What is tao-mine-aoi-images?

An image-mining workflow that represents target and source images as numerical embeddings, finds similar source images, and removes samples already chosen in earlier iterations.

Use it after routing visual-change samples to find similar source images for VCN AOI training iterations.

It helps build a fresh image-augmentation queue without repeatedly selecting the same examples. The workflow also makes the order of processing steps explicit.

tao-mine-aoi-images — Skill for Claude Code by NVIDIA-TAO

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-mine-aoi-images

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 76 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-mine-aoi-images/github.svg)](https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-mine-aoi-images)

Your own site

<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-mine-aoi-images"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-mine-aoi-images/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/nvidia-tao/tao-skill-bank/tao-mine-aoi-images"><img src="https://agentmods.dev/badge/skills/nvidia-tao/tao-skill-bank/tao-mine-aoi-images.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 3,586 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. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 8 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

high Privilege Escalation · line 62
Potential security issue detected. Manual review is recommended.

Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
high YARA Match · line 67
YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).

Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
medium MCP Rug Pull · line 27
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123
medium MCP Rug Pull · line 29
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123
medium MCP Rug Pull · line 55
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123
medium MCP Rug Pull · line 62
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123
medium MCP Rug Pull · line 169
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123
medium MCP Rug Pull · line 171
Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.

Fix: Pin the image: image:tag or image@sha256:abc123

How audits are shown

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.

Model	Per session	Once invoked
Fable 5.1	$0.00079	$0.03586
Opus 5	$0.00039	$0.01793
Sonnet 5	$0.00016	$0.00717
Haiku 4.5	$0.00008	$0.00359

Measured 8d ago against content hash e26f70f9eb61, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

tao-mine-aoi-images 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 8d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (hooks/_parse-stdin.sh, hooks/mining-artifacts-check.sh, hooks/mining-package.sh, …), 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/data/tao-mine-aoi-images/SKILL.md · 175 lines

How it starts

The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.

DEFT Mining and Embedding Skill

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).

You are the operator of the DEFT embed-then-mine workflow for VCN AOI. Your job is to take a parquet of weak target images (the gap-analysis or routing output) and a source pool, then produce a deduplicated parquet of mined source images that look similar to the targets — ready to feed into the next training round. Iterative DEFT callers also run the bundled history-aware post-processing step so a sample selected in an earlier iteration is not selected again.

The workflow is fixed and deterministic: embed the targets, embed the source pool, mine nearest neighbours, then (for iterative workflows) remove previously mined samples. Each GPU step's output parquet is the next step's input; history-aware selection is a host-side post-processing step, not another k-NN search. There is no clustering pass or human-in-the-loop selection — depth comes from picking the right encoder and a topn wide enough to leave novel candidates after history filtering.

The whole skill is a thin wrapper around three direct docker run invocations against the pinned TAO data-services image plus one optional bundled host-Python post-processor for iterative history (see versions.yaml, resolved at runtime in Setup). The container's entrypoint takes <category> <action> -e <spec.yaml> [hydra overrides...] — pass embedding image_embeddings -e <embedding_spec.yaml> … for embedding and tmm nearest_neighbors -e <mining_spec.yaml> … for mining. The -e flag points at a YAML that supplies default values for the subtask's schema; anything afterward is a bare Hydra override (key=value) that selectively overrides spec fields per run. (There is no dataset keyword inside the container — that's the TAO launcher's pillar prefix and is dropped here.) Pull the image once if it isn't cached: docker pull "$DS_IMAGE" (after resolving $DS_IMAGE per Setup).

Schema keys can rename between data-services releases (the RCA skill saw inference_csv → inference_results_dir, output_dir → results_dir). When in doubt, introspect the actual schema once per image: docker run --rm "$DS_IMAGE" embedding image_embeddings --cfg=job and ... tmm nearest_neighbors --cfg=job.

Read the full file on GitHub · 175 lines

Files

What ships with it

15 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.

8d ago Changed e26f70f9eb61
12d ago First seen · 175 lines · 79 tokens per session scan A 67f958f28947

Subscribe to this mod's changes

tao-mine-aoi-images is a skill published in the GitHub repository NVIDIA-TAO/tao-skill-bank (88 stars, last pushed today), licensed Apache-2.0. It adds 79 tokens to every session and 3,586 once invoked, about $0.0004 per session on Opus 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 2026-08-30.

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