Individual skill
tao-analyze-changenet-rca
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".
skills/tao-analyze-changenet-rca/SKILL.md
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- Last checked
- Nov 9, 2026
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- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
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BENCHMARK.mddoc · 7,029 bytesSKILL.mdcore · 6,528 bytesSKILL.mdcore · 6,528 bytesconfig/skillspector-baseline.yamlasset · 2,483 bytesevals/evals.jsonasset · 804 byteshooks/_parse-stdin.shruns · 1,471 byteshooks/rca-defect-coverage.shruns · 5,596 byteshooks/rca-depth-check.shruns · 9,912 byteshooks/rca-package.shruns · 2,964 byteshooks/rca-phase-completeness.shruns · 4,288 byteshooks/rca-report-check.shruns · 5,025 byteshooks/rca-script-check.shruns · 3,173 bytesreferences/investigation-phases.mddoc · 17,044 bytesreferences/output-structure.mddoc · 6,097 bytesreferences/parallelization.mddoc · 8,804 bytesskill-card.mddoc · 4,376 bytesskill.oms.sigasset · 7,245 bytes
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# TAO ChangeNet Classification RCA Skill
> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first (host preflight, credentials, cross-skill discovery).
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**.
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.
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## Inputs
1. **Experiment result directory** — contains `train/` and `inference/`
2. **Training code directory** — the `visual_changenet/` source tree