Tao Generate Od Defects

NVIDIAOfficialCreated by NVIDIA Corporation

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

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

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-generate-od-defects/download

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

ATTRIBUTION.mdAdded by AutoGineer939 B
BENCHMARK.md6.9 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md3.8 KiB
config/skillspector-baseline.yaml1.4 KiB
evals/evals.json901 B
references/container-runtime.md2.9 KiB
references/execution-contract.md4.5 KiB
references/skill_info.yaml1.8 KiB
scripts/generate_od_defects.pyScript15.0 KiB
scripts/tests/test_generate_od_defects.pyScript10.9 KiB
skill-card.md4.7 KiB
skill.oms.sig5.9 KiB

BENCHMARK.md

Skill Benchmark: tao-generate-od-defects

✅ Overall verdict: PASS — Recommended for publication

Publication Recommendation

Recommended for publication based on the completed evaluation evidence in this report.

Evaluation Metadata

Skill: tao-generate-od-defects
Evaluation date: 2026-10-16
Evaluator version: 1.5.6
Agents: Claude Code (aws/anthropic/bedrock-claude-opus-4-8), Codex (openai/openai/gpt-5.5)
Tasks: 1 evaluation tasks (1 positive)
Dataset digest: sha256:2b3faca7598b4464583dbea8e5f36e64e696ef58add6e39f847cca97a2f49fef (skill-evaluator-dataset-snapshot/1)
Attempts per task: 1
Environment: k8s-sandbox
Tier 2 evidence: required for publication
Tier 3 evidence: required for publication

Each task attempt ran in its own isolated sandbox pod.

What This Report Answers

The three-tier evaluation checks whether the skill:

is safe to use;
produces correct answers;
is discovered and activated when needed;
helps the agent complete the user's goal and expected workflow; and
avoids wasted skill and tool usage.

Results at a Glance

Measure	Claude Code (Baseline → Skill Uplift)	Codex (Baseline → Skill Uplift)
Overall	84.1% — baseline ran, but no comparable score was available; uplift unavailable	88.8% — baseline ran, but no comparable score was available; uplift unavailable
Security	100.0% → 100.0% (±0.0 points)	100.0% → 100.0% (±0.0 points)
Correctness	0.0% → 100.0% (+100.0 points)	20.0% → 100.0% (+80.0 points)
Discoverability	90.0% — baseline ran, but no comparable score was available; uplift unavailable	90.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness	15.0% → 40.0% (+25.0 points)	0.0% → 55.0% (+55.0 points)
Efficiency	90.5% — baseline ran, but no comparable score was available; uplift unavailable	98.9% — baseline ran, but no comparable score was available; uplift unavailable

How to read this table: baseline is the same task attempted without the target skill. Scores are rounded to one decimal; threshold-adjacent values use additional precision so their displayed band matches the verdict. Uplift is derived from those displayed scores and shown in percentage points.

Example: 47.0% → 92.0% (+45.0 points) means the skill-assisted run scored 92.0%, 45.0 percentage points above its 47.0% no-skill baseline.

Token Usage

Actual Tier 3 execution usage is reported for every observed agent/case pair and both conditions.

Agent	Dataset case	With skill	Without skill	Delta	Change	Coverage
claude-code	All cases	448,124	212,062	+236,062	+111.32%	skill 1/1; base 1/1
claude-code	generate-anomalygennext-od-defects	448,124	212,062	+236,062	+111.32%	skill 1/1; base 1/1
codex	All cases	28,247	13,657	+14,590	+106.83%	skill 1/1; base 1/1
codex	generate-anomalygennext-od-defects	28,247	13,657	+14,590	+106.83%	skill 1/1; base 1/1
ALL AGENTS	Dataset aggregate	476,371	225,719	+250,652	+111.05%	skill 2/2; base 2/2

Prompt tokens include cached reads, so total tokens are prompt + completion (cached is not added twice). The Efficiency score uses (prompt - cached) + completion. N/A means the relevant trajectory counters were not available; coverage is never estimated.

Tier Status

Tier	Purpose	Status	Evidence
Tier 1	Static validation	PASSED WITH OBSERVATIONS	11 validator(s); 26 finding(s)
Tier 2	Semantic deduplication	PASSED	2 validator(s); 0 finding(s)
Tier 3	Live agent evaluation	PASS	2 agent(s); 1 task(s)

Findings and Observations

<details> <summary>Show detailed findings and successful checks</summary>

MEDIUM QUALITY/quality_correctness: No documented scripts in table format (skills/data/tao-generate-od-defects/SKILL.md)
MEDIUM QUALITY/quality_correctness: Instructions don't mention 'run_script' (skills/data/tao-generate-od-defects/SKILL.md)
MEDIUM QUALITY/quality_correctness: SKILL_SPEC recommended field missing: 'metadata.tags' (skills/data/tao-generate-od-defects/SKILL.md)
MEDIUM QUALITY/quality_efficiency: Deeply nested references in container-runtime.md (skills/data/tao-generate-od-defects/SKILL.md)
MEDIUM SCHEMA/frontmatter_field_placement: Root field 'tags' is ignored; use 'metadata.tags' (skills/data/tao-generate-od-defects/SKILL.md)
21 additional finding(s) are available in the full evaluation artifacts.

</details>

Scoring Methodology

<details> <summary>Show dimension definitions, source signals, and thresholds</summary>

Dimension	Question	Scored signals
Security	Is it safe to use?	`security` (100%)
Correctness	Is the answer correct?	`accuracy` (100%)
Discoverability	Was the right skill loaded when needed?	`skill_execution` (100%)
Effectiveness	Did the skill help complete the task?	`goal_accuracy` (50%) + `behavior_check` (50%)
Efficiency	Did it avoid wasted tool calls and token usage?	`skill_efficiency` (50%) + `token_efficiency` (50%)

Dimension bands: PASS at 50% or above; NEUTRAL from 40% to below 50%; FAIL below 40%.
Overall Tier 3 lift: PASS at +5 points or more; FAIL at -10 points or less; values between those bands are NEUTRAL.
Overall verdict: PASS only when every configured dimension passes for at least one supported agent. Lift is reported as diagnostic evidence and does not override this gate.
The 50% attempt pass threshold is a separate per-task gate; it is not the dimension pass threshold.
Effectiveness is the equal-weight mean of goal completion (goal_accuracy) and expected workflow adherence (behavior_check).
Efficiency is 50% tool-call productivity (the backward-compatible skill_efficiency wire id) and 50% token_efficiency. Positive-case skill routing is scored under Discoverability, not Efficiency; a negative case without a routing target is N/A. N/A sources are omitted, remaining weights are renormalized, and the dimension is marked partial.

Signals present in this run:

security (Security): unsafe operations, secret leakage, and unauthorized access.
skill_execution (Skill Execution): whether the expected skill was selected, decoys were avoided, and the workflow executed.
skill_efficiency (Tool Productivity): tool-call productivity (legacy wire id; routing is scored under Discoverability).
accuracy (Accuracy): final-answer correctness against the reference answer.
goal_accuracy (Goal Accuracy): whether the user's goal was achieved.
behavior_check (Behavior Check): whether the expected workflow behavior was followed.
token_efficiency (Token Efficiency): actual uncached prompt plus completion usage (50% of Efficiency).

</details>

Freshness

Regenerate this benchmark when the skill, evaluation dataset, target agent/model, evaluator version, environment, or scoring policy changes.

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

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-generate-od-defects.zip