[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6H932WMMXPEVC3AWPSMVCQB":3,"skill-same:01M6H932WMMXPEVC3AWPSMVCQB":56},{"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":27,"linkOnly":28,"markdownContent":29,"markdownBody":30,"frontMatter":31,"refName":34,"commitSha":35,"skillDirectory":36,"forksCount":14,"repositoryHtmlUrl":37,"repositoryHomepageUrl":6,"ownerHtmlUrl":38,"ownerBlogUrl":39,"ownerDescription":40,"updatedAt":41,"install":42,"resources":55},"01M6H932WMMXPEVC3AWPSMVCQB","canary",null,"Canary deployment patterns","NeuralBlitz\u002FMito","NeuralBlitz","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F79354388?v=4","01M6H8T2TFH477HRS0KTSTKR2E","David Riggs","01M6H8T2YQQDXPYM092P670Z21",0,"MIT","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fcanary\u002FSKILL.md",[],[5,19,20,21,22,23,24,25,26],"deployment","devops","opencode","engineer","patterns","canary deployment","platform","deployment patterns","2026-11-10 10:12:30",false,"---\nname: canary\ndescription: Canary deployment patterns\nlicense: MIT\ncompatibility: opencode\nmetadata:\n  audience: devops-engineer, platform-engineer\n  category: devops\n---\n\n## What I do\n\n- Implement gradual traffic shifting strategies\n- Create automated canary deployments with metric analysis\n- Design rollback automation based on health checks\n- Build observability dashboards for canary analysis\n- Integrate canary releases with CI\u002FCD pipelines\n- Configure intelligent traffic routing based on user segments\n\n## When to use me\n\n- When introducing new features to production\n- When validating infrastructure changes\n- When testing performance under real load\n- When deploying to multiple regions progressively\n- When working with A\u002FB testing alongside deployments\n- When reducing blast radius of potential issues\n\n## Key Concepts\n\n### Flagger Canary CRD\n\n```yaml\napiVersion: flagger.app\u002Fv1alpha3\nkind: Canary\nmetadata:\n  name: myapp\nspec:\n  targetRef:\n    apiVersion: apps\u002Fv1\n    kind: Deployment\n    name: myapp\n  service:\n    port: 80\n  canaryAnalysis:\n    interval: 1m\n    threshold: 10\n    maxWeight: 50\n    stepWeight: 10\n    metrics:\n      - name: request-success-rate\n        threshold: 99\n        interval: 1m\n      - name: request-duration\n        threshold: 500\n        interval: 1m\n```\n\n### Deployment Strategy\n\n- Start with small percentage (1-5%)\n- Increase gradually if metrics stay healthy\n- Stop and rollback if errors spike\n- Analyze at each stage before proceeding\n- Full rollout typically takes 30-60 minutes\n\n### Analysis Criteria\n\n- Error rate within acceptable threshold\n- Response time not degraded\n- No memory leaks or resource exhaustion\n- Business metrics not negatively impacted\n- No adverse effects on dependent services\n","## What I do\n\n- Implement gradual traffic shifting strategies\n- Create automated canary deployments with metric analysis\n- Design rollback automation based on health checks\n- Build observability dashboards for canary analysis\n- Integrate canary releases with CI\u002FCD pipelines\n- Configure intelligent traffic routing based on user segments\n\n## When to use me\n\n- When introducing new features to production\n- When validating infrastructure changes\n- When testing performance under real load\n- When deploying to multiple regions progressively\n- When working with A\u002FB testing alongside deployments\n- When reducing blast radius of potential issues\n\n## Key Concepts\n\n### Flagger Canary CRD\n\n```yaml\napiVersion: flagger.app\u002Fv1alpha3\nkind: Canary\nmetadata:\n  name: myapp\nspec:\n  targetRef:\n    apiVersion: apps\u002Fv1\n    kind: Deployment\n    name: myapp\n  service:\n    port: 80\n  canaryAnalysis:\n    interval: 1m\n    threshold: 10\n    maxWeight: 50\n    stepWeight: 10\n    metrics:\n      - name: request-success-rate\n        threshold: 99\n        interval: 1m\n      - name: request-duration\n        threshold: 500\n        interval: 1m\n```\n\n### Deployment Strategy\n\n- Start with small percentage (1-5%)\n- Increase gradually if metrics stay healthy\n- Stop and rollback if errors spike\n- Analyze at each stage before proceeding\n- Full rollout typically takes 30-60 minutes\n\n### Analysis Criteria\n\n- Error rate within acceptable threshold\n- Response time not degraded\n- No memory leaks or resource exhaustion\n- Business metrics not negatively impacted\n- No adverse effects on dependent services\n",{"name":5,"license":15,"metadata":32,"description":7,"compatibility":21},{"audience":33,"category":20},"devops-engineer, platform-engineer","main","2ea764e153842704d24a2e9aa4170441a7f03081",".opencode\u002Fskills\u002Fcanary","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito","https:\u002F\u002Fgithub.com\u002FNeuralBlitz","nuralnexus.org","Theoretical and Futuristic AI Nerd 🤓 ","2026-04-21 14:37:05",{"source":8,"skillName":5,"commands":43,"prompt":53,"zipAvailable":54},[44,47,50],{"tool":45,"command":46},"npx","npx skills add NeuralBlitz\u002FMito --skill canary",{"tool":48,"command":49},"bunx","bunx skills add NeuralBlitz\u002FMito --skill canary",{"tool":51,"command":52},"pnpm","pnpm dlx skills add NeuralBlitz\u002FMito --skill canary","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fcanary\u002FSKILL.md",true,[],[57,73,86,99,107,132],{"skillNo":58,"name":59,"nameZh":6,"description":60,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":61,"categories":62,"keywords":63,"publishedAt":27},"01M6HAZSAQK1S27CEZYAYJTQDX","wireless-communications","Wireless systems, mobile networks, and 5G\u002F6G","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fwireless-communications\u002FSKILL.md",[],[64,65,66,67,68,69,70,71,21,72],"communications","wireless communications","wireless","systems","engineering","networks","5G","students","mobile",{"skillNo":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":77,"categories":78,"keywords":79,"publishedAt":27},"01M6HAZFX6E8NK3WZWDX5YCKYY","websockets","WebSocket implementation and best practices","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fwebassembly\u002Fwebsockets\u002FSKILL.md",[],[75,80,81,82,21,83,84,85],"websocket implementation","time","implementation","real-time","websocket","best 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engineering","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Fvlsi-design\u002FSKILL.md",[113,118],{"categoryNo":114,"categoryName":115,"taxonomyType":116,"taxonomyTypeDesc":117},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":119,"categoryName":120,"taxonomyType":121,"taxonomyTypeDesc":122},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[124,125,126,127,128,68,129,21,130,71,131],"vlsi design","VLSI","design","hardware","integration","computer","scale","circuit",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":138,"publishedAt":27},"01M6HAYPQ30FFE2JXD7WQJ8R6C","unsupervised","Unsupervised learning 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