[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6FSYCPSF415CS2HXNCC0414":3,"skill-same:01M6FSYCPSF415CS2HXNCC0414":60},{"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":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":14,"repositoryHtmlUrl":41,"repositoryHomepageUrl":6,"ownerHtmlUrl":42,"ownerBlogUrl":43,"ownerDescription":44,"updatedAt":45,"install":46,"resources":59},"01M6FSYCPSF415CS2HXNCC0414","debugging-toolkit-smart-debug",null,"Use when working with debugging toolkit smart debug","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\u002Fcreative\u002Fdebugging-toolkit-smart-debug\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[24,25,26,27,28,29,30,31],"debug","debugging","toolkit","debugging toolkit smart debug","debugging toolkit","use","community","with","2026-11-10 10:12:30",false,"---\nname: debugging-toolkit-smart-debug\ndescription: \"Use when working with debugging toolkit smart debug\"\nrisk: unknown\nsource: community\n---\n\n## Use this skill when\n\n- Working on debugging toolkit smart debug tasks or workflows\n- Needing guidance, best practices, or checklists for debugging toolkit smart debug\n\n## Do not use this skill when\n\n- The task is unrelated to debugging toolkit 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 debugging toolkit smart debug tasks or workflows\n- Needing guidance, best practices, or checklists for debugging toolkit smart debug\n\n## Do not use this skill when\n\n- The task is unrelated to debugging toolkit 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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Framework selection, WebGPU, optimization, PWA.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fweb-games\u002FSKILL.md",[],[68,69,70,71,72,73,30,74,75,76,77,78,79,80,81,82,83,84,85,86],"games","web games","web","grep","principles","selection","framework selection","edit","write","read","pwa","game","web browser","game development","Optimization","framework","browser","development","webgpu",{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":93,"publishedAt":32},"01M6G611H2YGV0GZFP90D38ZMJ","vr-ar","VR\u002FAR development principles. Comfort, interaction, performance requirements.","https:\u002F\u002Fgithub.com\u002Freikiplanet\u002Fdaoskills\u002Fblob\u002Fmaster\u002Fvr-ar\u002FSKILL.md",[],[94,95,85,96,97,30,75,76,77,71,98,72],"ar","vr ar","interaction","requirements","performance",{"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":32},"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",[],[101,106,107,108,109,110,30,29,77,111,112,71],"project scaffolding","From Scratch","tech","project","scaffolding","projects","applications",{"skillNo":114,"name":115,"nameZh":6,"description":116,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":117,"categories":118,"keywords":119,"publishedAt":32},"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",[],[120,121,122,123,124,30,125,31,126,127,29,128,129,130,131],"wcag audit patterns","wcag audit","patterns","WCAG","audit","manual","automated testing","guidance","remediation","verification","accessibility","testing",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":138,"publishedAt":32},"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",[],[139,140,141,142,143,144,145,146,30,77,71,147,72,148,149,150,151,152,153,154],"scanner","vulnerability scanner","vulnerability","prioritization","2025","mapping","chain","supply chain","Bash","analysis","risk","owasp","supply chain security","security","supply","advanced",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":161,"publishedAt":32},"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",[],[162,163,164,165,166,167,168,169,152,29,170,171,172,30,173,174,175,149,176,177],"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"]