[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71S78CYWPP3ND603WEEEJ30":3,"skill-same:01M71S78CYWPP3ND603WEEEJ30":57},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":37,"commitSha":38,"skillDirectory":39,"forksCount":13,"repositoryHtmlUrl":40,"repositoryHomepageUrl":6,"ownerHtmlUrl":41,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":42,"install":43,"resources":56},"01M71S78CYWPP3ND603WEEEJ30","error-diagnostics-smart-debug",null,"Use when working with error diagnostics smart debug","javiertarazon\u002Fide-agente-free-jt7-","javiertarazon","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F153141207?v=4","01M71R52TB3S9HXKH6WSNT8A7V","01M71R52VG5KS2DKDDH4GDQ30A",0,"MIT","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Ferror-diagnostics-smart-debug\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[23,24,25,26,27,28,29,30,31],"debug","diagnostics","error diagnostics smart debug","error","use","community","with","unknown","when","2026-11-10 13:02:02",false,"---\nname: error-diagnostics-smart-debug\ndescription: \"Use when working with error diagnostics smart debug\"\nrisk: unknown\nsource: community\n---\n\n## Use this skill when\n\n- Working on error diagnostics smart debug tasks or workflows\n- Needing guidance, best practices, or checklists for error diagnostics smart debug\n\n## Do not use this skill when\n\n- The task is unrelated to error diagnostics smart debug\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\nYou are an expert AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis.\n\n## Context\n\nProcess issue from: $ARGUMENTS\n\nParse for:\n- Error messages\u002Fstack traces\n- Reproduction steps\n- Affected components\u002Fservices\n- Performance characteristics\n- Environment (dev\u002Fstaging\u002Fproduction)\n- Failure patterns (intermittent\u002Fconsistent)\n\n## Workflow\n\n### 1. Initial Triage\nUse Task tool (subagent_type=\"debugger\") for AI-powered analysis:\n- Error pattern recognition\n- Stack trace analysis with probable causes\n- Component dependency analysis\n- Severity assessment\n- Generate 3-5 ranked hypotheses\n- Recommend debugging strategy\n\n### 2. Observability Data Collection\nFor production\u002Fstaging issues, gather:\n- Error tracking (Sentry, Rollbar, Bugsnag)\n- APM metrics (DataDog, New Relic, Dynatrace)\n- Distributed traces (Jaeger, Zipkin, Honeycomb)\n- Log aggregation (ELK, Splunk, Loki)\n- Session replays (LogRocket, FullStory)\n\nQuery for:\n- Error frequency\u002Ftrends\n- Affected user cohorts\n- Environment-specific patterns\n- Related errors\u002Fwarnings\n- Performance degradation correlation\n- Deployment timeline correlation\n\n### 3. Hypothesis Generation\nFor each hypothesis include:\n- Probability score (0-100%)\n- Supporting evidence from logs\u002Ftraces\u002Fcode\n- Falsification criteria\n- Testing approach\n- Expected symptoms if true\n\nCommon categories:\n- Logic errors (race conditions, null handling)\n- State management (stale cache, incorrect transitions)\n- Integration failures (API changes, timeouts, auth)\n- Resource exhaustion (memory leaks, connection pools)\n- Configuration drift (env vars, feature flags)\n- Data corruption (schema mismatches, encoding)\n\n### 4. Strategy Selection\nSelect based on issue characteristics:\n\n**Interactive Debugging**: Reproducible locally → VS Code\u002FChrome DevTools, step-through\n**Observability-Driven**: Production issues → Sentry\u002FDataDog\u002FHoneycomb, trace analysis\n**Time-Travel**: Complex state issues → rr\u002FRedux DevTools, record & replay\n**Chaos Engineering**: Intermittent under load → Chaos Monkey\u002FGremlin, inject failures\n**Statistical**: Small % of cases → Delta debugging, compare success vs failure\n\n### 5. Intelligent Instrumentation\nAI suggests optimal breakpoint\u002Flogpoint locations:\n- Entry points to affected functionality\n- Decision nodes where behavior diverges\n- State mutation points\n- External integration boundaries\n- Error handling paths\n\nUse conditional breakpoints and logpoints for production-like environments.\n\n### 6. Production-Safe Techniques\n**Dynamic Instrumentation**: OpenTelemetry spans, non-invasive attributes\n**Feature-Flagged Debug Logging**: Conditional logging for specific users\n**Sampling-Based Profiling**: Continuous profiling with minimal overhead (Pyroscope)\n**Read-Only Debug Endpoints**: Protected by auth, rate-limited state inspection\n**Gradual Traffic Shifting**: Canary deploy debug version to 10% traffic\n\n### 7. Root Cause Analysis\nAI-powered code flow analysis:\n- Full execution path reconstruction\n- Variable state tracking at decision points\n- External dependency interaction analysis\n- Timing\u002Fsequence diagram generation\n- Code smell detection\n- Similar bug pattern identification\n- Fix complexity estimation\n\n### 8. Fix Implementation\nAI generates fix with:\n- Code changes required\n- Impact assessment\n- Risk level\n- Test coverage needs\n- Rollback strategy\n\n### 9. Validation\nPost-fix verification:\n- Run test suite\n- Performance comparison (baseline vs fix)\n- Canary deployment (monitor error rate)\n- AI code review of fix\n\nSuccess criteria:\n- Tests pass\n- No performance regression\n- Error rate unchanged or decreased\n- No new edge cases introduced\n\n### 10. Prevention\n- Generate regression tests using AI\n- Update knowledge base with root cause\n- Add monitoring\u002Falerts for similar issues\n- Document troubleshooting steps in runbook\n\n## Example: Minimal Debug Session\n\n```typescript\n\u002F\u002F Issue: \"Checkout timeout errors (intermittent)\"\n\n\u002F\u002F 1. Initial analysis\nconst analysis = await aiAnalyze({\n  error: \"Payment processing timeout\",\n  frequency: \"5% of checkouts\",\n  environment: \"production\"\n});\n\u002F\u002F AI suggests: \"Likely N+1 query or external API timeout\"\n\n\u002F\u002F 2. Gather observability data\nconst sentryData = await getSentryIssue(\"CHECKOUT_TIMEOUT\");\nconst ddTraces = await getDataDogTraces({\n  service: \"checkout\",\n  operation: \"process_payment\",\n  duration: \">5000ms\"\n});\n\n\u002F\u002F 3. Analyze traces\n\u002F\u002F AI identifies: 15+ sequential DB queries per checkout\n\u002F\u002F Hypothesis: N+1 query in payment method loading\n\n\u002F\u002F 4. Add instrumentation\nspan.setAttribute('debug.queryCount', queryCount);\nspan.setAttribute('debug.paymentMethodId', methodId);\n\n\u002F\u002F 5. Deploy to 10% traffic, monitor\n\u002F\u002F Confirmed: N+1 pattern in payment verification\n\n\u002F\u002F 6. AI generates fix\n\u002F\u002F Replace sequential queries with batch query\n\n\u002F\u002F 7. Validate\n\u002F\u002F - Tests pass\n\u002F\u002F - Latency reduced 70%\n\u002F\u002F - Query count: 15 → 1\n```\n\n## Output Format\n\nProvide structured report:\n1. **Issue Summary**: Error, frequency, impact\n2. **Root Cause**: Detailed diagnosis with evidence\n3. **Fix Proposal**: Code changes, risk, impact\n4. **Validation Plan**: Steps to verify fix\n5. **Prevention**: Tests, monitoring, documentation\n\nFocus on actionable insights. Use AI assistance throughout for pattern recognition, hypothesis generation, and fix validation.\n\n---\n\nIssue to debug: $ARGUMENTS\n","## Use this skill when\n\n- Working on error diagnostics smart debug tasks or workflows\n- Needing guidance, best practices, or checklists for error diagnostics smart debug\n\n## Do not use this skill when\n\n- The task is unrelated to error diagnostics smart debug\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\nYou are an expert AI-assisted debugging specialist with deep knowledge of modern debugging tools, observability platforms, and automated root cause analysis.\n\n## Context\n\nProcess issue from: $ARGUMENTS\n\nParse for:\n- Error messages\u002Fstack traces\n- Reproduction steps\n- Affected components\u002Fservices\n- Performance characteristics\n- Environment (dev\u002Fstaging\u002Fproduction)\n- Failure patterns (intermittent\u002Fconsistent)\n\n## Workflow\n\n### 1. Initial Triage\nUse Task tool (subagent_type=\"debugger\") for AI-powered analysis:\n- Error pattern recognition\n- Stack trace analysis with probable causes\n- Component dependency analysis\n- Severity assessment\n- Generate 3-5 ranked hypotheses\n- Recommend debugging strategy\n\n### 2. Observability Data Collection\nFor production\u002Fstaging issues, gather:\n- Error tracking (Sentry, Rollbar, Bugsnag)\n- APM metrics (DataDog, New Relic, Dynatrace)\n- Distributed traces (Jaeger, Zipkin, Honeycomb)\n- Log aggregation (ELK, Splunk, Loki)\n- Session replays (LogRocket, FullStory)\n\nQuery for:\n- Error frequency\u002Ftrends\n- Affected user cohorts\n- Environment-specific patterns\n- Related errors\u002Fwarnings\n- Performance degradation correlation\n- Deployment timeline correlation\n\n### 3. Hypothesis Generation\nFor each hypothesis include:\n- Probability score (0-100%)\n- Supporting evidence from logs\u002Ftraces\u002Fcode\n- Falsification criteria\n- Testing approach\n- Expected symptoms if true\n\nCommon categories:\n- Logic errors (race conditions, null handling)\n- State management (stale cache, incorrect transitions)\n- Integration failures (API changes, timeouts, auth)\n- Resource exhaustion (memory leaks, connection pools)\n- Configuration drift (env vars, feature flags)\n- Data corruption (schema mismatches, encoding)\n\n### 4. Strategy Selection\nSelect based on issue characteristics:\n\n**Interactive Debugging**: Reproducible locally → VS Code\u002FChrome DevTools, step-through\n**Observability-Driven**: Production issues → Sentry\u002FDataDog\u002FHoneycomb, trace analysis\n**Time-Travel**: Complex state issues → rr\u002FRedux DevTools, record & replay\n**Chaos Engineering**: Intermittent under load → Chaos Monkey\u002FGremlin, inject failures\n**Statistical**: Small % of cases → Delta debugging, compare success vs failure\n\n### 5. Intelligent Instrumentation\nAI suggests optimal breakpoint\u002Flogpoint locations:\n- Entry points to affected functionality\n- Decision nodes where behavior diverges\n- State mutation points\n- External integration boundaries\n- Error handling paths\n\nUse conditional breakpoints and logpoints for production-like environments.\n\n### 6. Production-Safe Techniques\n**Dynamic Instrumentation**: OpenTelemetry spans, non-invasive attributes\n**Feature-Flagged Debug Logging**: Conditional logging for specific users\n**Sampling-Based Profiling**: Continuous profiling with minimal overhead (Pyroscope)\n**Read-Only Debug Endpoints**: Protected by auth, rate-limited state inspection\n**Gradual Traffic Shifting**: Canary deploy debug version to 10% traffic\n\n### 7. Root Cause Analysis\nAI-powered code flow analysis:\n- Full execution path reconstruction\n- Variable state tracking at decision points\n- External dependency interaction analysis\n- Timing\u002Fsequence diagram generation\n- Code smell detection\n- Similar bug pattern identification\n- Fix complexity estimation\n\n### 8. Fix Implementation\nAI generates fix with:\n- Code changes required\n- Impact assessment\n- Risk level\n- Test coverage needs\n- Rollback strategy\n\n### 9. Validation\nPost-fix verification:\n- Run test suite\n- Performance comparison (baseline vs fix)\n- Canary deployment (monitor error rate)\n- AI code review of fix\n\nSuccess criteria:\n- Tests pass\n- No performance regression\n- Error rate unchanged or decreased\n- No new edge cases introduced\n\n### 10. Prevention\n- Generate regression tests using AI\n- Update knowledge base with root cause\n- Add monitoring\u002Falerts for similar issues\n- Document troubleshooting steps in runbook\n\n## Example: Minimal Debug Session\n\n```typescript\n\u002F\u002F Issue: \"Checkout timeout errors (intermittent)\"\n\n\u002F\u002F 1. Initial analysis\nconst analysis = await aiAnalyze({\n  error: \"Payment processing timeout\",\n  frequency: \"5% of checkouts\",\n  environment: \"production\"\n});\n\u002F\u002F AI suggests: \"Likely N+1 query or external API timeout\"\n\n\u002F\u002F 2. Gather observability data\nconst sentryData = await getSentryIssue(\"CHECKOUT_TIMEOUT\");\nconst ddTraces = await getDataDogTraces({\n  service: \"checkout\",\n  operation: \"process_payment\",\n  duration: \">5000ms\"\n});\n\n\u002F\u002F 3. Analyze traces\n\u002F\u002F AI identifies: 15+ sequential DB queries per checkout\n\u002F\u002F Hypothesis: N+1 query in payment method loading\n\n\u002F\u002F 4. Add instrumentation\nspan.setAttribute('debug.queryCount', queryCount);\nspan.setAttribute('debug.paymentMethodId', methodId);\n\n\u002F\u002F 5. Deploy to 10% traffic, monitor\n\u002F\u002F Confirmed: N+1 pattern in payment verification\n\n\u002F\u002F 6. AI generates fix\n\u002F\u002F Replace sequential queries with batch query\n\n\u002F\u002F 7. Validate\n\u002F\u002F - Tests pass\n\u002F\u002F - Latency reduced 70%\n\u002F\u002F - Query count: 15 → 1\n```\n\n## Output Format\n\nProvide structured report:\n1. **Issue Summary**: Error, frequency, impact\n2. **Root Cause**: Detailed diagnosis with evidence\n3. **Fix Proposal**: Code changes, risk, impact\n4. **Validation Plan**: Steps to verify fix\n5. **Prevention**: Tests, monitoring, documentation\n\nFocus on actionable insights. 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Use when building React state management, creating global stores, or implementing reacti...","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fzustand-store-ts\u002FSKILL.md",[64],{"categoryNo":65,"categoryName":66,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000009","前端开发","SCENARIO","使用场景",[70,71,72,28,73,74,27,75,31,30,76,29,77,78,79,80,81,82,83,84],"zustand","store","zustand store ts","middleware","implementing","react state management","react","state","create","state management","typescript","action","management","separation","global",{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":94,"publishedAt":32},"01M71TQ5EAR40G6W0MV5ZDVEZT","zoom-automation","Automate Zoom meeting creation, management, recordings, webinars, and participant tracking via Rube MCP (Composio). Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fzoom-automation\u002FSKILL.md",[91],{"categoryNo":92,"categoryName":93,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000023","Skill与智能体开发",[95,96,97,28,98,99,100,101,102,30,103,104,105,106,107,108,109,110,82,111],"zoom automation","zoom","automation","creation","for","search","schemas","MCP","first","via","meeting","search tools","tracking","webinars","composio","recordings","Tools",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":119,"publishedAt":32},"01M71TQ29B1EYBCEAP1ZBGXQ1A","zoho-crm-automation","Automate Zoho CRM tasks via Rube MCP (Composio): create\u002Fupdate records, search contacts, manage leads, and convert leads. 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Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fzendesk-automation\u002FSKILL.md",[138],{"categoryNo":92,"categoryName":93,"taxonomyType":67,"taxonomyTypeDesc":68},[140,97,141,28,99,100,101,102,142,30,130,143,144,103,104,145,106,109,111,146],"zendesk automation","zendesk","organizations","replies","users","tickets","automate",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":153,"publishedAt":32},"01M71TPXDWKHKG4N6TVEPXWD0N","zapier-make-patterns","No-code automation democratizes workflow building. Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity ...","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fzapier-make-patterns\u002FSKILL.md",[],[154,155,156,157,158,159,160,161,162,97,163,30,164,165,166,167,168,146],"zapier make patterns","zapier","patterns","make","No-Code","complexity","writing","spawner","business","workflow","code","integromat","apache","processes","but",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":173,"categories":174,"keywords":178,"publishedAt":32},"01M71TPT5T10WXNKW9G7ZXZ27W","youtube-summarizer","Extract transcripts from YouTube videos and generate comprehensive, detailed summaries using intelligent analysis frameworks","https:\u002F\u002Fgithub.com\u002Fjaviertarazon\u002Fide-agente-free-jt7-\u002Fblob\u002Frelease\u002Fv4.2.11-panel-pro\u002F.github\u002Fskills\u002Fyoutube-summarizer\u002FSKILL.md",[175],{"categoryNo":176,"categoryName":177,"taxonomyType":67,"taxonomyTypeDesc":68},"01JBSEEDCAT000000000000016","图像与多媒体",[179,180,181,182,28,183,184,185,186,187,188,189,164,190,191,192,193,194,195,196],"summarizer","youtube","youtube summarizer","Video","content-analysis","frameworks","copilot","2025","github-copilot","extract transcripts","analysis","codex","cli","summaries","github","generate","summarization","2026"]