[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CY15PYWJPM2R3N3FZR2ABY":3,"skill-same:01M6CY15PYWJPM2R3N3FZR2ABY":111},{"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":65,"commitSha":66,"skillDirectory":67,"forksCount":68,"repositoryHtmlUrl":69,"repositoryHomepageUrl":70,"ownerHtmlUrl":71,"ownerBlogUrl":72,"ownerDescription":73,"updatedAt":74,"install":75,"resources":88},"01M6CY15PYWJPM2R3N3FZR2ABY","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.","eric861129\u002FSKILLS_All-in-one","eric861129","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F74950944?v=4","01M6CXHDYEYRPFGGV4EFXCSYCF","ChiYu-Huang","01M6CXHE2J5CN1EBTY0FKV3ZKW",52,"MIT","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FDevelopment & Code Tools\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","sre engineer","engineer","reliability","incident response","site reliability","incident","capacity planning","call","code","sli","slo","planning","kubernetes","use","chaos engineering","architect","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":58,"scope":59,"author":60,"domain":61,"version":62,"triggers":63,"output-format":41,"related-skills":64},"specialist","implementation","https:\u002F\u002Fgithub.com\u002FJeffallan","devops","1.1.0","SRE, site reliability, SLO, SLI, error budget, incident management, chaos engineering, toil reduction, on-call, MTTR","devops-engineer, cloud-architect, kubernetes-specialist","main","cbdd44aae21131521c09e42762e36178c790eacf","public\u002FSKILLS\u002FDevelopment & Code Tools\u002Fsre-engineer",13,"https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one","http:\u002F\u002Fhuangchiyu.com\u002FSKILLS_All-in-one\u002F","https:\u002F\u002Fgithub.com\u002Feric861129","https:\u002F\u002Fhuangchiyu.com\u002FChiYu-Blog\u002F","開發工程師，專注於 C#／.NET 系統現代化、AI-assisted engineering、開源工具與技術寫作。","2026-04-16 14:28:14",{"source":8,"skillName":5,"commands":76,"prompt":86,"zipAvailable":87},[77,80,83],{"tool":78,"command":79},"npx","npx skills add eric861129\u002FSKILLS_All-in-one --skill sre-engineer",{"tool":81,"command":82},"bunx","bunx skills add eric861129\u002FSKILLS_All-in-one --skill sre-engineer",{"tool":84,"command":85},"pnpm","pnpm dlx skills add eric861129\u002FSKILLS_All-in-one --skill sre-engineer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FDevelopment & Code Tools\u002Fsre-engineer\u002FSKILL.md",true,[89,95,99,103,107],{"path":90,"resourceType":91,"resourceTypeDesc":92,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fautomation-toil.md","REFERENCE","参考文档",14820,"https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FDevelopment & Code Tools\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\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FDevelopment & Code Tools\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\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FDevelopment & Code Tools\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\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FDevelopment & Code Tools\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\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FDevelopment & Code Tools\u002Fsre-engineer\u002Freferences\u002Fslo-sli-management.md",[112,139,170,195,219,239],{"skillNo":113,"name":114,"nameZh":6,"description":115,"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":52},"01M6CZ1WR3KNVTKSWH4GNWT646","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fwhat-if-oracle\u002FSKILL.md",[118],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[120,121,122,123,44,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138],"oracle","what if oracle","go","run","ux","futures","risk","analysis","explore","decision","branch","question","read","write","idea","exploration","options","stress-test","strategies",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":155,"publishedAt":52},"01M6CZ1QHGJE91A95MSN5EK5Q9","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fvenue-templates\u002FSKILL.md",[145,148,151,152],{"categoryNo":146,"categoryName":147,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":149,"categoryName":150,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":153,"categoryName":154,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[156,157,158,159,160,161,162,163,164,165,132,133,166,167,168,169],"templates","venue templates","research","guidelines","NeurIPS","ICML","LaTeX","science","Bash","access","edit","proposals","Academic","requirements",{"skillNo":171,"name":172,"nameZh":6,"description":173,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":178,"publishedAt":52},"01M6CZ1KBQD2E5RTFB3BGVX7CD","the-fool","Use when challenging ideas, plans, decisions, or proposals using structured critical reasoning. Invoke to play devil's advocate, run a pre-mortem, red team, or audit evidence and assumptions.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fthe-fool\u002FSKILL.md",[176,177],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[179,180,122,181,182,123,41,183,184,185,186,187,188,189,167,190,191,192,193,194],"the fool","review","workflow","architecture","audit","forge","devil","team","designer","reviewer","challenge","stress test","evidence","expert","poke","ideas",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":204,"publishedAt":52},"01M6CZ1ES826S7P1X3333E40TA","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fscientific-writing\u002FSKILL.md",[201,202,203],{"categoryNo":146,"categoryName":147,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":153,"categoryName":154,"taxonomyType":26,"taxonomyTypeDesc":27},[205,206,159,207,208,209,210,211,164,212,132,133,166,46,213,214,215,216,217,218],"writing","scientific writing","core","reporting","deep research","Citations","Outlines","tool","never","structure","deep","full","stage","prisma",{"skillNo":220,"name":221,"nameZh":6,"description":222,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":223,"categories":224,"keywords":226,"publishedAt":52},"01M6CZ1B2JGJTB9FRD0TW4RXXE","scientific-brainstorming","Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fscientific-brainstorming\u002FSKILL.md",[225],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[227,228,158,229,44,230,231,232,46,233,234,235,135,236,237,217,238],"brainstorming","scientific brainstorming","sessions","data","hypothesis","open","ideation","creative","do","connections","research ideation","generation",{"skillNo":240,"name":241,"nameZh":6,"description":242,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":243,"categories":244,"keywords":251,"publishedAt":52},"01M6CZ16JG8N7A2ZX04BGE8J2Y","research-lookup","Look up current research information using the Parallel Chat API (primary) or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information.","https:\u002F\u002Fgithub.com\u002Feric861129\u002FSKILLS_All-in-one\u002Fblob\u002Fmain\u002Fpublic\u002FSKILLS\u002FWriting & Research\u002Fresearch-lookup\u002FSKILL.md",[245,248,249,250],{"categoryNo":246,"categoryName":247,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":149,"categoryName":150,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[158,252,253,254,230,255,256,164,257,258,132,133,166,46,259,260,261,262,263,168],"lookup","research lookup","search","perplexity","api","parallel","openrouter","backend","up","look","queries","chat"]