Generated banner for Tao Route Visual Changenet Samples
Generated logo for Tao Route Visual Changenet Samples

Tao Route Visual Changenet Samples

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Workflow
  • Software development
  • Apache-2.0
  • Medium risk
  • Has scripts

From the author

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.
— the description by NVIDIA Corporation

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

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/tao-route-visual-changenet-samples/download
  2. Unzip 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-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.


Inputs

  1. gaps_parquet — the gap-analysis output (typically <exp_dir>/rca_results/<timestamp>/kpi_gaps.parquet from tao-analyze-gaps-visual-changenet). Required columns: filepath, label. Other columns (siamese_score, weakness) are preserved verbatim.

  2. source_pool_csv — VCN-format mining source pool CSV with a label column. Empty string or non-existent path is allowed; the mining subset will simply be empty in that case.

  3. 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>/.

  4. anomalygen_supported_labels (optional) — override the default AnomalyGen-eligible label set. Default: {"PASS", "EXCESS_SOLDER", "MISSING", "BRIDGE"}. Warning: This must stay in sync with ANOMALYGEN_SUPPORTED_LABELS in mdo-kratos-workflows/pipelines/sda/routing.py and 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 label

  • mining — yes if the label is in pool_labels, otherwise no

  • anomalygen — yes if the label is in ANOMALYGEN_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) == 0 and len(ag_df) == 0, something is wrong — flag prominently in the report.

  • If an entire label group routes to no module, the Recommended Actions section 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 capture

At 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

  1. Run date +%Y-%m-%d_%H%M%S to get the timestamp; create <output_dir>/routing_results/<timestamp>/.

  2. Run the Python recipe (Steps 1–4) to produce mining_gaps.parquet, anomalygen_gaps.parquet, and routing_summary.txt. Print summary stats to stdout so the script-check hook can verify it ran.

  3. Build the per-label decision table by reading both parquets and computing the routed-to verdict per label.

  4. Write Routing_Report.md last — writing it triggers the packaging hook, which copies session logs and skill config alongside.

Files

LICENSE

Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

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.

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

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)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA Corporation or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-route-visual-changenet-samples.zip