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tao-route-visual-changenet-samples

Routes the weakest VCN samples (output of `tao-analyze-gaps-visual-changenet`) into per-augmentation-module subsets based on each module's label eligibility. Use when the user asks to "route VCN gap samples", "split AOI gaps for k-NN mining and AnomalyGen", or prepare the immediate next step after DEFT gap analysis in a VCN AOI SDA iteration.

skills/tao-route-visual-changenet-samples/SKILL.md

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# TAO VCN Sample Routing 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 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.

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