[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6PECMSNGQ6D0V0BVVMVVTHN":3,"skill-same:01M6PECMSNGQ6D0V0BVVMVVTHN":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},"01M6PECMSNGQ6D0V0BVVMVVTHN","engineering-features-for-machine-learning",null,"This skill empowers Claude to perform feature engineering tasks for machine learning. It creates, selects, and transforms features to improve model performance. Use this skill when the user requests feature creation, feature selection, feature transformation, or any request that involves improving the features used in a machine learning model. Trigger terms include \"feature engineering\", \"feature selection\", \"feature transformation\", \"create features\", \"select features\", \"transform features\", \"improve model performance\", and similar phrases related to feature manipulation.","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\u002Ffeature-engineering-toolkit\u002Fskills\u002Ffeature-engineering-toolkit\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],"machine learning","features","for","machine","Engineering Features for Machine Learning","learning","engineering","improve","model","select","use","feature engineering","selection","creation","feature","transformation","trigger","create","tasks","performance","2026-11-10 10:12:30",false,"---\nname: engineering-features-for-machine-learning\ndescription: |\n  This skill empowers Claude to perform feature engineering tasks for machine learning. It creates, selects, and transforms features to improve model performance. Use this skill when the user requests feature creation, feature selection, feature transformation, or any request that involves improving the features used in a machine learning model. Trigger terms include \"feature engineering\", \"feature selection\", \"feature transformation\", \"create features\", \"select features\", \"transform features\", \"improve model performance\", and similar phrases related to feature manipulation.\n---\n\n## Overview\n\nThis skill enables Claude to leverage the feature-engineering-toolkit plugin to enhance machine learning models. It automates the process of creating new features, selecting the most relevant ones, and transforming existing features to better suit the model's needs. By using this skill, you can improve the accuracy, efficiency, and interpretability of your machine learning models.\n\n## How It Works\n\n1. **Analyzing Requirements**: Claude analyzes the user's request and identifies the specific feature engineering task required.\n2. **Generating Code**: Claude generates Python code using the feature-engineering-toolkit plugin to perform the requested task. This includes data validation and error handling.\n3. **Executing Task**: The generated code is executed, creating, selecting, or transforming features as requested.\n4. **Providing Insights**: Claude provides performance metrics and insights related to the feature engineering process, such as the importance of newly created features or the impact of transformations on model performance.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Create new features from existing data to improve model accuracy.\n- Select the most relevant features from a dataset to reduce model complexity and improve efficiency.\n- Transform features to better suit the assumptions of a machine learning model (e.g., scaling, normalization, encoding).\n\n## Examples\n\n### Example 1: Improving Model Accuracy\n\nUser request: \"Create new features from the existing 'age' and 'income' columns to improve the accuracy of a customer churn prediction model.\"\n\nThe skill will:\n1. Generate code to create interaction terms between 'age' and 'income' (e.g., age * income, age \u002F income).\n2. Execute the code and evaluate the impact of the new features on model performance.\n\n### Example 2: Reducing Model Complexity\n\nUser request: \"Select the top 10 most important features from the dataset to reduce the complexity of a fraud detection model.\"\n\nThe skill will:\n1. Generate code to calculate feature importance using a suitable method (e.g., Random Forest, SelectKBest).\n2. Execute the code and select the top 10 features based on their importance scores.\n\n## Best Practices\n\n- **Data Validation**: Always validate the input data to ensure it is clean and consistent before performing feature engineering.\n- **Feature Scaling**: Scale numerical features to prevent features with larger ranges from dominating the model.\n- **Encoding Categorical Features**: Encode categorical features appropriately (e.g., one-hot encoding, label encoding) to make them suitable for machine learning models.\n\n## Integration\n\nThis skill integrates with the feature-engineering-toolkit plugin, providing a seamless way to create, select, and transform features for machine learning models. It can be used in conjunction with other Claude Code skills to build complete machine learning pipelines.\n","## Overview\n\nThis skill enables Claude to leverage the feature-engineering-toolkit plugin to enhance machine learning models. It automates the process of creating new features, selecting the most relevant ones, and transforming existing features to better suit the model's needs. By using this skill, you can improve the accuracy, efficiency, and interpretability of your machine learning models.\n\n## How It Works\n\n1. **Analyzing Requirements**: Claude analyzes the user's request and identifies the specific feature engineering task required.\n2. **Generating Code**: Claude generates Python code using the feature-engineering-toolkit plugin to perform the requested task. This includes data validation and error handling.\n3. **Executing Task**: The generated code is executed, creating, selecting, or transforming features as requested.\n4. **Providing Insights**: Claude provides performance metrics and insights related to the feature engineering process, such as the importance of newly created features or the impact of transformations on model performance.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Create new features from existing data to improve model accuracy.\n- Select the most relevant features from a dataset to reduce model complexity and improve efficiency.\n- Transform features to better suit the assumptions of a machine learning model (e.g., scaling, normalization, encoding).\n\n## Examples\n\n### Example 1: Improving Model Accuracy\n\nUser request: \"Create new features from the existing 'age' and 'income' columns to improve the accuracy of a customer churn prediction model.\"\n\nThe skill will:\n1. Generate code to create interaction terms between 'age' and 'income' (e.g., age * income, age \u002F income).\n2. Execute the code and evaluate the impact of the new features on model performance.\n\n### Example 2: Reducing Model Complexity\n\nUser request: \"Select the top 10 most important features from the dataset to reduce the complexity of a fraud detection model.\"\n\nThe skill will:\n1. Generate code to calculate feature importance using a suitable method (e.g., Random Forest, SelectKBest).\n2. Execute the code and select the top 10 features based on their importance scores.\n\n## Best Practices\n\n- **Data Validation**: Always validate the input data to ensure it is clean and consistent before performing feature engineering.\n- **Feature Scaling**: Scale numerical features to prevent features with larger ranges from dominating the model.\n- **Encoding Categorical Features**: Encode categorical features appropriately (e.g., one-hot encoding, label encoding) to make them suitable for machine learning models.\n\n## Integration\n\nThis skill integrates with the feature-engineering-toolkit plugin, providing a seamless way to create, select, and transform features for machine learning models. It can be used in conjunction with other Claude Code skills to build complete machine learning pipelines.\n",{"name":5,"description":7},"master","be156301b65e301887f327d08190ed4f17ba08d2","docs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Ffeature-engineering-toolkit\u002Fskills\u002Ffeature-engineering-toolkit",0,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp","https:\u002F\u002Fgithub.com\u002Fsandraschi","2026-11-12 11:03:54",{"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 engineering-features-for-machine-learning",{"tool":62,"command":63},"bunx","bunx skills add sandraschi\u002Fdocumentation-mcp --skill engineering-features-for-machine-learning",{"tool":65,"command":66},"pnpm","pnpm dlx skills add sandraschi\u002Fdocumentation-mcp --skill engineering-features-for-machine-learning","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\u002Ffeature-engineering-toolkit\u002Fskills\u002Ffeature-engineering-toolkit\u002FSKILL.md",true,[70,76,82],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"assets\u002FREADME.md","ASSET","资产文件",479,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Ffeature-engineering-toolkit\u002Fskills\u002Ffeature-engineering-toolkit\u002Fassets\u002FREADME.md",{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"references\u002FREADME.md","REFERENCE","参考文档",764,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Ffeature-engineering-toolkit\u002Fskills\u002Ffeature-engineering-toolkit\u002Freferences\u002FREADME.md",{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"scripts\u002FREADME.md","SCRIPT","脚本",746,"https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fnot-mcp-related\u002Fclaude-skills\u002Ftemp\u002Fcode-plugins\u002Fplugins\u002Fai-ml\u002Ffeature-engineering-toolkit\u002Fskills\u002Ffeature-engineering-toolkit\u002Fscripts\u002FREADME.md",[89,111,137,147,172,197],{"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},[100,101,102,103,34,104,105,106,107,108,109,110],"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",[],[118,119,120,121,122,123,124,125,25,126,127,128,129,130,131,132,133,134,135,136],"drift","wrappee drift","api","data","audit","capabilities","training","memory","repo audit","competitors","fleet","now","software","tool list","only","never","coverage","list","read",{"skillNo":138,"name":139,"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":140,"categories":141,"keywords":146,"publishedAt":44},"01M6PFADA3X031SK3W3T6X0N6H","modules","https:\u002F\u002Fgithub.com\u002Fsandraschi\u002Fdocumentation-mcp\u002Fblob\u002Fmaster\u002Fdocs\u002Fskills\u002Fwindsurf-mcp-standards\u002Fmodules\u002FSKILL.md",[142,145],{"categoryNo":143,"categoryName":144,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[139],{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":157,"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",[153,156],{"categoryNo":154,"categoryName":155,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[158,110,103,34,159,160,161,162,163,164,26,165,166,167,168,169,109,170,171,104],"mcp standards","monitoring","sampling","scaffolding","servers","cd","error","CI\u002FCD","access","ci","mcp servers","error handling","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":154,"categoryName":155,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[181,182,183,184,185,110,186,187,188,26,40,189,190,191,192,193,194,195,106,196],"webapp","test","webapp cua test","playwright","mcp server","dev","dom","assertions","click","app","tauri","repo","stack","e2e","ocr","browser",{"skillNo":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":201,"categories":202,"keywords":204,"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",[203],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[199,192,26,40,205,206,207,208,209,210,211,106,212,186,213,214,215,185,216,110],"gates","ship it","artifact","done","say","work","publish","push","shipping","quality gates","commit","github"]