tao-run-deft-od-aoi

tao-run-deft-od-aoi is a skill for Claude Code from NVIDIA-TAO/tao-skill-bank. It costs 81 tokens per session (4,042 once invoked), scanned A, original, Apache-2.0.

A fixed workflow for industrial visual inspection using RT-DETR, an object-detection model that locates defects in images. It measures results on separate data, finds missed or wrongly detected cases, adds approved real images, retrains, and selects by AP50, a detection accuracy score.

In plain words
What is it for?
Use it for automated optical inspection of industrial images when the task is detecting one defect class. It is not a general multi-class object-detection workflow.
Why use it?
It provides a repeatable loop for improving defect detection while keeping evaluation images separate from training data.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the tao-skills plugin — 82 skills shipped together , and of tao-skill-bank

Good fit Use it for automated optical inspection of industrial images when the task is detecting one defect class. It is not a general multi-class object-detection workflow.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/nvidia-tao/tao-skill-bank/tao-run-deft-od-aoi
Install

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.

Any agent
npx skills add NVIDIA-TAO/tao-skill-bank --skill tao-run-deft-od-aoi
Clone the repo
git clone --depth 1 https://github.com/NVIDIA-TAO/tao-skill-bank

Made for: Claude Code.

Or install tao-skills, the plugin that ships this one along with the rest of its 82 skills.

Wrote 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.

agentmods badge for tao-run-deft-od-aoi

README.md
[![agentmods](https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-run-deft-od-aoi/github.svg)](https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-run-deft-od-aoi)
Your own site
<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-run-deft-od-aoi"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-run-deft-od-aoi/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.

agentmods 80×15 button for tao-run-deft-od-aoi

Your own site · 80×15
<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-run-deft-od-aoi"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-run-deft-od-aoi.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 81 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,042 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00081 $0.04042
Opus 5.5 $0.00032 $0.01617
Sonnet 5.5 $0.00016 $0.00808
Haiku 4.5 $0.00008 $0.00404

Measured 15d ago against content hash b24a19b5ee80, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

tao-run-deft-od-aoi 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 15d ago.

The scan reads SKILL.md. This mod also ships 28 executable files (scripts/admit_deft_od_aoi_coco.py, scripts/commit_deft_od_aoi_stage.py, scripts/deft_od_aoi_round_robin_admission.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/applications/tao-run-deft-od-aoi/SKILL.md · 362 lines

How it starts

The opening of the file, as written. The whole thing — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TAO DEFT OD AOI

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).

This application is a disk-backed RT-DETR loop for one foreground class, defect. Its core is real-data-only; AnomalyGenNext synthesis is an optional route with separate preparation, generation, and admission gates.

References

Read only the references needed for the current stage:

  • intake and launch: references/defaults.md, references/data-contract.md, references/source-manifest.md, and references/preflight.md;
  • orchestration: references/pipeline.md and references/scripts-and-agents.md;
  • gaps and retrieval: references/gap-routing.md and references/tao-analyze-gaps-od-map.md;
  • training and selection: references/training-policy.md;
  • optional synthesis: references/anomalygen-pool.md.

Start

Select an installed platform, read its skill, then invoke tao-launch-workflow. The single launch review must include the four normalized COCO roles, trainable RT-DETR base checkpoint, maximum iterations, image and Data Services containers, GPU shape, and expected runtime. After approval, prepare source COCO files when needed:

scripts/prepare_deft_od_aoi_sources.py \
  --manifest /data/dataset_sources.json --check-only

scripts/prepare_deft_od_aoi_sources.py \
  --manifest /data/dataset_sources.json \
  --output-dir /new/results/normalized

--check-only runs without a container. Materialization must run in the pinned TAO Data Services image because it delegates each role's canonical COCO merge to the existing annotations merge action. The application then validates the merged role contracts before emitting sources.json. Include --gpus when launching the materialization container, as documented in references/source-manifest.md.

The user-facing manifest calls the held-out input benchmark. The second command maps it to the existing internal kpi role and emits sources.json with the canonical kpi, test, real, and clean mapping. Copy assets/default_policy.yaml, use that mapping for sources, fill the other required values, and initialize once:

Read the full file on GitHub · 362 lines

Files

What ships with it

42 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.

Changes

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.

  1. 15d ago Changed · +2 lines b24a19b5ee80
  2. 19d ago Changed · +5 lines d74dcbe14c06
  3. 21d ago Changed · +35 lines 2306a9d3c313
  4. 22d ago Changed · +29 lines 45679ceb134e
  5. 24d ago Changed · +36 lines b6c1c47dfd6b
  6. 1mo ago Changed · +12 lines 638db71b310b
  7. 1mo ago Changed · +21 lines 4d5b19262bb5
  8. 1mo ago Changed · +2 lines ad8ae7601ece
  9. 1mo ago Changed · +137 lines e03be2509d9c
  10. 1mo ago First seen · 83 lines · 81 tokens per session scan A d4fb6286350a

Subscribe to this mod's changes

tao-run-deft-od-aoi 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 81 tokens to every session and 4,042 once invoked, about $0.0003 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.

Related

Other skills, from other repositories

fair-esm2

Embed proteins with Meta AI's ESM-2 (fair-esm package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.

UnicomAI/wanwu · 66 tokens

evo2

Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream classification, (3) Generating DNA conditioned on a prefix, (4) Scoring…

UnicomAI/wanwu · 83 tokens

openfold3

Structure prediction using OpenFold3, an open-weights PyTorch reproduction of AlphaFold3 from the AlQuraishi Lab. Use this skill when predicting protein/nucleic-acid/ligand complex structures with an Apache-2.0-licensed AF3 reimplementation.

UnicomAI/wanwu · 60 tokens

boltz

Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or CCD ligand, or to get an open-source AlphaFold3 alternative…

UnicomAI/wanwu · 88 tokens

scgpt

Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3) Gene-level representation for perturbation/GRN tasks. For…

UnicomAI/wanwu · 89 tokens

solublempnn

Inverse-fold a backbone with SolubleMPNN — ProteinMPNN retrained on a soluble-PDB subset (Dauparas et al. 2022) — for sequences biased toward cytosolic expression and reduced aggregation. Reach for this skill when designs from vanilla ProteinMPNN are aggregating or going to inclusion bodies, when redesigning a…

UnicomAI/wanwu · 96 tokens