From tao-skill-bank
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 / FAR / 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".
How this skill is triggered — by the user, by Claude, or both
Slash command
/tao-skill-bank:tao-analyze-changenet-rcaThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
You 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**.
BENCHMARK.mdevals/evals.jsonhooks/_parse-stdin.shhooks/rca-defect-coverage.shhooks/rca-depth-check.shhooks/rca-package.shhooks/rca-phase-completeness.shhooks/rca-report-check.shhooks/rca-script-check.shreferences/output-and-deliverable.mdreferences/parallelization.mdreferences/phases.mdreferences/report-structure.mdskill-card.mdskill.oms.sigYou 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.
When 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.
train/ and inference/visual_changenet/ source treeThe ChangeNet model compares a test image against a golden image (known-good reference) to detect differences. When viewing images, check these three things:
The 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.
See references/phases.md for the full step-by-step procedure of every phase and sub-phase, including all commands, scripts, thresholds, numeric values, image path construction rules, severity guidance, and required report outputs. Execute every step exactly as specified there.
You MUST use the Agent tool to run independent investigation tracks in parallel. Run Phase 1 yourself in the main thread, then launch 6 subagents (Agents A–F) simultaneously for Phase 2–4 tracks, collect and synthesize their findings (paying special attention to exploratory Agents E and F), run Phase 5 yourself, and write the report. The report-writing step enforces a mandatory Image Embedding Protocol — every visual evidence table row must carry inline thumbnail columns or the hook will reject the report.
See references/parallelization.md for the complete execution plan: the exact Phase 1 outputs to save, the per-agent checklists and inputs for Agents A–F, the synthesis cross-checks, the full mandatory Image Embedding Protocol with per-section rules and table format, the exploratory findings section, and the subagent prompt template including the required Thumbnail Map return format. Follow it exactly.
softmax(model(img1, img2), dim=1)[:, 1] → score = P(defect). Higher = more defective.F.pairwise_distance(embed1, embed2) → score = distance. Higher = more different.fail_wt = (num_pass / num_fail) * fpratio_sampling. Defects sampled at fail_wt:1 rate.{images_dir}/{input_path}/{object_name}_{light_condition}.{ext}lr * (1.0 - epoch / (num_epochs + 1))SiameseNetworkTRIDataset for num_golden=1, MultiGoldenDataset for num_golden>1Produce RCA_Report.md with 9 top-level sections: (1) Verdict, (2) Score Analysis, (3) Visual Evidence (with inline thumbnails throughout), (4) Cross-Dimensional Analysis, (5) Data Issues, (6) Training Config Issues, (7) Exploratory Findings, (8) Counterfactual Impact Analysis, and (9) Recommended Fixes (prioritized by impact × feasibility). Visual Evidence tables must embed thumbnails generated into rca_images/.
See references/report-structure.md for the complete report skeleton with every section, subsection, table column layout, and inline-thumbnail requirement. Match it exactly.
Always save into a timestamped folder under <experiment_result_dir>/rca_results/YYYY-MM-DD_HHMMSS/ containing RCA_Report.md, the rca_images/ thumbnail folder, the hook-populated rca_config/, and claude_session.jsonl. Get the real timestamp by running date +%Y-%m-%d_%H%M%S in Bash — never hardcode or guess it.
See references/output-and-deliverable.md for the full directory tree and the exact ordered steps for creating the folder, writing thumbnails, and writing the report (which triggers the packaging hook). If the user specifies a custom path, use that instead but maintain the same structure.
npx claudepluginhub nvidia-tao/tao-skills-bank --plugin tao-skillsGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.