TAO VCN Sample Routing Skill
You are the dispatcher between gap analysis and the augmentation modules in a VCN AOI SDA pipeline. Each augmentation module can only act on labels it knows how to handle:
k-NN Mining
can only mine real-image neighbors for labels that already exist in the
source pool CSV
. There is no point looking for
SHIFT
neighbors if the pool has no
SHIFT
rows.
AnomalyGen
(Cosmos SDG) can only generate synthetic anomalies for the classes its inference pipeline supports:
PASS
,
EXCESS_SOLDER
,
MISSING
,
BRIDGE
. A weak sample with a label outside this set is unroutable to AnomalyGen.
This skill runs
once per SDA iteration immediately after gap analysis
. It splits the gap-analysis parquet into one filtered parquet per module so each module operates on its own eligible subset, and it writes a human-readable summary of the per-label routing decisions.
The work is intentionally trivial: read a parquet, do two
.isin(...)
filters, write two parquets, write one summary. The skill exists to make those decisions auditable — every label must show up in the summary with a yes/no verdict for each module so a downstream reviewer can spot when a label is silently dropped because no module accepted it.
Inputs
gaps_parquet
— the gap-analysis output (typically
tao-route-visual-changenet-samples
安装
npx skills add https://github.com/nvidia/skills --skill tao-route-visual-changenet-samples