[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73GR9CFAT9SHXM7BWTHQGQZ":3,"skill-same:01M73GR9CFAT9SHXM7BWTHQGQZ":75},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":44,"repositoryHtmlUrl":45,"repositoryHomepageUrl":46,"ownerHtmlUrl":47,"ownerBlogUrl":48,"ownerDescription":49,"updatedAt":50,"install":51,"resources":64},"01M73GR9CFAT9SHXM7BWTHQGQZ","data-report",null,"把 CSV\u002FExcel\u002FJSON 数据转成漂亮的可视化报告页","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Ftools\u002Fyana-web\u002Fhtml-skills\u002Fdata-report\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: data-report\nzh_name: \"数据可视化报告\"\nen_name: \"Data Visualization Report\"\nemoji: \"📊\"\ndescription: \"把 CSV\u002FExcel\u002FJSON 数据转成漂亮的可视化报告页\"\ncategory: data\nscenario: finance\naspect_hint: \"桌面长页面\"\nfeatured: 10\ntags: [\"data\", \"report\", \"chart\", \"数据\", \"报告\"]\nexample_id: sample-data-weekly-report\nexample_name: \"数据报告 · 周报\"\nexample_format: csv\nexample_tagline: \"KPI 卡 + Chart.js 图表 + 表格\"\nexample_desc: \"9 个月增长数据自动渲染成可视化报告, 内联 Chart.js\"\n---\n\n【模板: 数据可视化报告】\n- 头部: 报告标题 + 时间区间 + 数据来源说明。\n- KPI 卡片网格: 3-5 个最重要指标, 每个卡片显示数值 + 同比变化 + 微型趋势线。\n- 主图表区: 至少 2 个图表 (柱状 \u002F 折线 \u002F 饼 \u002F 散点), 使用 Chart.js 或 ECharts (jsdelivr CDN 引入), 数据从用户输入解析得到。\n- **图表容器必须有固定高度**: 每个 `\u003Ccanvas>` 外层包一个 `\u003Cdiv style=\"position:relative;height:NNNpx\">` (KPI 迷你图 ~40px, 主图表 ~240–280px)。Chart.js 用 `responsive:true, maintainAspectRatio:false` 时若父容器没有显式高度, 会陷入 ResizeObserver 死循环, 图表无限增高直至卡死浏览器。**绝对不要**直接给 canvas 写 `height=` 属性当布局, 那个只是初始值。\n- 数据表格: 用户原始数据节选, 使用 `\u003Ctable>` + 现代化样式 (zebra stripe, hover, sticky header)。\n- 洞察块: 3-5 条文字洞察, 用 emoji 开头, 像产品周报。\n- 底部\"方法论\"折叠区。\n- 配色克制专业: 主色 1 + 中性色阶, 图表用调色板。\n- **必须解析用户提供的实际数据**, 不要捏造。\n","【模板: 数据可视化报告】\n- 头部: 报告标题 + 时间区间 + 数据来源说明。\n- KPI 卡片网格: 3-5 个最重要指标, 每个卡片显示数值 + 同比变化 + 微型趋势线。\n- 主图表区: 至少 2 个图表 (柱状 \u002F 折线 \u002F 饼 \u002F 散点), 使用 Chart.js 或 ECharts (jsdelivr CDN 引入), 数据从用户输入解析得到。\n- **图表容器必须有固定高度**: 每个 `\u003Ccanvas>` 外层包一个 `\u003Cdiv style=\"position:relative;height:NNNpx\">` (KPI 迷你图 ~40px, 主图表 ~240–280px)。Chart.js 用 `responsive:true, maintainAspectRatio:false` 时若父容器没有显式高度, 会陷入 ResizeObserver 死循环, 图表无限增高直至卡死浏览器。**绝对不要**直接给 canvas 写 `height=` 属性当布局, 那个只是初始值。\n- 数据表格: 用户原始数据节选, 使用 `\u003Ctable>` + 现代化样式 (zebra stripe, hover, sticky header)。\n- 洞察块: 3-5 条文字洞察, 用 emoji 开头, 像产品周报。\n- 底部\"方法论\"折叠区。\n- 配色克制专业: 主色 1 + 中性色阶, 图表用调色板。\n- **必须解析用户提供的实际数据**, 不要捏造。\n",{"name":5,"tags":24,"emoji":30,"en_name":31,"zh_name":32,"category":25,"featured":33,"scenario":34,"example_id":35,"aspect_hint":36,"description":7,"example_desc":37,"example_name":38,"example_format":39,"example_tagline":40},[25,26,27,28,29],"data","report","chart","数据","报告","📊","Data Visualization Report","数据可视化报告",10,"finance","sample-data-weekly-report","桌面长页面","9 个月增长数据自动渲染成可视化报告, 内联 Chart.js","数据报告 · 周报","csv","KPI 卡 + Chart.js 图表 + 表格","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","tools\u002Fyana-web\u002Fhtml-skills\u002Fdata-report",1,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-11 23:56:26",{"source":8,"skillName":5,"commands":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill data-report",{"tool":57,"command":58},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill data-report",{"tool":60,"command":61},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill data-report","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Ftools\u002Fyana-web\u002Fhtml-skills\u002Fdata-report\u002FSKILL.md",true,[65,71],{"path":66,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":69,"sourceHtmlUrl":70},"example.html","REFERENCE","参考文档",10528,"https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Ftools\u002Fyana-web\u002Fhtml-skills\u002Fdata-report\u002Fexample.html",{"path":72,"resourceType":67,"resourceTypeDesc":68,"sizeBytes":73,"sourceHtmlUrl":74},"example.md",599,"https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Ftools\u002Fyana-web\u002Fhtml-skills\u002Fdata-report\u002Fexample.md",[76,84,91,99,107,115],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":80,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":85,"name":86,"nameZh":6,"description":87,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":90,"publishedAt":19},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":92,"name":93,"nameZh":6,"description":94,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":95,"sourceHtmlUrl":96,"categories":97,"keywords":98,"publishedAt":19},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":103,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":19},"01M73ASZ6NJRJM44Z2P84NGQ3Q","detecting-evasion-techniques-in-endpoint-logs","Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":111,"sourceHtmlUrl":112,"categories":113,"keywords":114,"publishedAt":19},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":116,"name":117,"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":118,"categories":119,"keywords":120,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]