[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71P2VDC503RCFWQ06XWT77B":3,"skill-same:01M71P2VDC503RCFWQ06XWT77B":111},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":15,"sourceHtmlUrl":16,"categories":17,"keywords":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":61,"commitSha":62,"skillDirectory":63,"forksCount":64,"repositoryHtmlUrl":65,"repositoryHomepageUrl":66,"ownerHtmlUrl":67,"ownerBlogUrl":68,"ownerDescription":6,"updatedAt":69,"install":70,"resources":83},"01M71P2VDC503RCFWQ06XWT77B","sre-engineer",null,"Use when producing SRE artifacts: SLI\u002FSLO definitions, error budget policies, golden-signal alerts, incident runbooks, capacity models, chaos tests. Includes blameless postmortems and toil-reduction automation. For the org role persona see sre.","monoes\u002Fmonomind","monoes","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F276744696?v=4","01M71KWF37AJ2NJBP3KWBVSDGT","01M71KWF4CSF8ADH0M6HA09VD2",23,"MIT","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fsre-engineer\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fsre-engineer\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},"01JBSEEDCAT000000000000040","财务","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],"sre engineer","engineer","sre","tests","signal","for","observability","golden","org","Budget","alerts","when","persona","runbooks","incident","claude-skills","reliability","sli","error","slo","2026-11-10 10:56:23",false,"---\nname: sre-engineer\ndescription: \"Use when producing SRE artifacts: SLI\u002FSLO definitions, error budget policies, golden-signal alerts, incident runbooks, capacity models, chaos tests. Includes blameless postmortems and toil-reduction automation. For the org role persona see sre.\"\ntags: [\"devops\",\"operations\",\"reliability\",\"observability\",\"incident\"]\ntools: []\nlicense: MIT\nsource: https:\u002F\u002Fgithub.com\u002FJeffallan\u002Fclaude-skills\nsource_path: \"skills\u002Fsre-engineer\"\nsource_commit: 882ef55e377dbf9a4dbe496bb41ac6ccd0e555cf\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\n[Documentation](https:\u002F\u002Fjeffallan.github.io\u002Fclaude-skills\u002Fskills\u002Fdevops\u002Fsre-engineer\u002F)\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\n[Documentation](https:\u002F\u002Fjeffallan.github.io\u002Fclaude-skills\u002Fskills\u002Fdevops\u002Fsre-engineer\u002F)\n",{"name":5,"tags":54,"tools":57,"source":58,"license":14,"description":7,"source_path":59,"source_commit":60},[55,56,45,35,43],"devops","operations",[],"https:\u002F\u002Fgithub.com\u002FJeffallan\u002Fclaude-skills","skills\u002Fsre-engineer","882ef55e377dbf9a4dbe496bb41ac6ccd0e555cf","main","f7cd4595d0f8959822163e14f9787117765c931a","packages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fsre-engineer",2,"https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind","https:\u002F\u002Fmonoes.github.io\u002Fmonomind\u002F","https:\u002F\u002Fgithub.com\u002Fmonoes","https:\u002F\u002Fmonoes.me","2026-11-10 01:53:48",{"source":8,"skillName":5,"commands":71,"prompt":81,"zipAvailable":82},[72,75,78],{"tool":73,"command":74},"npx","npx skills add monoes\u002Fmonomind --skill sre-engineer",{"tool":76,"command":77},"bunx","bunx skills add monoes\u002Fmonomind --skill sre-engineer",{"tool":79,"command":80},"pnpm","pnpm dlx skills add monoes\u002Fmonomind --skill sre-engineer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fsre-engineer\u002FSKILL.md",true,[84,89,95,99,103,107],{"path":85,"resourceType":86,"resourceTypeDesc":87,"sizeBytes":88,"sourceHtmlUrl":15},"LICENSE.txt","LICENSE","许可证",1056,{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fautomation-toil.md","REFERENCE","参考文档",14820,"https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fsre-engineer\u002Freferences\u002Fautomation-toil.md",{"path":96,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":97,"sourceHtmlUrl":98},"references\u002Ferror-budget-policy.md",9852,"https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fsre-engineer\u002Freferences\u002Ferror-budget-policy.md",{"path":100,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":101,"sourceHtmlUrl":102},"references\u002Fincident-chaos.md",16791,"https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fsre-engineer\u002Freferences\u002Fincident-chaos.md",{"path":104,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":105,"sourceHtmlUrl":106},"references\u002Fmonitoring-alerting.md",11553,"https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fsre-engineer\u002Freferences\u002Fmonitoring-alerting.md",{"path":108,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":109,"sourceHtmlUrl":110},"references\u002Fslo-sli-management.md",6774,"https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fsre-engineer\u002Freferences\u002Fslo-sli-management.md",[112,139,170,197,221,245],{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":116,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":138},"01M71PB3SHWKMNJM6MD65VX8D6","zk-steward","Org role guidance for a ZK steward: engineer zero-knowledge proof systems with circuit design, SNARK vs STARK choice and security review. This is zero-knowledge cryptography, not Zettelkasten note-taking.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmonoes\u002Fmonomind\u002Fblob\u002Fmain\u002Fpackages\u002F@monomind\u002Fcli\u002Forg-skills\u002Fzk-steward\u002FSKILL.md",[],[120,121,122,123,30,124,125,126,127,128,129,130,131,132,133,134,135,136,37,137],"zk","zk steward","steward","zero-knowledge","choice","design","knowledge","impact","query","snark","review","monomind","security review","stark","zettelkasten","cryptography","security","blockchain","2026-11-10 13:02:02",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":143,"sourceHtmlUrl":144,"categories":145,"keywords":152,"publishedAt":138},"01M71PB18SDG28WBKPKJNB814K","writing-plans","Use when you have a spec or requirements for a multi-step coding task and must write an implementation plan before touching code. 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