[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71PTCFD2X4XC161M8E2WEPB":3,"skill-same:01M71PTCFD2X4XC161M8E2WEPB":134},{"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":18,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":51,"commitSha":52,"skillDirectory":53,"forksCount":14,"repositoryHtmlUrl":54,"repositoryHomepageUrl":55,"ownerHtmlUrl":56,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":57,"install":58,"resources":71},"01M71PTCFD2X4XC161M8E2WEPB","tao-analyze-changenet-rca",null,"Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall \u002F FAR \u002F PASS-NO_PASS metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model. Trigger phrases include \"RCA on my ChangeNet model\", \"why is my AOI model failing\", \"audit ChangeNet predictions\", \"investigate FAR regressions\", \"root cause analysis on visual-changenet\".","pattanation\u002FNorvik","pattanation","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F235419268?v=4","01M71M5THHYYB15ME8R2E4FCZG","Patta","01M71M5TJM1NDM992M8PDKYR2K",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Ftao-analyze-changenet-rca\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"rca","tao analyze changenet rca","changenet","analyze","tao","inspection","image","failing","when","defect","predictions","deep","read","model","Bash","why","analysis","pipeline","audit","workflows","2026-11-10 13:02:02",false,"---\nname: tao-analyze-changenet-rca\ndescription: Performs deep Root Cause Analysis (RCA) on NVIDIA TAO Visual ChangeNet classification experiments with\n  image-evidence-driven investigation. Use when analyzing ChangeNet model failures, investigating poor recall \u002F FAR \u002F PASS-NO_PASS\n  metrics, auditing visual inspection pipeline quality, or running an RCA report for an AOI defect-detection model.\n  Trigger phrases include \"RCA on my ChangeNet model\", \"why is my AOI model failing\", \"audit ChangeNet predictions\",\n  \"investigate FAR regressions\", \"root cause analysis on visual-changenet\".\nlicense: Apache-2.0\ncompatibility: Requires docker + nvidia-container-toolkit. Workflows declare additional requirements.\nmetadata:\n  author: NVIDIA Corporation\n  version: \"0.1.0\"\nallowed-tools: Read Bash\ntags:\n- application\n- rca\n- changenet\n---\n\n# TAO ChangeNet Classification RCA Skill\n\nYou are an expert investigator for NVIDIA TAO Visual ChangeNet classification experiments. Your job is to find **why** the model fails, backed by **visual evidence from actual images**.\n\nWhen the user provides an experiment result directory and training code directory, perform a deep Root Cause Analysis. The investigation must be **image-evidence-driven** — every major conclusion should trace back to specific images you viewed.\n\n---\n\n## Inputs\n\n1. **Experiment result directory** — contains `train\u002F` and `inference\u002F`\n2. **Training code directory** — the `visual_changenet\u002F` source tree\n3. **Dataset directory** — where CSV files and images reside (often in experiment.yaml)\n4. **Target KPI** — default to **Recall-first** if not specified. Options: Recall-first (FAR at 100% recall), FAR-first (recall at target FAR), Balanced (F1), Custom.\n\n---\n\n## Visual Inspection Primer\n\nThe ChangeNet model compares a **test image** against a **golden image** (known-good reference) to detect differences. When viewing images, check these three things:\n\n1. **Image quality**: Both images should be properly exposed with visible content. Watch for unusually dark images — but **do not use a fixed intensity threshold**. Some illumination types (e.g., SolderLight) produce systemically dark images where mean intensity \u003C 30 is normal. Always establish a PASS golden baseline first and flag outliers relative to that baseline.\n2. **Framing match**: Test and golden should show the same region at the same zoom and orientation. Mismatched framing (e.g., wide-field vs close-up) indicates a golden pipeline error.\n3. **Defect visibility**: Can you see the difference between test and golden? Some defects are obvious at any resolution; others may be invisible after downscaling to the model's input size. Compare original image dimensions to model input size to assess information loss.\n\n---\n\n## Investigation Flow\n\nThe investigation has 5 phases. Phase 1 (numbers) gives you hypotheses. Phase 2 (images) proves or disproves them. Phase 3 (cross-dimensional) finds hidden patterns. Phase 4 (config) explains the mechanism. Phase 5 (counterfactual) quantifies fixes. **Phase 2 is the core — spend the most effort there. Phase 5 is the most actionable — never skip it.**\n\n- **Phase 1 — Score Analysis**: score statistics, tier classification, threshold sweep, per-defect-type table, drop-N threshold-critical analysis, KPI verdict.\n- **Phase 2 — Deep Image Investigation**: threshold-critical sample deep dive (2A), systematic golden audit + failure mode clustering (2B), false positive deep dive (2C), comparative visual analysis (2D), label semantics & visual pattern alignment audit (2E).\n- **Phase 3 — Cross-Dimensional Analysis**: component-type clustering (3A), board-level & positional analysis (3B), training image deep dive (3C), multi-light condition analysis (3D).\n- **Phase 4 — Data & Training Config Analysis**: data sufficiency (4A), training config audit (4B), training metrics (4C), loss function & decision boundary analysis (4D).\n- **Phase 5 — Counterfactual & Actionability**: what-if simulations (5A), minimum viable fix path (5B).\n\nSee `references\u002Finvestigation-phases.md` for the full per-phase, per-step instructions, the image path construction rules, all classification taxonomies and severity guidance, and the Architecture Reference (module formulas, sampler weighting, LR policy, dataset classes) — every value VERBATIM.\n\n---\n\n## Execution: Parallelize With Subagents\n\n**You MUST use the Agent tool to run independent investigation tracks in parallel.** Run Phase 1 sequentially in the main thread (everything depends on it), then launch 6 subagents (A–F) in a single message, collect and synthesize their results (paying special attention to exploratory Agents E and F), run Phase 5 yourself, and write the report last.\n\nBefore writing `RCA_Report.md`, run `ls rca_images\u002F` to inventory thumbnails, and follow the **mandatory Image Embedding Protocol**: every visual-evidence table row must carry inline thumbnail columns using `![caption](rca_images\u002F\u003Cfilename>.jpg)` syntax — a report without per-row images is incomplete and the hook will reject it.\n\nSee `references\u002Fparallelization.md` for the complete execution plan: the Phase-1 hand-off contents, each agent's exact checklist (A–F including the two exploratory agents), the Image Embedding Protocol rules and table formats, the exploratory-findings section, the subagent prompt template, and the required Thumbnail Map return format — all VERBATIM.\n\n---\n\n## Report Structure and Output\n\nProduce `RCA_Report.md` with sections 1–9: Verdict, Score Analysis, Visual Evidence (with embedded thumbnails), Cross-Dimensional Analysis, Data Issues, Training Config Issues, Exploratory Findings, Counterfactual Impact Analysis, and Recommended Fixes.\n\nAlways save into a timestamped folder under the experiment result directory:\n```\n\u003Cexperiment_result_dir>\u002Frca_results\u002FYYYY-MM-DD_HHMMSS\u002F\n├── RCA_Report.md\n├── rca_images\u002F\n├── rca_config\u002F\n└── claude_session.jsonl\n```\nGet the real timestamp by running `date +%Y-%m-%d_%H%M%S` in Bash — never hardcode or guess it. If the user specifies a custom path, use that instead but keep the same structure.\n\nSee `references\u002Foutput-structure.md` for the complete section-by-section report skeleton (every table header and summary line) and the full output layout with hook-copied contents — VERBATIM.\n","# TAO ChangeNet Classification RCA Skill\n\nYou are an expert investigator for NVIDIA TAO Visual ChangeNet classification experiments. Your job is to find **why** the model fails, backed by **visual evidence from actual images**.\n\nWhen the user provides an experiment result directory and training code directory, perform a deep Root Cause Analysis. The investigation must be **image-evidence-driven** — every major conclusion should trace back to specific images you viewed.\n\n---\n\n## Inputs\n\n1. **Experiment result directory** — contains `train\u002F` and `inference\u002F`\n2. **Training code directory** — the `visual_changenet\u002F` source tree\n3. **Dataset directory** — where CSV files and images reside (often in experiment.yaml)\n4. **Target KPI** — default to **Recall-first** if not specified. Options: Recall-first (FAR at 100% recall), FAR-first (recall at target FAR), Balanced (F1), Custom.\n\n---\n\n## Visual Inspection Primer\n\nThe ChangeNet model compares a **test image** against a **golden image** (known-good reference) to detect differences. When viewing images, check these three things:\n\n1. **Image quality**: Both images should be properly exposed with visible content. Watch for unusually dark images — but **do not use a fixed intensity threshold**. Some illumination types (e.g., SolderLight) produce systemically dark images where mean intensity \u003C 30 is normal. Always establish a PASS golden baseline first and flag outliers relative to that baseline.\n2. **Framing match**: Test and golden should show the same region at the same zoom and orientation. Mismatched framing (e.g., wide-field vs close-up) indicates a golden pipeline error.\n3. **Defect visibility**: Can you see the difference between test and golden? Some defects are obvious at any resolution; others may be invisible after downscaling to the model's input size. Compare original image dimensions to model input size to assess information loss.\n\n---\n\n## Investigation Flow\n\nThe investigation has 5 phases. Phase 1 (numbers) gives you hypotheses. Phase 2 (images) proves or disproves them. Phase 3 (cross-dimensional) finds hidden patterns. Phase 4 (config) explains the mechanism. Phase 5 (counterfactual) quantifies fixes. **Phase 2 is the core — spend the most effort there. Phase 5 is the most actionable — never skip it.**\n\n- **Phase 1 — Score Analysis**: score statistics, tier classification, threshold sweep, per-defect-type table, drop-N threshold-critical analysis, KPI verdict.\n- **Phase 2 — Deep Image Investigation**: threshold-critical sample deep dive (2A), systematic golden audit + failure mode clustering (2B), false positive deep dive (2C), comparative visual analysis (2D), label semantics & visual pattern alignment audit (2E).\n- **Phase 3 — Cross-Dimensional Analysis**: component-type clustering (3A), board-level & positional analysis (3B), training image deep dive (3C), multi-light condition analysis (3D).\n- **Phase 4 — Data & Training Config Analysis**: data sufficiency (4A), training config audit (4B), training metrics (4C), loss function & decision boundary analysis (4D).\n- **Phase 5 — Counterfactual & Actionability**: what-if simulations (5A), minimum viable fix path (5B).\n\nSee `references\u002Finvestigation-phases.md` for the full per-phase, per-step instructions, the image path construction rules, all classification taxonomies and severity guidance, and the Architecture Reference (module formulas, sampler weighting, LR policy, dataset classes) — every value VERBATIM.\n\n---\n\n## Execution: Parallelize With Subagents\n\n**You MUST use the Agent tool to run independent investigation tracks in parallel.** Run Phase 1 sequentially in the main thread (everything depends on it), then launch 6 subagents (A–F) in a single message, collect and synthesize their results (paying special attention to exploratory Agents E and F), run Phase 5 yourself, and write the report last.\n\nBefore writing `RCA_Report.md`, run `ls rca_images\u002F` to inventory thumbnails, and follow the **mandatory Image Embedding Protocol**: every visual-evidence table row must carry inline thumbnail columns using `![caption](rca_images\u002F\u003Cfilename>.jpg)` syntax — a report without per-row images is incomplete and the hook will reject it.\n\nSee `references\u002Fparallelization.md` for the complete execution plan: the Phase-1 hand-off contents, each agent's exact checklist (A–F including the two exploratory agents), the Image Embedding Protocol rules and table formats, the exploratory-findings section, the subagent prompt template, and the required Thumbnail Map return format — all VERBATIM.\n\n---\n\n## Report Structure and Output\n\nProduce `RCA_Report.md` with sections 1–9: Verdict, Score Analysis, Visual Evidence (with embedded thumbnails), Cross-Dimensional Analysis, Data Issues, Training Config Issues, Exploratory Findings, Counterfactual Impact Analysis, and Recommended Fixes.\n\nAlways save into a timestamped folder under the experiment result directory:\n```\n\u003Cexperiment_result_dir>\u002Frca_results\u002FYYYY-MM-DD_HHMMSS\u002F\n├── RCA_Report.md\n├── rca_images\u002F\n├── rca_config\u002F\n└── claude_session.jsonl\n```\nGet the real timestamp by running `date +%Y-%m-%d_%H%M%S` in Bash — never hardcode or guess it. 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Covers Vitest, Vue Test Utils, component testing, mocking, testing patterns, and Playwright for E2E testing.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-testing-best-practices\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-testing-best-practices\u002FSKILL.md",[225,226],{"categoryNo":143,"categoryName":144,"taxonomyType":145,"taxonomyTypeDesc":146},{"categoryNo":148,"categoryName":149,"taxonomyType":145,"taxonomyTypeDesc":146},[228,152,229,215,230,163,231,232,233,210,234,154,235,236,237,186,238,216,239],"vue testing best practices","testing best practices","best practices","test","testing patterns","component","vitest","mocking","e2e","utils","patterns","e2e testing",{"skillNo":241,"name":242,"nameZh":6,"description":243,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":198,"licenseFileUrl":244,"sourceHtmlUrl":245,"categories":246,"keywords":248,"publishedAt":39},"01M71QD358H2DJ9THJRBP77T3T","vue-router-best-practices","Vue Router 4 patterns, navigation guards, route params, and route-component lifecycle interactions.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-router-best-practices\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-router-best-practices\u002FSKILL.md",[247],{"categoryNo":148,"categoryName":149,"taxonomyType":145,"taxonomyTypeDesc":146},[249,215,250,230,251,233,252,210,238,253,254],"router","vue router best practices","route","guards","navigation","lifecycle",{"skillNo":256,"name":257,"nameZh":6,"description":258,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":198,"licenseFileUrl":259,"sourceHtmlUrl":260,"categories":261,"keywords":263,"publishedAt":39},"01M71QCYPNWDE2MQZ1TF3ED5S4","vue-best-practices","MUST be used for Vue.js tasks. Strongly recommends Composition API with `\u003Cscript setup>` and TypeScript as the standard approach. Covers Vue 3, SSR, Volar, vue-tsc. Load for any Vue, .vue files, Vue Router, Pinia, or Vite with Vue work. ALWAYS use Composition API unless the project explicitly requires Options API.","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-best-practices\u002FLICENSE.md","https:\u002F\u002Fgithub.com\u002Fpattanation\u002FNorvik\u002Fblob\u002Fclaude\u002Finstall-gsd-build-foQdV\u002F.agents\u002Fskills\u002Fvue-best-practices\u002FSKILL.md",[262],{"categoryNo":148,"categoryName":149,"taxonomyType":145,"taxonomyTypeDesc":146},[264,215,230,265,207,266,267,268,249,269,270,271,272,273,274,154,275,186,276,277],"vue best practices","Composition","files","tasks","api","pinia","work","options","ssr","typescript","as","vite","load","standard"]