[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6RCFS7DDYVTVT52SJD19GWT":3,"skill-same:01M6RCFS7DDYVTVT52SJD19GWT":107},{"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":31,"publishedAt":52,"linkOnly":53,"markdownContent":54,"markdownBody":55,"frontMatter":56,"refName":63,"commitSha":64,"skillDirectory":65,"forksCount":66,"repositoryHtmlUrl":67,"repositoryHomepageUrl":6,"ownerHtmlUrl":68,"ownerBlogUrl":69,"ownerDescription":6,"updatedAt":70,"install":71,"resources":84},"01M6RCFS7DDYVTVT52SJD19GWT","sre-engineer",null,"Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations and automation scripts for production systems. Use when defining SLIs\u002FSLOs, managing error budgets, building reliable systems at scale, incident management, chaos engineering, toil reduction, or capacity planning.","PIXARTSeu\u002FSynapse","PIXARTSeu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F274446381?v=4","01M6RB4XPN9YBZCRY4FVN9PJCT","Pixarts ","01M6RB4XTF8G9KHQEMC21FGDKD",12,"MIT","https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fsre-engineer\u002FSKILL.md",[18,23,28],{"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},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",{"categoryNo":29,"categoryName":30,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000040","财务",[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51],"sre engineer","sre","engineer","implementation","specialist","for","at","scale","systems","capacity planning","call","models","github","reliability","devops","use","chaos engineering","Budget","mttr","monitoring","2026-11-10 10:12:30",false,"---\nname: sre-engineer\ndescription: Defines service level objectives, creates error budget policies, designs incident response procedures, develops capacity models, and produces monitoring configurations and automation scripts for production systems. Use when defining SLIs\u002FSLOs, managing error budgets, building reliable systems at scale, incident management, chaos engineering, toil reduction, or capacity planning.\nlicense: MIT\nmetadata:\n  author: https:\u002F\u002Fgithub.com\u002FJeffallan\n  version: \"1.1.0\"\n  domain: devops\n  triggers: SRE, site reliability, SLO, SLI, error budget, incident management, chaos engineering, toil reduction, on-call, MTTR\n  role: specialist\n  scope: implementation\n  output-format: code\n  related-skills: devops-engineer, cloud-architect, kubernetes-specialist\n---\n\n# SRE Engineer\n\n## Core Workflow\n\n1. **Assess reliability** - Review architecture, SLOs, incidents, toil levels\n2. **Define SLOs** - Identify meaningful SLIs and set appropriate targets\n3. **Verify alignment** - Confirm SLO targets reflect user expectations before proceeding\n4. **Implement monitoring** - Build golden signal dashboards and alerting\n5. **Automate toil** - Identify repetitive tasks and build automation\n6. **Test resilience** - Design and execute chaos experiments; verify recovery meets RTO\u002FRPO targets before marking the experiment complete; validate recovery behavior end-to-end\n\n## Reference Guide\n\nLoad detailed guidance based on context:\n\n| Topic | Reference | Load When |\n|-------|-----------|-----------|\n| SLO\u002FSLI | `references\u002Fslo-sli-management.md` | Defining SLOs, calculating error budgets |\n| Error Budgets | `references\u002Ferror-budget-policy.md` | Managing budgets, burn rates, policies |\n| Monitoring | `references\u002Fmonitoring-alerting.md` | Golden signals, alert design, dashboards |\n| Automation | `references\u002Fautomation-toil.md` | Toil reduction, automation patterns |\n| Incidents | `references\u002Fincident-chaos.md` | Incident response, chaos engineering |\n\n## Constraints\n\n### MUST DO\n- Define quantitative SLOs (e.g., 99.9% availability)\n- Calculate error budgets from SLO targets\n- Monitor golden signals (latency, traffic, errors, saturation)\n- Write blameless postmortems for all incidents\n- Measure toil and track reduction progress\n- Automate repetitive operational tasks\n- Test failure scenarios with chaos engineering\n- Balance reliability with feature velocity\n\n### MUST NOT DO\n- Set SLOs without user impact justification\n- Alert on symptoms without actionable runbooks\n- Tolerate >50% toil without automation plan\n- Skip postmortems or assign blame\n- Implement manual processes for recurring tasks\n- Deploy without capacity planning\n- Ignore error budget exhaustion\n- Build systems that can't degrade gracefully\n\n## Output Templates\n\nWhen implementing SRE practices, provide:\n1. SLO definitions with SLI measurements and targets\n2. Monitoring\u002Falerting configuration (Prometheus, etc.)\n3. Automation scripts (Python, Go, Terraform)\n4. Runbooks with clear remediation steps\n5. Brief explanation of reliability impact\n\n## Concrete Examples\n\n### SLO Definition & Error Budget Calculation\n\n```\n# 99.9% availability SLO over a 30-day window\n# Allowed downtime: (1 - 0.999) * 30 * 24 * 60 = 43.2 minutes\u002Fmonth\n# Error budget (request-based): 0.001 * total_requests\n\n# Example: 10M requests\u002Fmonth → 10,000 error budget requests\n# If 5,000 errors consumed in week 1 → 50% budget burned in 25% of window\n# → Trigger error budget policy: freeze non-critical releases\n```\n\n### Prometheus SLO Alerting Rule (Multiwindow Burn Rate)\n\n```yaml\ngroups:\n  - name: slo_availability\n    rules:\n      # Fast burn: 2% budget in 1h (14.4x burn rate)\n      - alert: HighErrorBudgetBurn\n        expr: |\n          (\n            sum(rate(http_requests_total{status=~\"5..\"}[1h]))\n            \u002F\n            sum(rate(http_requests_total[1h]))\n          ) > 0.014400\n          and\n          (\n            sum(rate(http_requests_total{status=~\"5..\"}[5m]))\n            \u002F\n            sum(rate(http_requests_total[5m]))\n          ) > 0.014400\n        for: 2m\n        labels:\n          severity: critical\n        annotations:\n          summary: \"High error budget burn rate detected\"\n          runbook: \"https:\u002F\u002Fwiki.internal\u002Frunbooks\u002Fhigh-error-burn\"\n\n      # Slow burn: 5% budget in 6h (1x burn rate sustained)\n      - alert: SlowErrorBudgetBurn\n        expr: |\n          (\n            sum(rate(http_requests_total{status=~\"5..\"}[6h]))\n            \u002F\n            sum(rate(http_requests_total[6h]))\n          ) > 0.001\n        for: 15m\n        labels:\n          severity: warning\n        annotations:\n          summary: \"Sustained error budget consumption\"\n          runbook: \"https:\u002F\u002Fwiki.internal\u002Frunbooks\u002Fslow-error-burn\"\n```\n\n### PromQL Golden Signal Queries\n\n```promql\n# Latency — 99th percentile request duration\nhistogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))\n\n# Traffic — requests per second by service\nsum(rate(http_requests_total[5m])) by (service)\n\n# Errors — error rate ratio\nsum(rate(http_requests_total{status=~\"5..\"}[5m])) by (service)\n  \u002F\nsum(rate(http_requests_total[5m])) by (service)\n\n# Saturation — CPU throttling ratio\nsum(rate(container_cpu_cfs_throttled_seconds_total[5m])) by (pod)\n  \u002F\nsum(rate(container_cpu_cfs_periods_total[5m])) by (pod)\n```\n\n### Toil Automation Script (Python)\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"Auto-remediation: restart pods exceeding error threshold.\"\"\"\nimport subprocess, sys, json\n\nERROR_THRESHOLD = 0.05  # 5% error rate triggers restart\n\ndef get_error_rate(service: str) -> float:\n    \"\"\"Query Prometheus for current error rate.\"\"\"\n    import urllib.request\n    query = f'sum(rate(http_requests_total{{status=~\"5..\",service=\"{service}\"}}[5m])) \u002F sum(rate(http_requests_total{{service=\"{service}\"}}[5m]))'\n    url = f\"http:\u002F\u002Fprometheus:9090\u002Fapi\u002Fv1\u002Fquery?query={urllib.request.quote(query)}\"\n    with urllib.request.urlopen(url) as resp:\n        data = json.load(resp)\n    results = data[\"data\"][\"result\"]\n    return float(results[0][\"value\"][1]) if results else 0.0\n\ndef restart_deployment(namespace: str, deployment: str) -> None:\n    subprocess.run(\n        [\"kubectl\", \"rollout\", \"restart\", f\"deployment\u002F{deployment}\", \"-n\", namespace],\n        check=True\n    )\n    print(f\"Restarted {namespace}\u002F{deployment}\")\n\nif __name__ == \"__main__\":\n    service, namespace, deployment = sys.argv[1], sys.argv[2], sys.argv[3]\n    rate = get_error_rate(service)\n    print(f\"Error rate for {service}: {rate:.2%}\")\n    if rate > ERROR_THRESHOLD:\n        restart_deployment(namespace, deployment)\n    else:\n        print(\"Within SLO threshold — no action required\")\n```\n","# SRE Engineer\n\n## Core Workflow\n\n1. **Assess reliability** - Review architecture, SLOs, incidents, toil levels\n2. **Define SLOs** - Identify meaningful SLIs and set appropriate targets\n3. **Verify alignment** - Confirm SLO targets reflect user expectations before proceeding\n4. **Implement monitoring** - Build golden signal dashboards and alerting\n5. **Automate toil** - Identify repetitive tasks and build automation\n6. **Test resilience** - Design and execute chaos experiments; verify recovery meets RTO\u002FRPO targets before marking the experiment complete; validate recovery behavior end-to-end\n\n## Reference Guide\n\nLoad detailed guidance based on context:\n\n| Topic | Reference | Load When |\n|-------|-----------|-----------|\n| SLO\u002FSLI | `references\u002Fslo-sli-management.md` | Defining SLOs, calculating error budgets |\n| Error Budgets | `references\u002Ferror-budget-policy.md` | Managing budgets, burn rates, policies |\n| Monitoring | `references\u002Fmonitoring-alerting.md` | Golden signals, alert design, dashboards |\n| Automation | `references\u002Fautomation-toil.md` | Toil reduction, automation patterns |\n| Incidents | `references\u002Fincident-chaos.md` | Incident response, chaos engineering |\n\n## Constraints\n\n### MUST DO\n- Define quantitative SLOs (e.g., 99.9% availability)\n- Calculate error budgets from SLO targets\n- Monitor golden signals (latency, traffic, errors, saturation)\n- Write blameless postmortems for all incidents\n- Measure toil and track reduction progress\n- Automate repetitive operational tasks\n- Test failure scenarios with chaos engineering\n- Balance reliability with feature velocity\n\n### MUST NOT DO\n- Set SLOs without user impact justification\n- Alert on symptoms without actionable runbooks\n- Tolerate >50% toil without automation plan\n- Skip postmortems or assign blame\n- Implement manual processes for recurring tasks\n- Deploy without capacity planning\n- Ignore error budget exhaustion\n- Build systems that can't degrade gracefully\n\n## Output Templates\n\nWhen implementing SRE practices, provide:\n1. SLO definitions with SLI measurements and targets\n2. Monitoring\u002Falerting configuration (Prometheus, etc.)\n3. Automation scripts (Python, Go, Terraform)\n4. Runbooks with clear remediation steps\n5. Brief explanation of reliability impact\n\n## Concrete Examples\n\n### SLO Definition & Error Budget Calculation\n\n```\n# 99.9% availability SLO over a 30-day window\n# Allowed downtime: (1 - 0.999) * 30 * 24 * 60 = 43.2 minutes\u002Fmonth\n# Error budget (request-based): 0.001 * total_requests\n\n# Example: 10M requests\u002Fmonth → 10,000 error budget requests\n# If 5,000 errors consumed in week 1 → 50% budget burned in 25% of window\n# → Trigger error budget policy: freeze non-critical releases\n```\n\n### Prometheus SLO Alerting Rule (Multiwindow Burn Rate)\n\n```yaml\ngroups:\n  - name: slo_availability\n    rules:\n      # Fast burn: 2% budget in 1h (14.4x burn rate)\n      - alert: HighErrorBudgetBurn\n        expr: |\n          (\n            sum(rate(http_requests_total{status=~\"5..\"}[1h]))\n            \u002F\n            sum(rate(http_requests_total[1h]))\n          ) > 0.014400\n          and\n          (\n            sum(rate(http_requests_total{status=~\"5..\"}[5m]))\n            \u002F\n            sum(rate(http_requests_total[5m]))\n          ) > 0.014400\n        for: 2m\n        labels:\n          severity: critical\n        annotations:\n          summary: \"High error budget burn rate detected\"\n          runbook: \"https:\u002F\u002Fwiki.internal\u002Frunbooks\u002Fhigh-error-burn\"\n\n      # Slow burn: 5% budget in 6h (1x burn rate sustained)\n      - alert: SlowErrorBudgetBurn\n        expr: |\n          (\n            sum(rate(http_requests_total{status=~\"5..\"}[6h]))\n            \u002F\n            sum(rate(http_requests_total[6h]))\n          ) > 0.001\n        for: 15m\n        labels:\n          severity: warning\n        annotations:\n          summary: \"Sustained error budget consumption\"\n          runbook: \"https:\u002F\u002Fwiki.internal\u002Frunbooks\u002Fslow-error-burn\"\n```\n\n### PromQL Golden Signal Queries\n\n```promql\n# Latency — 99th percentile request duration\nhistogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))\n\n# Traffic — requests per second by service\nsum(rate(http_requests_total[5m])) by (service)\n\n# Errors — error rate ratio\nsum(rate(http_requests_total{status=~\"5..\"}[5m])) by (service)\n  \u002F\nsum(rate(http_requests_total[5m])) by (service)\n\n# Saturation — CPU throttling ratio\nsum(rate(container_cpu_cfs_throttled_seconds_total[5m])) by (pod)\n  \u002F\nsum(rate(container_cpu_cfs_periods_total[5m])) by (pod)\n```\n\n### Toil Automation Script (Python)\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"Auto-remediation: restart pods exceeding error threshold.\"\"\"\nimport subprocess, sys, json\n\nERROR_THRESHOLD = 0.05  # 5% error rate triggers restart\n\ndef get_error_rate(service: str) -> float:\n    \"\"\"Query Prometheus for current error rate.\"\"\"\n    import urllib.request\n    query = f'sum(rate(http_requests_total{{status=~\"5..\",service=\"{service}\"}}[5m])) \u002F sum(rate(http_requests_total{{service=\"{service}\"}}[5m]))'\n    url = f\"http:\u002F\u002Fprometheus:9090\u002Fapi\u002Fv1\u002Fquery?query={urllib.request.quote(query)}\"\n    with urllib.request.urlopen(url) as resp:\n        data = json.load(resp)\n    results = data[\"data\"][\"result\"]\n    return float(results[0][\"value\"][1]) if results else 0.0\n\ndef restart_deployment(namespace: str, deployment: str) -> None:\n    subprocess.run(\n        [\"kubectl\", \"rollout\", \"restart\", f\"deployment\u002F{deployment}\", \"-n\", namespace],\n        check=True\n    )\n    print(f\"Restarted {namespace}\u002F{deployment}\")\n\nif __name__ == \"__main__\":\n    service, namespace, deployment = sys.argv[1], sys.argv[2], sys.argv[3]\n    rate = get_error_rate(service)\n    print(f\"Error rate for {service}: {rate:.2%}\")\n    if rate > ERROR_THRESHOLD:\n        restart_deployment(namespace, deployment)\n    else:\n        print(\"Within SLO threshold — no action required\")\n```\n",{"name":5,"license":15,"metadata":57,"description":7},{"role":36,"scope":35,"author":58,"domain":46,"version":59,"triggers":60,"output-format":61,"related-skills":62},"https:\u002F\u002Fgithub.com\u002FJeffallan","1.1.0","SRE, site reliability, SLO, SLI, error budget, incident management, chaos engineering, toil reduction, on-call, MTTR","code","devops-engineer, cloud-architect, kubernetes-specialist","main","c19c0bc0a04d25a849ce749fe88250e688f06437",".agents\u002Fskills\u002Fsre-engineer",1,"https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse","https:\u002F\u002Fgithub.com\u002FPIXARTSeu","https:\u002F\u002Fpixarts.eu\u002F","2026-09-24 06:30:45",{"source":8,"skillName":5,"commands":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add PIXARTSeu\u002FSynapse --skill sre-engineer",{"tool":77,"command":78},"bunx","bunx skills add PIXARTSeu\u002FSynapse --skill sre-engineer",{"tool":80,"command":81},"pnpm","pnpm dlx skills add PIXARTSeu\u002FSynapse --skill sre-engineer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fsre-engineer\u002FSKILL.md",true,[85,91,95,99,103],{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fautomation-toil.md","REFERENCE","参考文档",14820,"https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fsre-engineer\u002Freferences\u002Fautomation-toil.md",{"path":92,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Ferror-budget-policy.md",9852,"https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fsre-engineer\u002Freferences\u002Ferror-budget-policy.md",{"path":96,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Fincident-chaos.md",16791,"https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fsre-engineer\u002Freferences\u002Fincident-chaos.md",{"path":100,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fmonitoring-alerting.md",11553,"https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fsre-engineer\u002Freferences\u002Fmonitoring-alerting.md",{"path":104,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fslo-sli-management.md",6774,"https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fsre-engineer\u002Freferences\u002Fslo-sli-management.md",[108,138,145,172,194,220],{"skillNo":109,"name":110,"nameZh":6,"description":111,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":112,"categories":113,"keywords":118,"publishedAt":52},"01M6X0ECZKGP44NP2JW588B9RG","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002Fskills\u002Fvercel-react-best-practices\u002FSKILL.md",[114,117],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[119,120,121,122,123,124,125,126,127,128,129,61,130,131,132,133,134,135,136,137],"react","vercel react best practices","best practices","react best practices","vercel","writing","performance","components","react components","guidelines","tasks","Optimization","data","refactoring","patterns","next","triggers","data fetching","when",{"skillNo":139,"name":110,"nameZh":6,"description":111,"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":144,"publishedAt":52},"01M6X05WCVH879XKZEAQ15PHQX","https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002Fpackages\u002Fcodegraph\u002Fdata\u002Fskill\u002Fvercel-react-best-practices\u002FSKILL.md",[142,143],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[119,120,121,122,123,124,125,126,127,128,129,61,130,131,132,133,134,135,136,137],{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":155,"publishedAt":52},"01M6WZ286AR2SYE9GZYVW0CNYG","wordpress-pro","Develops custom WordPress themes and plugins, creates and registers Gutenberg blocks and block patterns, configures WooCommerce stores, implements WordPress REST API endpoints, applies security hardening (nonces, sanitization, escaping, capability checks), and optimizes performance through caching and query tuning. Use when building WordPress themes, writing plugins, customizing Gutenberg blocks, extending WooCommerce, working with ACF, using the WordPress REST API, applying hooks and filters, or improving WordPress performance and security.","https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002Fpackages\u002Fcodegraph\u002Fdata\u002Flifecycle-skills\u002Fwordpress-pro\u002FSKILL.md",[151,154],{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[156,157,158,124,159,125,61,160,161,162,163,164,165,166,167,168,169,170,171,137],"wordpress","wordpress pro","pro","security","tuning","api","laravel","reviewer","hooks","filters","expert","query","woocommerce","caching","themes","sanitization",{"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":52},"01M6WZ1ZVG6PJ4PVN9RTK0BX7Z","websocket-engineer","Use when building real-time communication systems with WebSockets or Socket.IO. Invoke for bidirectional messaging, horizontal scaling with Redis, presence tracking, room management.","https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002Fpackages\u002Fcodegraph\u002Fdata\u002Flifecycle-skills\u002Fwebsocket-engineer\u002FSKILL.md",[178,179],{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[181,34,182,183,61,184,51,161,163,185,166,186,187,188,189,190,191,192,193,137],"websocket","websocket engineer","architecture","messaging","fastapi","time","scaling","io","communication systems","presence","updates","socket","invoke",{"skillNo":195,"name":196,"nameZh":6,"description":197,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":198,"categories":199,"keywords":203,"publishedAt":52},"01M6WZ1G86ZR73H0WNM6CGNRGF","vue-expert","Builds Vue 3 components with Composition API patterns, configures Nuxt 3 SSR\u002FSSG projects, sets up Pinia stores, scaffolds Quasar\u002FCapacitor mobile apps, implements PWA features, and optimises Vite builds. Use when creating Vue 3 applications with Composition API, writing reusable composables, managing state with Pinia, building hybrid mobile apps with Quasar or Capacitor, configuring service workers, or tuning Vite configuration and TypeScript integration.","https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002Fpackages\u002Fcodegraph\u002Fdata\u002Flifecycle-skills\u002Fvue-expert\u002FSKILL.md",[200,201,202],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[204,166,205,206,124,207,126,208,209,210,211,212,47,213,214,215,216,217,218,219],"vue","vue expert","frontend","typescript","config","applications","Composition","projects","pinia","router","workers","mobile","reusable","worker","apps","hybrid",{"skillNo":221,"name":222,"nameZh":6,"description":223,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":224,"categories":225,"keywords":228,"publishedAt":52},"01M6WZ1AP3GFCBAMD712X8KRP7","vue-expert-js","Creates Vue 3 components, builds vanilla JS composables, configures Vite projects, and sets up routing and state management using JavaScript only — no TypeScript. Generates JSDoc-typed code with @typedef, @param, and @returns annotations for full type coverage without a TS compiler. Use when building Vue 3 applications with JavaScript only (no TypeScript), when projects require JSDoc-based type hints, when migrating from Vue 2 Options API to Composition API in JS, or when teams prefer vanilla JavaScript, .mjs modules, or need quick prototypes without TypeScript setup.","https:\u002F\u002Fgithub.com\u002FPIXARTSeu\u002FSynapse\u002Fblob\u002Fmain\u002Fpackages\u002Fcodegraph\u002Fdata\u002Flifecycle-skills\u002Fvue-expert-js\u002FSKILL.md",[226,227],{"categoryNo":115,"categoryName":116,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[204,229,166,230,205,206,207,126,61,161,210,211,231,232,233,234,235,236,237,137],"js","vue expert js","coverage","only","up","modules","options","jsdoc","quick"]