Agent skill · data analytics · nvidia
tao-analyze-gaps-visual-changenet
Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the pinned TAO data-services container directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sample weakness, and emits a top-K weakest parquet expanded per-lighting for downstream augmentation. Use when analyzing VCN classification failures, picking SDA augmentation targets, or auditing PASS/NO_PASS boundary cases.
Why this skill is useful
Adds specific commands and configurations for performing gap analysis on NVIDIA TAO VCN Classify experiments that the AI wouldn't reliably generate on its own.
What it needs
Requires docker, nvidia-container-toolkit installed locally. About 9k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
TAO VCN Classify Gap Analysis 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 analyst for NVIDIA TAO VCN Classify (Visual Component Net) inference results. Your job is to identify the weakest samples per ground-truth label by measuring signed distance from the decision threshold in the wrong direction, then surface them for downstream augmentation or relabeling. This skill is intentionally lightweight. VCN's classify head is a single-score binary boundary (PASS vs NOPASS by siamesescore), so the analysis is computational, not investigative. The whole computation lives behind one direct docker run invocation against the pinned TAO data-services image (see Setup). The container's entrypoint takes <category> <action> [hydra overrides...]; we pass gapanalysis vcnaoi key=value …. Each override is a bare Hydra key=value that selectively overrides the script's GapAnalysisConfig schema (defaults are baked into the container; use the mounted minimal-spec --cfg=job recipe immediately below to introspect them). (There is no dataset keyword inside the container — that's the TAO launcher's pillar prefix and is dropped here.) You do not need delegated analysis, multi-phase image audits, or component-type clustering — VCN does not expose those dimensions. View only a small set of representative weak samples to qualify the gaps after the container returns. CLI surface can shift between data-services container builds. If a gapanalysis vcnaoi invocation fails on argument parsing, introspect the actual schema once per image with a minimal spec on a bind-mounted host path: The -e path must resolve inside the container, so the spec must live under the directory mounted at /w. Reconcile any renamed keys (e.g. inferencecsv vs inferenceresultsdir, outputdir vs resultsdir) before retrying. Output parquet name is kpigaps.parquet. --- Inputs 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-analyze-gaps-visual-changenet