Agent skill · nvidia
tao-prepare-anomalygennext-inputs
Prepare normalized object-detection false negatives, source masks, and clean-image embedding inputs for AnomalyGenNext. Use when turning box-level FN gaps into a frozen, pair-preserving preparation plan. Do not use for training or generation.
What it needs
About 3k tokens when loaded.
What this skill does
Prepare AnomalyGenNext Inputs The first action freezes eligible false-negative identities, isolates each FN mask to its bounding box, selects one deterministic same-type mask, and emits the clean and FN embedding specs. The second action consumes those embedding results, preserves every FN-to-clean pair, and runs native automatic mask placement (AMP) from the AnomalyGenNext container. The final action accepts only pairs for which both mask branches passed AMP, then freezes the exact generation inputs and their hashes. Input contract Start from assets/defaultfiltering.yaml. The gap parquet must contain: Identity is explicit: anomalytype must equal textureid+defectclass. Preparation intentionally has no filename or directory-layout inference. A caller adapting an unfamiliar dataset must normalize these fields before this boundary. Clean references are resolved independently for each routed FN/type. A missing directory or a directory with no supported images skips only affected FNs with reason nocleanreferenceimages; it does not disable other types. Each selected dataset maps to an existing fine-tuned checkpoint and recipe. The recipe must declare the exact anomaly type, and defectspec.jsonl must contain its placement definition. Text-routed definitions require a nonempty roipromptdefectlocation. The pool layout is: Action Run through the selected platform after the common launch review: The output root must not exist. The action emits fnqueries.parquet, selectedfnqueries.parquet, maskselection.parquet, cleanpool.parquet, two tao-generate-image-embeddings specs, the copied filtering config, and inputcontract.json. Mask eligibility is evaluated per FN. The contract lists eligible FN IDs plus stable skipped FN IDs and machine-readable reasons; skipped FNs are absent from every query, embedding, mask, and AMP input. Donor masks are tried in deterministic order until one valid same-type donor is found. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-prepare-anomalygennext-inputs