Tao Route Visual Changenet Samples
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Workflow
- Software development
- Apache-2.0
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- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill acts as a dispatcher within a Visual ChangeNet (VCN) AOI SDA pipeline. It processes the output of a gap analysis by filtering samples into subsets for k-NN Mining and AnomalyGen (Cosmos SDG) based on label compatibility. By creating auditable routing decisions, it ensures that every weak sample is either processed by an appropriate module or flagged for manual review, preventing silent data loss during the augmentation iteration.
What it does
- Filters gap analysis parquet files into module-specific subsets.
- Validates label eligibility against k-NN Mining source pools and AnomalyGen supported classes.
- Generates a human-readable summary of routing decisions for every label.
- Produces a structured routing report for auditability and troubleshooting.
- Identifies dropped labels that require configuration updates or data seeding.
When to use it
- Immediately after running gap analysis in a VCN AOI SDA iteration.
- When you need to split gap samples for k-NN mining and AnomalyGen processing.
- When you need to audit why certain defect labels are not being processed by downstream modules.
- To prepare the necessary input files for the next stage of the augmentation pipeline.
Advantages
- Ensures deterministic and auditable routing of weak samples.
- Prevents silent data loss by explicitly flagging unroutable labels.
- Provides clear recommendations for fixing configuration or data gaps.
- Maintains schema consistency for downstream augmentation modules.
Usage examples
- “Route VCN gap samples for the current experiment.”
- The skill processes the gap analysis parquet, creates filtered subsets for Mining and AnomalyGen, and generates a routing report.
- “Split AOI gaps for k-NN mining and AnomalyGen.”
- The skill evaluates the input labels against the source pool and AnomalyGen capabilities, writing the respective parquets and a summary file.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-route-visual-changenet-samples/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-route-visual-changenet-samples.zip -d .agents/skills
Documentation by NVIDIA Corporation
TAO VCN Sample Routing Skill
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill 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
SHIFTneighbors if the pool has noSHIFTrows.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<exp_dir>/rca_results/<timestamp>/kpi_gaps.parquetfromtao-analyze-gaps-visual-changenet). Required columns:filepath,label. Other columns (siamese_score,weakness) are preserved verbatim.source_pool_csv— VCN-format mining source pool CSV with alabelcolumn. Empty string or non-existent path is allowed; the mining subset will simply be empty in that case.Output directory — where the two routed parquets, the summary, and the report are written. Default: a timestamped folder under the gap-analysis result directory:
<rca_result_dir>/routing_results/<timestamp>/.anomalygen_supported_labels(optional) — override the default AnomalyGen-eligible label set. Default:{"PASS", "EXCESS_SOLDER", "MISSING", "BRIDGE"}. Warning: This must stay in sync withANOMALYGEN_SUPPORTED_LABELSinmdo-kratos-workflows/pipelines/sda/routing.pyand the AnomalyGen integration's actual generator coverage. Adding a new defect class to AnomalyGen means adding it here too.
Method
The whole skill is two .isin(...) masks against the uppercased label column.
Step 1 — Load and uppercase
df = pd.read_parquet(gaps_parquet)
labels_upper = df["label"].astype(str).str.upper()The match is case-insensitive for both module checks. The original label column is preserved unchanged in the output parquets — only the comparison key is uppercased.
Step 2 — Mining subset
if source_pool_csv and os.path.isfile(source_pool_csv):
pool_df = pd.read_csv(source_pool_csv)
pool_labels = {str(l).upper() for l in pool_df["label"].unique()}
mn_mask = labels_upper.isin(pool_labels)
mn_df = df[mn_mask]
else:
pool_missing = True
pool_labels = set()
mn_df = df.iloc[0:0] # empty, but with the same schema
mn_df.to_parquet(mining_gaps_parquet, index=False)If the pool CSV is missing or empty, the mining subset is an empty DataFrame with the same columns as the input so downstream readers don't crash on schema mismatch. Flag this case in the summary.
Step 3 — AnomalyGen subset
ANOMALYGEN_SUPPORTED = {"PASS", "EXCESS_SOLDER", "MISSING", "BRIDGE"}
ag_mask = labels_upper.isin(ANOMALYGEN_SUPPORTED)
ag_df = df[ag_mask]
ag_df.to_parquet(anomalygen_gaps_parquet, index=False)Rows whose label is in the AnomalyGen-supported set are written verbatim to anomalygen_gaps.parquet. The schema matches the input parquet exactly — downstream AnomalyGen (Cosmos SDG) needs no other changes.
Step 4 — Per-label routing breakdown
For every distinct label in the input gaps parquet (uppercased), record:
count— how many rows have this labelmining— yes if the label is inpool_labels, otherwise noanomalygen— yes if the label is inANOMALYGEN_SUPPORTED, otherwise no
A label can route to both modules (e.g. PASS rows route to AnomalyGen, and if the source pool also contains PASS rows they route to Mining too). A label can also route to none — flag those, since they are silently dropped and may signal a configuration mismatch.
Write the breakdown to routing_summary.txt. The format mirrors the reference component exactly:
Weak-sample routing summary
Total weak samples: <N>
Mining subset: <N_mn> -> <mining_gaps_parquet>
AnomalyGen subset: <N_ag> -> <anomalygen_gaps_parquet>
[If pool missing:]
No source pool CSV at '<path>'; mining subset is empty.
Per-label breakdown (count, mining, anomalygen):
PASS: 50 (mining=yes, anomalygen=yes)
MISSING: 32 (mining=no, anomalygen=yes)
SHIFT: 14 (mining=yes, anomalygen=no)
EXCESS_SOLDER: 9 (mining=yes, anomalygen=yes)
...Step 5 — Sanity checks
After both subsets are written, verify:
The sum of subset sizes is not required to equal
len(df)— overlap is allowed (a label can route to both modules). What matters is that every input row appears in at least one subset, OR appears in the "none" list with an explicit reason.If
len(mn_df) == 0andlen(ag_df) == 0, something is wrong — flag prominently in the report.If an entire label group routes to no module, the
Recommended Actionssection must call this out so the user can either seed the source pool with that label or extend AnomalyGen's supported set.
Reference Python Recipe
This is the exact computation, lifted from mdo-kratos-workflows/pipelines/sda/routing.py. Run as a single Python script via Bash; it produces every artifact except the report.
import os
import pandas as pd
ANOMALYGEN_SUPPORTED = {"PASS", "EXCESS_SOLDER", "MISSING", "BRIDGE"}
df = pd.read_parquet(gaps_parquet)
labels_upper = df["label"].astype(str).str.upper()
# Mining subset
pool_missing = False
if source_pool_csv and os.path.isfile(source_pool_csv):
pool_df = pd.read_csv(source_pool_csv)
pool_labels = {str(l).upper() for l in pool_df["label"].unique()}
mn_mask = labels_upper.isin(pool_labels)
mn_df = df[mn_mask]
else:
pool_missing = True
pool_labels = set()
mn_df = df.iloc[0:0]
os.makedirs(os.path.dirname(mining_gaps_parquet) or ".", exist_ok=True)
mn_df.to_parquet(mining_gaps_parquet, index=False)
# AnomalyGen subset
ag_mask = labels_upper.isin(ANOMALYGEN_SUPPORTED)
ag_df = df[ag_mask]
os.makedirs(os.path.dirname(anomalygen_gaps_parquet) or ".", exist_ok=True)
ag_df.to_parquet(anomalygen_gaps_parquet, index=False)
# Per-label breakdown
summary_lines = [
"Weak-sample routing summary",
f"Total weak samples: {len(df)}",
f"Mining subset: {len(mn_df)} -> {mining_gaps_parquet}",
f"AnomalyGen subset: {len(ag_df)} -> {anomalygen_gaps_parquet}",
"",
]
if pool_missing:
summary_lines.append(f"No source pool CSV at {source_pool_csv!r}; mining subset is empty.")
summary_lines.append("")
summary_lines.append("Per-label breakdown (count, mining, anomalygen):")
label_counts = labels_upper.value_counts()
for label, count in label_counts.items():
in_mn = (not pool_missing) and label in pool_labels
in_ag = label in ANOMALYGEN_SUPPORTED
summary_lines.append(
f" {label}: {count} "
f"(mining={'yes' if in_mn else 'no'}, "
f"anomalygen={'yes' if in_ag else 'no'})"
)
summary_text = "\n".join(summary_lines) + "\n"
os.makedirs(logs_dir, exist_ok=True)
with open(os.path.join(logs_dir, "routing_summary.txt"), "w", encoding="utf-8") as f:
f.write(summary_text)
print(summary_text.strip())Outputs
Write everything into a timestamped folder. Any runtime packaging hook may add routing_config/ and session-capture artifacts after Routing_Report.md is written.
<output_dir>/routing_results/YYYY-MM-DD_HHMMSS/
├── Routing_Report.md # Full routing report
├── mining_gaps.parquet # Subset routed to k-NN Mining
├── anomalygen_gaps.parquet # Subset routed to AnomalyGen (Cosmos SDG)
├── routing_summary.txt # Plain-text per-label breakdown
├── routing_config/ # Auto-copied by hook
└── session log/artifacts # Optional, runtime-dependent packaging captureAt the start of the run, get the real timestamp by running date +%Y-%m-%d_%H%M%S in Bash. If the user specifies a custom output path, use it directly but maintain the internal layout.
Report Structure
Keep the report short (400–800 words). Routing is a deterministic decision; the value is making the decisions auditable, not narrative.
# VCN Routing Report: <Iteration / Experiment Name>
## 1. Verdict
- Total weak samples in: <N>
- Mining subset: <N_mn> rows → `mining_gaps.parquet`
- AnomalyGen subset: <N_ag> rows → `anomalygen_gaps.parquet`
- Source pool present? <yes/no — and the path>
- One-line headline: "<X> labels routed, <Y> labels dropped (no module accepted)"
## 2. Inputs
| Input | Path | Notes |
|-------|------|-------|
| gaps_parquet | … | rows=<N>, columns=<col list> |
| source_pool_csv | … | rows=<M> or "not provided" / "missing" |
## 3. Per-Label Routing Decisions
| Label | Count in gaps | In source pool? | Mining? | AnomalyGen? | Routed To |
|-------|----------------|------------------|----------|--------------|-----------|
(One row per distinct label in `gaps_parquet`, uppercased. `Routed To` is one of:
`mining only`, `anomalygen only`, `mining+anomalygen`, `neither (DROPPED)`.
Use `neither (DROPPED)` whenever no module accepted the label. Sort by count descending.)
## 4. Module-Level Summaries
### 4.1 k-NN Mining
- Pool labels (from source_pool_csv): <list, or "pool missing">
- Labels accepted from input: <list>
- Total rows routed: <N_mn>
- Per-label row counts: <breakdown>
### 4.2 AnomalyGen (Cosmos SDG)
- Eligible labels (configured): PASS, EXCESS_SOLDER, MISSING, BRIDGE
- Labels accepted from input: <list>
- Total rows routed: <N_ag>
- Per-label row counts: <breakdown>
## 5. Dropped Labels (routed to NEITHER module)
| Label | Count | Why dropped | Suggested fix |
|-------|-------|-------------|----------------|
(Empty table is OK and means no labels were dropped. If non-empty, every row needs a
"why" — typically one of: "not in source pool AND not in AnomalyGen supported set",
"source pool missing entirely AND label not in AnomalyGen set", "label name doesn't
match any module's expected canonicalization".)
## 6. Recommended Actions
1. **If any labels are dropped**: seed the source pool with that label, OR extend
`ANOMALYGEN_SUPPORTED_LABELS` (and the AnomalyGen generator coverage).
2. **If source pool is missing**: provide `source_pool_csv` to enable the Mining branch.
Without it, half of the augmentation pipeline is dark.
3. **If AnomalyGen subset is empty**: gap analysis only surfaced labels AnomalyGen cannot
generate; rely on Mining for this iteration, or extend the AnomalyGen integration.
4. **If both subsets are empty**: stop the SDA iteration. Nothing downstream can run.Execution Order
Run
date +%Y-%m-%d_%H%M%Sto get the timestamp; create<output_dir>/routing_results/<timestamp>/.Run the Python recipe (Steps 1–4) to produce
mining_gaps.parquet,anomalygen_gaps.parquet, androuting_summary.txt. Print summary stats to stdout so the script-check hook can verify it ran.Build the per-label decision table by reading both parquets and computing the routed-to verdict per label.
Write
Routing_Report.mdlast — writing it triggers the packaging hook, which copies session logs and skill config alongside.
Files
- ATTRIBUTION.mdAdded by AutoGineer972 B
- BENCHMARK.md7.1 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md12.4 KiB
- config/skillspector-baseline.yaml1.7 KiB
- evals/evals.json849 B
- hooks/_parse-stdin.shScript1.4 KiB
- hooks/routing-artifacts-check.shScript3.5 KiB
- hooks/routing-coverage-check.shScript2.8 KiB
- hooks/routing-package.shScript2.5 KiB
- hooks/routing-script-check.shScript3.1 KiB
- hooks/routing-section-check.shScript3.4 KiB
- skill-card.md4.0 KiB
- skill.oms.sig6.2 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Needs Python
The author says: Standalone — no external runtime requirements.
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Attribution and licence
- Created by
- NVIDIA Corporation
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/tao-route-visual-changenet-samples · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
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