[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CD7EGQ8A2QJG96D170PEFJ":3,"skill-same:01M6CD7EGQ8A2QJG96D170PEFJ":112},{"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":36,"linkOnly":37,"markdownContent":38,"markdownBody":39,"frontMatter":40,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":50,"repositoryHtmlUrl":51,"repositoryHomepageUrl":6,"ownerHtmlUrl":52,"ownerBlogUrl":53,"ownerDescription":54,"updatedAt":55,"install":56,"resources":69},"01M6CD7EGQ8A2QJG96D170PEFJ","evaluating-machine-learning-models",null,"This skill allows claude to evaluate machine learning models using a comprehensive suite of metrics. it should be used when the user requests model performance analysis, validation, or testing. claude can use this skill to assess model accuracy, p...","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\u002Fmodel-evaluation-suite\u002Fskills\u002Fmodel-evaluation-suite\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35],"Evaluating Machine Learning Models","machine learning","models","learning","model","use","user","edit","write","read","grep","testing","Bash","analysis","metrics","validation","performance","2026-11-10 10:12:30",false,"---\ndescription: This skill allows claude to evaluate machine learning models using a\n  comprehensive suite of metrics. it should be used when the user requests model performance\n  analysis, validation, or testing. claude can use this skill to assess model accuracy,\n  p...\nallowed-tools:\n- Read\n- Write\n- Edit\n- Grep\n- Glob\n- Bash\nname: evaluating-machine-learning-models\nlicense: MIT\n---\n## Overview\n\nThis skill empowers Claude to perform thorough evaluations of machine learning models, providing detailed performance insights. It leverages the `model-evaluation-suite` plugin to generate a range of metrics, enabling informed decisions about model selection and optimization.\n\n## How It Works\n\n1. **Analyzing Context**: Claude analyzes the user's request to identify the model to be evaluated and any specific metrics of interest.\n2. **Executing Evaluation**: Claude uses the `\u002Feval-model` command to initiate the model evaluation process within the `model-evaluation-suite` plugin.\n3. **Presenting Results**: Claude presents the generated metrics and insights to the user, highlighting key performance indicators and potential areas for improvement.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Assess the performance of a machine learning model.\n- Compare the performance of multiple models.\n- Identify areas where a model can be improved.\n- Validate a model's performance before deployment.\n\n## Examples\n\n### Example 1: Evaluating Model Accuracy\n\nUser request: \"Evaluate the accuracy of my image classification model.\"\n\nThe skill will:\n1. Invoke the `\u002Feval-model` command.\n2. Analyze the model's performance on a held-out dataset.\n3. Report the accuracy score and other relevant metrics.\n\n### Example 2: Comparing Model Performance\n\nUser request: \"Compare the F1-score of model A and model B.\"\n\nThe skill will:\n1. Invoke the `\u002Feval-model` command for both models.\n2. Extract the F1-score from the evaluation results.\n3. Present a comparison of the F1-scores for model A and model B.\n\n## Best Practices\n\n- **Specify Metrics**: Clearly define the specific metrics of interest for the evaluation.\n- **Data Validation**: Ensure the data used for evaluation is representative of the real-world data the model will encounter.\n- **Interpret Results**: Provide context and interpretation of the evaluation results to facilitate informed decision-making.\n\n## Integration\n\nThis skill integrates seamlessly with the `model-evaluation-suite` plugin, providing a comprehensive solution for model evaluation within the Claude Code environment. It can be combined with other skills to build automated machine learning workflows.","## Overview\n\nThis skill empowers Claude to perform thorough evaluations of machine learning models, providing detailed performance insights. It leverages the `model-evaluation-suite` plugin to generate a range of metrics, enabling informed decisions about model selection and optimization.\n\n## How It Works\n\n1. **Analyzing Context**: Claude analyzes the user's request to identify the model to be evaluated and any specific metrics of interest.\n2. **Executing Evaluation**: Claude uses the `\u002Feval-model` command to initiate the model evaluation process within the `model-evaluation-suite` plugin.\n3. **Presenting Results**: Claude presents the generated metrics and insights to the user, highlighting key performance indicators and potential areas for improvement.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Assess the performance of a machine learning model.\n- Compare the performance of multiple models.\n- Identify areas where a model can be improved.\n- Validate a model's performance before deployment.\n\n## Examples\n\n### Example 1: Evaluating Model Accuracy\n\nUser request: \"Evaluate the accuracy of my image classification model.\"\n\nThe skill will:\n1. Invoke the `\u002Feval-model` command.\n2. Analyze the model's performance on a held-out dataset.\n3. Report the accuracy score and other relevant metrics.\n\n### Example 2: Comparing Model Performance\n\nUser request: \"Compare the F1-score of model A and model B.\"\n\nThe skill will:\n1. Invoke the `\u002Feval-model` command for both models.\n2. Extract the F1-score from the evaluation results.\n3. Present a comparison of the F1-scores for model A and model B.\n\n## Best Practices\n\n- **Specify Metrics**: Clearly define the specific metrics of interest for the evaluation.\n- **Data Validation**: Ensure the data used for evaluation is representative of the real-world data the model will encounter.\n- **Interpret Results**: Provide context and interpretation of the evaluation results to facilitate informed decision-making.\n\n## Integration\n\nThis skill integrates seamlessly with the `model-evaluation-suite` plugin, providing a comprehensive solution for model evaluation within the Claude Code environment. It can be combined with other skills to build automated machine learning workflows.",{"name":5,"license":15,"description":7,"allowed-tools":41},[42,43,44,45,46,31],"Read","Write","Edit","Grep","Glob","main","01d3fafeca1133f841956b8b90b939fee92d71e7","plugins\u002Fclaude-code-plugins-plus\u002Fplugins\u002Fai-ml\u002Fmodel-evaluation-suite\u002Fskills\u002Fmodel-evaluation-suite",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":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add BbgnsurfTech\u002Fclaude-skills-collection --skill evaluating-machine-learning-models",{"tool":62,"command":63},"bunx","bunx 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",[119],{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[125,30,126,26,29,28,27,24,127,128,129,130,131,31],"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",[157,158],{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},{"categoryNo":159,"categoryName":160,"taxonomyType":161,"taxonomyTypeDesc":162},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[128,164,165,166,167,24,148,26,168,27,28,169,31,33,142,143,170,30,29],"reports","Generating Test Reports","test report","specialized","report","visualizations","documentation",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":178,"publishedAt":36},"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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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",[204],{"categoryNo":120,"categoryName":121,"taxonomyType":122,"taxonomyTypeDesc":123},[128,206,207,24,26,27,28,29,31,142,143,195],"Generating Test Doubles","dependency"]