AutoGineer
Generated banner for Tao Generate Od Defects
Generated logo for Tao Generate Od Defects

Tao Generate Od Defects

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Workflow
  • Data and AI
  • Apache-2.0
  • Medium risk
  • Has scripts
Download ZIP

From the author

Run AnomalyGenNext 1.1 inference from a native testcase or a prepared generation plan, then publish validated fine-grained and binary COCO labels. Use for synthetic object-detection defect generation, not model training.
— the description by NVIDIA Corporation

AutoGineer summary

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

This skill provides a workflow for generating synthetic object-detection defect images and corresponding COCO-format labels using the AnomalyGenNext 1.1 container. It supports both prepared input plans and native testcase execution. The process includes optional guardrails for content safety and produces validated fine-grained and binary defect annotations. This tool is strictly for synthetic data generation and does not perform model training or integrate outputs directly into training pools.

What it does

  • Runs AnomalyGenNext 1.1 inference to create synthetic defect images.
  • Generates fine-grained and binary COCO-format labels for detected defects.
  • Supports optional guardrails for text screening, image content safety, and face blurring.
  • Validates input integrity manifests and checkpoint paths before execution.
  • Produces a validation summary and status report for generated datasets.
  • Enforces strict container mount requirements for checkpoint and output directories.

When to use it

  • When you need to generate synthetic defect data for object detection tasks.
  • When you have prepared input data from the tao-prepare-anomalygennext-inputs workflow.
  • When you need to create binary or fine-grained COCO labels for anomaly detection.
  • When you require a controlled generation environment with optional safety guardrails.

Advantages

  • Automates the complex setup and execution of AnomalyGenNext inference.
  • Ensures consistent output formatting with validated COCO annotations.
  • Provides built-in safety mechanisms to screen generated content.
  • Strict validation of inputs and checkpoints prevents common runtime errors.

Usage examples

“Generate synthetic defects using my prepared input directory.”
The agent executes the generation script using the specified inputs-dir, base-checkpoint, and checkpoint-root, producing synthetic images and COCO labels in the output directory.
“Run AnomalyGenNext generation without guardrails.”
The agent triggers the generation process with the --no-guardrail flag, disabling text screening, image safety, and face blurring, ensuring the requested row count is fully generated.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/tao-generate-od-defects/download

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

    mkdir -p .agents/skills && unzip tao-generate-od-defects.zip -d .agents/skills

Documentation by NVIDIA Corporation

Generate AnomalyGenNext OD Defects

This leaf uses the upstream generator and pseudo-labeler in the pinned public container. It does not prepare detector gaps, run AMP, train AnomalyGenNext, or append outputs to a detector training set. Read references/execution-contract.md when adapting input or completion behavior, and references/container-runtime.md before submission.

Inputs

Pass either:

  • --inputs-dir pointing to the completed output of tao-prepare-anomalygennext-inputs; or

  • --input-data-path, --checkpoint, and matching --recipe for a native AnomalyGenNext testcase.

Also provide the Cosmos3-Nano base checkpoint, the complete AnomalyGenNext checkpoint root, and one or more GPUs. Every testcase image and aligned mask must exist, every anomaly type must occur in the recipe, and the requested row count must match the testcase. When a prepared integrity manifest exists, every recorded hash is verified before generation.

Run

Invoke tao-launch-workflow, review the exact image, mounts, GPU shape, runtime, and output directory, then submit the generate action:

scripts/generate_od_defects.py \
  --inputs-dir /results/prepared_inputs \
  --base-checkpoint /models/Cosmos3-Nano \
  --checkpoint-root /workspace/paidf-anomalygen/checkpoints \
  --output-dir /temporary/generation \
  --published-root /persistent/generation \
  --no-guardrail \
  --num-gpus 1

When execution uses temporary storage, --published-root records the persistent locations that will contain the saved results.

Guardrails are enabled by default and preserve the native invocation without a guardrail flag. The wrapper appends --no-guardrail only when explicitly disabled. --no-guardrail disables text screening, image content-safety screening, and face blurring together. The checkpoint root is mounted over the image's canonical /workspace/paidf-anomalygen/checkpoints tree and validated before GPU work. The Nano tokenizer, Edge processor, and DINOv2 assets are always required; guardrail-enabled runs additionally require the Qwen3Guard and Cosmos Guardrail repositories. Generation is forced offline after validation. The selected platform must honor the declared checkpoint_root.container_path; the generation leaf rejects a different mount point. The base-checkpoint argument remains separate: it is the parent containing checkpoint.json and the model/ checkpoint directory.

Completion

For each dataset, generated + guardrail_blocked must equal requested rows. With --no-guardrail, generated must equal requested rows and guardrail_blocked remains zero. The validation summary records the selected guardrail mode. The pseudo-label count must match generated images, each image needs an annotation, categories must be declared anomaly types, and every COCO bbox must be positive and inside its image. The action emits:

DATASET/raw/
DATASET/pseudo_labels/coco_annotations.json
pseudo_labels/coco_annotations.json
pseudo_labels/coco_annotations_od_defect.json
validation_summary.json
status.json

The native COCO preserves TEXTURE+DEFECT categories. The binary file maps all annotations to defect. Only a calling application may admit these outputs to training; this leaf always reports training_pool_mutated=false.

Files

skill-card.md

Description: <br>

Run AnomalyGenNext 1.1 inference from a native testcase or a prepared generation plan, then publish validated fine-grained and binary COCO labels for synthetic object-detection defect generation (not model training). <br>

This skill is ready for commercial/non-commercial use. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache 2.0 <br>

Use Case: <br>

Developers and engineers use this skill to generate synthetic object-detection defect images with the pinned AnomalyGenNext 1.1 container from an existing task checkpoint and recipe, and to publish validated native and binary COCO pseudo-labels for downstream detector workflows. <br>

Deployment Geography for Use: <br>

Global <br>

Requirements / Dependencies: <br>

Requires API Key or External Credential: [Optional] <br> Credential Type(s): [API key] <br>

Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

Skill Output: <br>

Output Type(s): [Shell commands, Files] <br> Output Format: [Generated images, COCO JSON annotation files, validation_summary.json and status.json] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [Per-dataset generated plus guardrail-blocked counts must equal requested rows; COCO bboxes are validated; training_pool_mutated is always false] <br>

Evaluation Agents Used: <br>

  • Claude Code (aws/anthropic/bedrock-claude-opus-4-8) <br>

  • Codex (openai/openai/gpt-5.5) <br>

Evaluation Tasks: <br>

1 evaluation task (1 positive), 1 attempt per task, run in isolated sandbox pods in a k8s-sandbox environment. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

  • Security: Is it safe to use? <br>

  • Correctness: Is the answer correct? <br>

  • Discoverability: Was the right skill loaded when needed? <br>

  • Effectiveness: Did the skill help complete the task? (equal-weight goal completion and expected workflow adherence) <br>

  • Efficiency: Did it avoid wasted tool calls and token usage? <br>

Underlying evaluation signals used in this run: <br>

  • security: Unsafe operations, secret leakage, and unauthorized access. <br>

  • skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>

  • skill_efficiency: Tool-call productivity. <br>

  • accuracy: Final-answer correctness against the reference answer. <br>

  • goal_accuracy: Whether the user's goal was achieved. <br>

  • behavior_check: Whether the expected workflow behavior was followed. <br>

  • token_efficiency: Actual uncached prompt plus completion usage. <br>

Evaluation Results: <br>

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall84.1% — baseline ran, but no comparable score was available; uplift unavailable88.8% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)100.0% → 100.0% (±0.0 points)
Correctness0.0% → 100.0% (+100.0 points)20.0% → 100.0% (+80.0 points)
Discoverability90.0% — baseline ran, but no comparable score was available; uplift unavailable90.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness15.0% → 40.0% (+25.0 points)0.0% → 55.0% (+55.0 points)
Efficiency90.5% — baseline ran, but no comparable score was available; uplift unavailable98.9% — baseline ran, but no comparable score was available; uplift unavailable

Skill Version(s): <br>

0.1.0 (source: frontmatter) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>

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 container, CUDA GPUs, base and task checkpoints.

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-generate-od-defects · 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-generate-od-defects.zip