[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CT0EQV76W82HTF28Q9R5KV":3,"skill-same:01M6CT0EQV76W82HTF28Q9R5KV":68},{"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":29,"linkOnly":30,"markdownContent":31,"markdownBody":32,"frontMatter":33,"refName":36,"commitSha":37,"skillDirectory":38,"forksCount":39,"repositoryHtmlUrl":40,"repositoryHomepageUrl":40,"ownerHtmlUrl":41,"ownerBlogUrl":6,"ownerDescription":42,"updatedAt":43,"install":44,"resources":57},"01M6CT0EQV76W82HTF28Q9R5KV","error-diagnostics-smart-debug",null,"Use when working with error diagnostics smart debug","Sam-06060\u002Fjarvis-assistant","Sam-06060","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F208600848?v=4","01M6CRKT7C7FXD21V6HXF293S0","Samson Frank Ganta","01M6CRKTBEKYFD8YX5Z1GT99EE",6,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Ferror-diagnostics-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],"debug","diagnostics","error diagnostics smart debug","use","community","2026-11-10 10:12:30",false,"---\nname: error-diagnostics-smart-debug\ndescription: \"Use when working with error diagnostics smart debug\"\nrisk: unknown\nsource: community\ndate_added: \"2026-02-27\"\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. Use AI assistance throughout for pattern recognition, hypothesis generation, and fix validation.\n\n---\n\nIssue to debug: $ARGUMENTS\n",{"name":5,"risk":34,"source":28,"date_added":35,"description":7},"unknown","2026-02-27","main","a50a3aa6b39fb0e1b7db88bef1bfc9c93606e126","modules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Ferror-diagnostics-smart-debug",0,"https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant","https:\u002F\u002Fgithub.com\u002FSam-06060","Mobile Application Developer | Kotlin | Flutter, KMP, Swfit, Android,Swift UI | Building at Saar It Resources | Creator of Jarvis (AI Assistant)","2026-06-23 05:13:38",{"source":8,"skillName":5,"commands":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add Sam-06060\u002Fjarvis-assistant --skill error-diagnostics-smart-debug",{"tool":50,"command":51},"bunx","bunx skills add Sam-06060\u002Fjarvis-assistant --skill error-diagnostics-smart-debug",{"tool":53,"command":54},"pnpm","pnpm dlx skills add Sam-06060\u002Fjarvis-assistant --skill error-diagnostics-smart-debug","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Ferror-diagnostics-smart-debug\u002FSKILL.md",true,[58,64],{"path":59,"resourceType":60,"resourceTypeDesc":61,"sizeBytes":62,"sourceHtmlUrl":63},"agents\u002Fopenai.yaml","REFERENCE","参考文档",228,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fbnc4vk\u002Fcodex-tui-proof\u002Fskills\u002Fvalidate-tui-in-codex\u002Fagents\u002Fopenai.yaml",{"path":65,"resourceType":60,"resourceTypeDesc":61,"sizeBytes":66,"sourceHtmlUrl":67},"references\u002Fevidence-contract.md",2477,"https:\u002F\u002Fgithub.com\u002Fhashgraph-online\u002Fawesome-codex-plugins\u002Fblob\u002Fmain\u002Fplugins\u002Fbnc4vk\u002Fcodex-tui-proof\u002Fskills\u002Fvalidate-tui-in-codex\u002Freferences\u002Fevidence-contract.md",[69,82,98,125,144,171],{"skillNo":70,"name":71,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":72,"categories":73,"keywords":79,"publishedAt":29},"01M6GDRVS6FXX3ESH0C9DW01C0","test_skill","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fscratch\u002Ftest_skill\u002FSKILL.md",[74],{"categoryNo":75,"categoryName":76,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[80,81],"Test Skill","test",{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":29},"01M6GDRQ40HX22MA6XWC31RHHK","zustand-store-ts","Create Zustand stores following established patterns with proper TypeScript types and middleware.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzustand-store-ts\u002FSKILL.md",[],[89,90,91,92,93,94,95,28,96,97],"zustand store ts","zustand","store","with","types","middleware","patterns","create","typescript",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":102,"categories":103,"keywords":107,"publishedAt":29},"01M6GDRKEA0WE670SPFWX5GKCN","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\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoom-automation\u002FSKILL.md",[104],{"categoryNo":105,"categoryName":106,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000023","Skill与智能体开发",[108,109,110,111,112,113,114,115,116,117,118,119,120,121,28,122,123,124],"automation","zoom automation","zoom","first","Tools","search","MCP","via","meeting","automate","critical","creation","webinars","composio","recordings","management","tracking",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":132,"publishedAt":29},"01M6GDRFS1452PC71JQ38S24HR","zoho-crm-automation","Automate Zoho CRM tasks via Rube MCP (Composio): create\u002Fupdate records, search contacts, manage leads, and convert leads. Always search tools first for current schemas.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzoho-crm-automation\u002FSKILL.md",[131],{"categoryNo":105,"categoryName":106,"taxonomyType":77,"taxonomyTypeDesc":78},[133,108,134,135,136,137,138,28,113,139,121,140,118,117,112,141,96,142,114,143],"crm automation","zoho crm","crm","zoho crm automation","zoho","leads","contacts","records","update","tasks","manage",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":153,"publishedAt":29},"01M6GDRCKQBAVTGHX5XBH26KPZ","zod-validation-expert","Expert in Zod — TypeScript-first schema validation. Covers parsing, custom errors, refinements, type inference, and integration with React Hook Form, Next.js, and tRPC.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzod-validation-expert\u002FSKILL.md",[150],{"categoryNo":151,"categoryName":152,"taxonomyType":77,"taxonomyTypeDesc":78},"01JBSEEDCAT000000000000009","前端开发",[154,155,156,157,158,97,159,160,161,162,163,164,92,165,166,167,168,28,169,170],"expert","zod","zod validation expert","validation","react","safe","parsing","errors","schema validation","react hook form","hook","trpc","form","type","next","schema","Next.js",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":175,"categories":176,"keywords":178,"publishedAt":29},"01M6GDR658B08K4MY0RWD4A6EH","zeroize-audit","Detects missing zeroization of sensitive data in source code and identifies zeroization removed by compiler optimizations, with assembly-level analysis, and control-flow verification. Use for auditing C\u002FC++\u002FRust code handling secrets, keys, passwords, or other sensitive data.","https:\u002F\u002Fgithub.com\u002FSam-06060\u002Fjarvis-assistant\u002Fblob\u002Fmain\u002Fmodules\u002Fagent_skills\u002Fawesome-skills\u002Fplaybooks\u002Fzeroize-audit\u002FSKILL.md",[177],{"categoryNo":105,"categoryName":106,"taxonomyType":77,"taxonomyTypeDesc":78},[179,180,181,182,183,184,185,186,28,187,188,189,190,191,192,193,194,195,196,114],"zeroize audit","audit","write","passwords","get","project","c++","activate","assembly","read","Bash","secrets","analysis","data","flow","verification","code","rust"]