Tao Prepare Anomalygennext Inputs

NVIDIAOfficialCreated by NVIDIA Corporation

Skill
Workflow
Software development
Apache-2.0
Medium risk
Has scripts

From the author

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.

— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured workflow to prepare data for the AnomalyGenNext pipeline. It processes false-negative (FN) object detection results by isolating masks, selecting deterministic donor masks, and generating embedding specifications. The workflow ensures data integrity by freezing configurations, validating mask eligibility, and maintaining a strict input contract for downstream embedding and automatic mask placement (AMP) tasks. It is designed to handle complex data preparation without modifying the original source pool, ensuring reproducibility across the generation pipeline.

What it does

Freezes eligible false-negative identities and isolates masks to their bounding boxes.
Selects deterministic same-type donor masks for each false negative.
Generates embedding specifications for clean images and false-negative inputs.
Executes automatic mask placement (AMP) to validate mask branches.
Produces a final integrity manifest and generation plan for valid pairs.
Records skip reasons for ineligible false negatives to maintain auditability.

When to use it

When you need to prepare false-negative gaps for AnomalyGenNext generation.
When you must turn box-level false-negative data into a frozen, pair-preserving preparation plan.
When you need to validate mask eligibility before running image generation.
When you are preparing inputs for downstream embedding and AMP tasks.

Advantages

Ensures deterministic and reproducible data preparation.
Maintains strict input contracts to prevent configuration drift.
Provides detailed reporting on skipped items and failure reasons.
Preserves original source data by avoiding any mutation of the training pool.
Supports independent processing of eligible false negatives.

Usage examples

“Prepare my object detection false negatives for AnomalyGenNext using the default filtering configuration.”: The skill executes the preparation script, validates the input parquet, and generates the necessary embedding specs and input contract for the specified dataset.
“Run the automatic mask placement for my prepared AnomalyGenNext inputs.”: The skill triggers the AMP process, verifying the frozen configuration and checkpoint paths to produce the final testcase and candidate parquet files.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-prepare-anomalygennext-inputs/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip tao-prepare-anomalygennext-inputs.zip -d .agents/skills

Documentation by NVIDIA Corporation

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/default_filtering.yaml. The gap parquet must contain:

image_id filepath gap_type bbox class split
dataset_id texture_id defect_class anomaly_type fn_mask_source

Identity is explicit: anomaly_type must equal texture_id+defect_class. 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 no_clean_reference_images; 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 defect_spec.jsonl must contain its placement definition. Text-routed definitions require a nonempty roi_prompt_defect_location. The pool layout is:

POOL/TEXTURE/clean_image/*
POOL/TEXTURE/mask/DEFECT/*

Action

Run through the selected platform after the common launch review:

scripts/prepare_anomalygennext_inputs.py \
  --config /path/to/filtering.yaml \
  --pool-dataset-root /path/to/anomalygen_pool \
  --output-dir /new/result/root

The output root must not exist. The action emits fn_queries.parquet, selected_fn_queries.parquet, mask_selection.parquet, clean_pool.parquet, two tao-generate-image-embeddings specs, the copied filtering config, and input_contract.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.

If no FN survives, preparation emits only a SKIPPED input_contract.json with reason no_eligible_false_negatives. Detailed skip_counts, per-FN records, and warning evidence distinguish missing clean references from mask eligibility failures. Do not run embeddings or AMP for that typed no-op, and do not represent it as generation success. A partial skip emits warning evidence while specs contain only eligible FNs.

pool_dataset_root is one user-level folder input for the workflow. The platform stages it once and retains the binding between its source and the compute-visible path passed to preparation. Preparation freezes that compute path into filtering_config.yaml, every clean filepath in clean_pool.parquet, and input_contract.json. The contract declares the same typed folder as a read-only downstream input for clean-image embedding and AMP. Each later container must remount the original source at that exact compute path; the generated script arguments are platform plumbing, not new user choices.

Run both emitted specs through tao-generate-image-embeddings, preserving the same encoder. The clean-image embedding action must consume input_contract.json.downstream_inputs.clean_embeddings so the pool paths in its parquet resolve. The FN embedding action likewise needs mounts for the source-image paths already carried by its input parquet. Place both outputs under embeddings/ as named by the specs, then submit the run_amp action:

scripts/run_anomalygennext_amp.py \
  --prepared-root /temporary/execution/root \
  --published-root /persistent/result/root \
  --pool-dataset-root /path/to/anomalygen_pool \
  --checkpoint-root /workspace/paidf-anomalygen/checkpoints

run_amp always loads the frozen config below --prepared-root; it accepts no second config. Its typed pool argument confirms that the platform remounted the folder at the path already frozen into the config and parquets.

This writes knn_candidates.parquet, the native AMP request, and amp/testcase.jsonl. When execution uses temporary storage, --published-root records the persistent locations that will contain the saved results. The complete checkpoint root is a required typed input mounted at the image's canonical /workspace/paidf-anomalygen/checkpoints path. Before GPU work, run_amp verifies the pinned Qwen cache, the configured amp.model_id as a complete direct-local model or Hugging Face snapshot, and facebook/sam2.1-hiera-large/sam2.1_hiera_large.pt; a separately mounted SAM2 file does not satisfy the native runtime contract. The selected platform must honor the declared checkpoint_root.container_path; the leaf rejects a different mount point. Finalize the saved output in that persistent result root:

scripts/finalize_anomalygennext_inputs.py --prepared-root /existing/result/root

The finalizer writes pair status, selected pairs, copied aligned masks, per-dataset testcase and provenance JSONL, and a generation plan plus integrity manifest. A clean image is unique only within one FN; separate FNs may reuse it. Platform skills own staging and cache placement.

Gates

Preserve each box-level FN as a distinct fn_id.
Produce exactly two masks per FN: isolated FN and deterministic same-type.
Skip one invalid FN without aborting other eligible FNs.
Embed a repeated source image once while preserving every FN query row.
Require successful, nonempty, non-full-image aligned masks for both branches.
Never infer an anomaly type or placement prompt.
Refuse output reuse and never mutate a training pool.
Platform skills own transient staging, caches, and copy-back.

Read references/input-contract.md when authoring the normalized parquet or dataset mapping.

Files

ATTRIBUTION.mdAdded by AutoGineer969 B
BENCHMARK.md7.0 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md6.5 KiB
assets/default_filtering.yaml1.3 KiB
config/skillspector-baseline.yaml3.0 KiB
evals/evals.json931 B
references/input-contract.md5.4 KiB
references/skill_info.yaml2.1 KiB
scripts/finalize_anomalygennext_inputs.pyScript9.6 KiB
scripts/prepare_anomalygennext_inputs.pyScript17.5 KiB
scripts/run_anomalygennext_amp.pyScript12.6 KiB
scripts/tests/test_finalize_anomalygennext_inputs.pyScript4.5 KiB
scripts/tests/test_prepare_anomalygennext_inputs.pyScript15.6 KiB
scripts/tests/test_run_anomalygennext_amp.pyScript11.5 KiB
skill-card.md5.0 KiB
skill.oms.sig7.0 KiB

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: Requires the AnomalyGenNext 1.1 image, pandas, pyarrow, NumPy, Pillow, and PyYAML.

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-prepare-anomalygennext-inputs · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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-prepare-anomalygennext-inputs.zip