Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA-TAO/tao-skill-bank --skill tao-generate-od-defectsgit clone --depth 1 https://github.com/NVIDIA-TAO/tao-skill-bankWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-generate-od-defects)<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-generate-od-defects"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-generate-od-defects/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-generate-od-defects"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-generate-od-defects.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.00869 |
| Opus 5.5 | $0.00022 | $0.00348 |
| Sonnet 5.5 | $0.00011 | $0.00174 |
| Haiku 4.5 | $0.00005 | $0.00087 |
Grade A, and why
tao-generate-od-defects scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 22d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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-dirpointing to the completed output oftao-prepare-anomalygennext-inputs; or--input-data-path,--checkpoint, and matching--recipefor 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.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 22d ago Changed · +14 lines c64c49991fe4
- 1mo ago Changed · +6 lines edbb9cb049c4
- 1mo ago First seen · 74 lines · 54 tokens per session scan A 290484a86399
tao-generate-od-defects is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (97 stars, last pushed 5d ago), licensed Apache-2.0. It adds 54 tokens to every session and 869 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-10.
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