[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F6W6RCPD3TPXFYG84H9YZ2":3,"skill-same:01M6F6W6RCPD3TPXFYG84H9YZ2":77},{"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":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":59,"ownerHtmlUrl":60,"ownerBlogUrl":6,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M6F6W6RCPD3TPXFYG84H9YZ2","detecting-data-anomalies",null,"This skill empowers Claude to identify anomalies and outliers within datasets. It leverages the anomaly-detection-system plugin to analyze data, apply appropriate machine learning algorithms, and highlight unusual data points. Use this skill when the user requests anomaly detection, outlier analysis, or identification of unusual patterns in data. Trigger this skill when the user mentions \"anomaly detection,\" \"outlier analysis,\" \"unusual data,\" or requests insights into data irregularities.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",67,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fjeremylongshore\u002Fclaude-code-plugins-plus-skills\u002Fanomaly-detection-system\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"data","detecting","Detecting Data Anomalies","datasets","insights","it","machine","identification","anomaly detection","trigger","patterns","machine learning","use","analyze","system","learning","analysis","anomaly-detection","detection","plugin","2026-11-10 10:12:30",false,"---\nname: detecting-data-anomalies\ndescription: |\n  This skill empowers Claude to identify anomalies and outliers within datasets. It leverages the anomaly-detection-system plugin to analyze data, apply appropriate machine learning algorithms, and highlight unusual data points. Use this skill when the user requests anomaly detection, outlier analysis, or identification of unusual patterns in data. Trigger this skill when the user mentions \"anomaly detection,\" \"outlier analysis,\" \"unusual data,\" or requests insights into data irregularities.\n---\n\n## Overview\n\nThis skill allows Claude to utilize the anomaly-detection-system plugin to pinpoint unusual data points within a given dataset. It automates the process of anomaly detection, providing insights into potential errors, fraud, or other significant deviations from expected patterns.\n\n## How It Works\n\n1. **Data Analysis**: Claude analyzes the user's request and the provided data to understand the context and requirements for anomaly detection.\n2. **Algorithm Selection**: Based on the data characteristics, Claude selects an appropriate anomaly detection algorithm (e.g., Isolation Forest, One-Class SVM).\n3. **Anomaly Identification**: The selected algorithm is applied to the data, and potential anomalies are identified based on their deviation from the norm.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Identify fraudulent transactions in financial data.\n- Detect unusual network traffic patterns that may indicate a security breach.\n- Find manufacturing defects based on sensor data from production lines.\n\n## Examples\n\n### Example 1: Fraud Detection\n\nUser request: \"Analyze this transaction data for potential fraud.\"\n\nThe skill will:\n1. Use the anomaly-detection-system plugin to identify transactions that deviate significantly from typical spending patterns.\n2. Highlight the potentially fraudulent transactions and provide a summary of their characteristics.\n\n### Example 2: Network Security\n\nUser request: \"Detect anomalies in network traffic to identify potential security threats.\"\n\nThe skill will:\n1. Use the anomaly-detection-system plugin to analyze network traffic data for unusual patterns.\n2. Identify potential security breaches based on deviations from normal network behavior.\n\n## Best Practices\n\n- **Data Preprocessing**: Ensure the data is clean, properly formatted, and scaled appropriately before applying anomaly detection algorithms.\n- **Algorithm Selection**: Choose an anomaly detection algorithm that is suitable for the type of data and the specific characteristics of the anomalies you are trying to detect.\n- **Threshold Tuning**: Carefully tune the threshold for anomaly detection to balance the trade-off between detecting true anomalies and minimizing false positives.\n\n## Integration\n\nThis skill can be used in conjunction with other data analysis and visualization tools to provide a more comprehensive understanding of the data and the identified anomalies. It can also be integrated with alerting systems to automatically notify users when anomalies are detected.\n","## Overview\n\nThis skill allows Claude to utilize the anomaly-detection-system plugin to pinpoint unusual data points within a given dataset. It automates the process of anomaly detection, providing insights into potential errors, fraud, or other significant deviations from expected patterns.\n\n## How It Works\n\n1. **Data Analysis**: Claude analyzes the user's request and the provided data to understand the context and requirements for anomaly detection.\n2. **Algorithm Selection**: Based on the data characteristics, Claude selects an appropriate anomaly detection algorithm (e.g., Isolation Forest, One-Class SVM).\n3. **Anomaly Identification**: The selected algorithm is applied to the data, and potential anomalies are identified based on their deviation from the norm.\n\n## When to Use This Skill\n\nThis skill activates when you need to:\n- Identify fraudulent transactions in financial data.\n- Detect unusual network traffic patterns that may indicate a security breach.\n- Find manufacturing defects based on sensor data from production lines.\n\n## Examples\n\n### Example 1: Fraud Detection\n\nUser request: \"Analyze this transaction data for potential fraud.\"\n\nThe skill will:\n1. Use the anomaly-detection-system plugin to identify transactions that deviate significantly from typical spending patterns.\n2. Highlight the potentially fraudulent transactions and provide a summary of their characteristics.\n\n### Example 2: Network Security\n\nUser request: \"Detect anomalies in network traffic to identify potential security threats.\"\n\nThe skill will:\n1. Use the anomaly-detection-system plugin to analyze network traffic data for unusual patterns.\n2. Identify potential security breaches based on deviations from normal network behavior.\n\n## Best Practices\n\n- **Data Preprocessing**: Ensure the data is clean, properly formatted, and scaled appropriately before applying anomaly detection algorithms.\n- **Algorithm Selection**: Choose an anomaly detection algorithm that is suitable for the type of data and the specific characteristics of the anomalies you are trying to detect.\n- **Threshold Tuning**: Carefully tune the threshold for anomaly detection to balance the trade-off between detecting true anomalies and minimizing false positives.\n\n## Integration\n\nThis skill can be used in conjunction with other data analysis and visualization tools to provide a more comprehensive understanding of the data and the identified anomalies. It can also be integrated with alerting systems to automatically notify users when anomalies are detected.\n",{"name":5,"description":7},"main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002Fjeremylongshore\u002Fclaude-code-plugins-plus-skills\u002Fanomaly-detection-system",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 22:07:19",{"source":8,"skillName":5,"commands":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add ComeOnOliver\u002Fskillshub --skill detecting-data-anomalies",{"tool":69,"command":70},"bunx","bunx skills add ComeOnOliver\u002Fskillshub --skill detecting-data-anomalies",{"tool":72,"command":73},"pnpm","pnpm dlx skills add ComeOnOliver\u002Fskillshub --skill detecting-data-anomalies","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fjeremylongshore\u002Fclaude-code-plugins-plus-skills\u002Fanomaly-detection-system\u002FSKILL.md",true,[],[78,88,98,107,113,120],{"skillNo":79,"name":80,"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":81,"categories":82,"keywords":84,"publishedAt":49},"01M6FKJN13GMG4KY57J3QD8GY5","model-hierarchy-skill","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzscole\u002Fmodel-hierarchy-skill\u002Fmodel-hierarchy-skill\u002FSKILL.md",[83],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[85,86,87],"model","model hierarchy","model hierarchy skill",{"skillNo":89,"name":90,"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":91,"categories":92,"keywords":94,"publishedAt":49},"01M6FKJJJ6GCAMR4PPPRD12M5X","zoom-mcp","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-mcp\u002FSKILL.md",[93],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[95,96,97],"MCP","zoom","zoom mcp",{"skillNo":99,"name":100,"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":101,"categories":102,"keywords":103,"publishedAt":49},"01M6FKJE3KYT3KPEZMEEHYKA6K","zoom-apps-sdk","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fzoom-apps-sdk\u002FSKILL.md",[],[104,105,106,96],"sdk","zoom apps sdk","apps",{"skillNo":108,"name":109,"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":110,"categories":111,"keywords":112,"publishedAt":49},"01M6FKJ9EDH9Q5VWDF8HAJRSP0","windows","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwindows\u002FSKILL.md",[],[109],{"skillNo":114,"name":115,"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":116,"categories":117,"keywords":119,"publishedAt":49},"01M6FKJ666BEEFC3AE9GJ72Q93","whiteboard","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwhiteboard\u002FSKILL.md",[118],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[115],{"skillNo":121,"name":122,"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":123,"categories":124,"keywords":125,"publishedAt":49},"01M6FKJ21WFCDGTJQ2Q91T9DVW","websockets","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fzoom\u002Fskills\u002Fwebsockets\u002FSKILL.md",[],[122]]