FSKILL.md",[204],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[200,193,165,189,206,207,208,209,210,211,212,106,213,186,214,215,216,185,217,110],"gates","ship it","artifact","done","say","work","publish","push","shipping","quality gates","commit","github"]