[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6G1F53APF36NM0VG124M1E4":3,"skill-same:01M6G1F53APF36NM0VG124M1E4":72},{"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":23,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":14,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":49,"ownerDescription":50,"updatedAt":51,"install":52,"resources":65},"01M6G1F53APF36NM0VG124M1E4","incident-response-smart-fix",null,"[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and res","reikiplanet\u002Fdaoskills","reikiplanet","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F96377599?v=4","01M6CVWCMD740YHQDK7E9QYBW4","Reiki Planet","01M6CVWCS4KXVA2CWFVJ43EAXK",0,"MIT","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fcreative\u002Fincident-response-smart-fix\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37],"incident response","fix","incident response smart fix","incident","workflow","diagnose","debugging","observability","pipeline","Tools","sophisticated","community","thinking","resolution","2026-11-10 10:12:30",false,"---\nname: incident-response-smart-fix\ndescription: \"[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and res\"\nrisk: unknown\nsource: community\n---\n\n# Intelligent Issue Resolution with Multi-Agent Orchestration\n\n[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and resolve production issues. The intelligent debugging strategy combines automated root cause analysis with human expertise, using modern 2024\u002F2025 practices including AI code assistants (GitHub Copilot, Claude Code), observability platforms (Sentry, DataDog, OpenTelemetry), git bisect automation for regression tracking, and production-safe debugging techniques like distributed tracing and structured logging. The process follows a rigorous four-phase approach: (1) Issue Analysis Phase - error-detective and debugger agents analyze error traces, logs, reproduction steps, and observability data to understand the full context of the failure including upstream\u002Fdownstream impacts, (2) Root Cause Investigation Phase - debugger and code-reviewer agents perform deep code analysis, automated git bisect to identify introducing commit, dependency compatibility checks, and state inspection to isolate the exact failure mechanism, (3) Fix Implementation Phase - domain-specific agents (python-pro, typescript-pro, rust-expert, etc.) implement minimal fixes with comprehensive test coverage including unit, integration, and edge case tests while following production-safe practices, (4) Verification Phase - test-automator and performance-engineer agents run regression suites, performance benchmarks, security scans, and verify no new issues are introduced. Complex issues spanning multiple systems require orchestrated coordination between specialist agents (database-optimizer → performance-engineer → devops-troubleshooter) with explicit context passing and state sharing. The workflow emphasizes understanding root causes over treating symptoms, implementing lasting architectural improvements, automating detection through enhanced monitoring and alerting, and preventing future occurrences through type system enhancements, static analysis rules, and improved error handling patterns. Success is measured not just by issue resolution but by reduced mean time to recovery (MTTR), prevention of similar issues, and improved system resilience.]\n\n## Use this skill when\n\n- Working on intelligent issue resolution with multi-agent orchestration tasks or workflows\n- Needing guidance, best practices, or checklists for intelligent issue resolution with multi-agent orchestration\n\n## Do not use this skill when\n\n- The task is unrelated to intelligent issue resolution with multi-agent orchestration\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources\u002Fimplementation-playbook.md`.\n\n## Resources\n\n- `resources\u002Fimplementation-playbook.md` for detailed patterns and examples.\n","# Intelligent Issue Resolution with Multi-Agent Orchestration\n\n[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and resolve production issues. The intelligent debugging strategy combines automated root cause analysis with human expertise, using modern 2024\u002F2025 practices including AI code assistants (GitHub Copilot, Claude Code), observability platforms (Sentry, DataDog, OpenTelemetry), git bisect automation for regression tracking, and production-safe debugging techniques like distributed tracing and structured logging. The process follows a rigorous four-phase approach: (1) Issue Analysis Phase - error-detective and debugger agents analyze error traces, logs, reproduction steps, and observability data to understand the full context of the failure including upstream\u002Fdownstream impacts, (2) Root Cause Investigation Phase - debugger and code-reviewer agents perform deep code analysis, automated git bisect to identify introducing commit, dependency compatibility checks, and state inspection to isolate the exact failure mechanism, (3) Fix Implementation Phase - domain-specific agents (python-pro, typescript-pro, rust-expert, etc.) implement minimal fixes with comprehensive test coverage including unit, integration, and edge case tests while following production-safe practices, (4) Verification Phase - test-automator and performance-engineer agents run regression suites, performance benchmarks, security scans, and verify no new issues are introduced. Complex issues spanning multiple systems require orchestrated coordination between specialist agents (database-optimizer → performance-engineer → devops-troubleshooter) with explicit context passing and state sharing. The workflow emphasizes understanding root causes over treating symptoms, implementing lasting architectural improvements, automating detection through enhanced monitoring and alerting, and preventing future occurrences through type system enhancements, static analysis rules, and improved error handling patterns. Success is measured not just by issue resolution but by reduced mean time to recovery (MTTR), prevention of similar issues, and improved system resilience.]\n\n## Use this skill when\n\n- Working on intelligent issue resolution with multi-agent orchestration tasks or workflows\n- Needing guidance, best practices, or checklists for intelligent issue resolution with multi-agent orchestration\n\n## Do not use this skill when\n\n- The task is unrelated to intelligent issue resolution with multi-agent orchestration\n- You need a different domain or tool outside this scope\n\n## Instructions\n\n- Clarify goals, constraints, and required inputs.\n- Apply relevant best practices and validate outcomes.\n- Provide actionable steps and verification.\n- If detailed examples are required, open `resources\u002Fimplementation-playbook.md`.\n\n## Resources\n\n- `resources\u002Fimplementation-playbook.md` for detailed patterns and examples.\n",{"name":5,"risk":43,"source":35,"description":7},"unknown","master","dff58085da947e81e8418c922055644bf0082b1b","skills\u002Fcreative\u002Fincident-response-smart-fix","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills","https:\u002F\u002Fgithub.com\u002Freikiplanet","https:\u002F\u002Fwww.reiki-planet.com","Reiki Planet Game","2026-03-29 06:02:29",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add reikiplanet\u002Fdaoskills --skill incident-response-smart-fix",{"tool":58,"command":59},"bunx","bunx skills add reikiplanet\u002Fdaoskills --skill incident-response-smart-fix",{"tool":61,"command":62},"pnpm","pnpm dlx skills add reikiplanet\u002Fdaoskills --skill incident-response-smart-fix","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fcreative\u002Fincident-response-smart-fix\u002FSKILL.md",true,[66],{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"resources\u002Fimplementation-playbook.md","REFERENCE","参考文档",31650,"https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fcreative\u002Fincident-response-smart-fix\u002Fresources\u002Fimplementation-playbook.md",[73,99,111,126,146,169],{"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":38},"01M6G614JTEQJHPT8AKK3YQ18S","web-games","Web browser game development principles. Framework selection, WebGPU, optimization, PWA.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fweb-games\u002FSKILL.md",[],[80,81,82,83,84,85,35,86,87,88,89,90,91,92,93,94,95,96,97,98],"games","web games","web","grep","principles","selection","framework selection","edit","write","read","pwa","game","web browser","game development","Optimization","framework","browser","development","webgpu",{"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":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":38},"01M6G611H2YGV0GZFP90D38ZMJ","vr-ar","VR\u002FAR development principles. Comfort, interaction, performance requirements.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fvr-ar\u002FSKILL.md",[],[106,107,97,108,109,35,87,88,89,83,110,84],"ar","vr ar","interaction","requirements","performance",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":38},"01M6G60XNMHAMV1SV0EJ1CX0AY","templates","Project scaffolding templates for new applications. Use when creating new projects from scratch. Contains 12 templates for various tech stacks.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Ftemplates\u002FSKILL.md",[],[113,118,119,120,121,122,35,123,89,124,125,83],"project scaffolding","From Scratch","tech","project","scaffolding","use","projects","applications",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":132,"publishedAt":38},"01M6G60T0GATMK3Q5H8GDC1Z8S","wcag-audit-patterns","Conduct WCAG 2.2 accessibility audits with automated testing, manual verification, and remediation guidance. Use when auditing websites for accessibility, fixing WCAG violations, or implementing ac...","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fsecurity\u002Fwcag-audit-patterns\u002FSKILL.md",[],[133,134,135,136,137,35,138,139,140,141,123,142,143,144,145],"wcag audit patterns","wcag audit","patterns","WCAG","audit","manual","with","automated testing","guidance","remediation","verification","accessibility","testing",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":152,"publishedAt":38},"01M6G60PMCHW1FRZ63F931QK3R","vulnerability-scanner","Advanced vulnerability analysis principles. OWASP 2025, Supply Chain Security, attack surface mapping, risk prioritization.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fsecurity\u002Fvulnerability-scanner\u002FSKILL.md",[],[153,154,155,156,157,158,159,160,35,89,83,161,84,162,163,164,165,166,167,168],"scanner","vulnerability scanner","vulnerability","prioritization","2025","mapping","chain","supply chain","Bash","analysis","risk","owasp","supply chain security","security","supply","advanced",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":175,"publishedAt":38},"01M6G60JZBB8DK0W3DSG1WNMZK","threat-modeling-expert","Expert in threat modeling methodologies, security architecture review, and risk assessment. Masters STRIDE, PASTA, attack trees, and security requirement extraction. Use for security architecture r...","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fskills\u002Fsecurity\u002Fthreat-modeling-expert\u002FSKILL.md",[],[176,177,178,179,180,181,182,183,166,123,184,185,186,35,187,188,189,163,190,191],"modeling","threat modeling expert","threat modeling","expert","threat","risk assessment","architecture review","architecture","security architecture review","trees","security architecture","security requirement extraction","assessment","extraction","review","stride"]