What is tao-mine-od-images?

An image-selection workflow that matches embedded source images to target samples while assigning each source image only once. Embeddings are numerical representations used to compare image similarity.

Use it with source and target embedding Parquet files to retrieve a requested number of unique images and save the selected files, summary, and iteration results.

It finds a unique set of nearby source images without reusing the same file, while avoiding the need to calculate embeddings itself.

tao-mine-od-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-od-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 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.

README.md

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

Your own site

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

Per session 74 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,060 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: 4 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 88
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 Privilege Escalation · line 95
Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.

Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
medium MCP Rug Pull · line 88
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 151
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	$0.00074	$0.02060
Opus 5.5	$0.00030	$0.00824
Sonnet 5.5	$0.00015	$0.00412
Haiku 4.5	$0.00007	$0.00206

Measured 1mo ago against content hash a4af7fb58f44, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

tao-mine-od-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 1mo ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/verify_unique_neighbor_matching_spec.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/data/tao-mine-od-images/SKILL.md · 187 lines

How it starts

The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TAO Mine OD Images (Unique Neighbor Matching)

Use this skill to run TAO Data Services TMM unique-neighbor matching mining for object detection. The skill consumes pre-embedded source and target parquets and writes a directory of outputs including final_unique_files.parquet and summary.json. It does not compute embeddings; upstream steps must produce the source and target embedding parquets first.

The container entrypoint is:

tmm unique_neighbor_matching -e /absolute/path/to/unique_neighbor_matching.yaml

Inputs

The user can provide either an existing spec or the fields needed to generate one.

Required spec fields:

Field	Meaning
`source_path`	Absolute path to the source embeddings parquet or directory of parquets.
`target_path`	Absolute path to the target embeddings parquet or directory of parquets.
`output_dir`	Absolute path to the output directory. Writes `final_unique_files.parquet`, `summary.json`, and per-iteration parquets.
`desired_unique_count`	Total number of unique source files to retrieve.

Common optional fields:

Field	Default	Meaning
`allocation_policy`	`global`	`global` or `class_stratified`.
`distance_metric`	`euclidean`	One of `euclidean`, `cosine`, or `manhattan`. Embeddings are L2-normalized before search.
`candidate_expansion_factor`	`5`	Candidate-pool multiplier per iteration. Increase if desired count is not reached.
`source_embedding_column`	`embedding`	Embedding column in `source_path`.
`target_embedding_column`	`embedding`	Embedding column in `target_path`.
`source_filepath_column`	`filepath`	Filepath column in `source_path`; also the column of `final_unique_files.parquet`.
`target_filepath_column`	`filepath`	Filepath column in `target_path`.
`exclude_path`	`null`	Parquet with a `filepath` column; those images are removed from the source pool.
`source_detection_file`	`null`	COCO `.json` or KITTI label directory for the source. Required for `class_stratified`.
`target_detection_file`	`null`	COCO `.json` or KITTI label directory for the target. Required for `class_stratified`.
`detection_format`	`null`	`coco` or `kitti`. Required whenever a detection file is set; never inferred from the path.
`rare_class_list`	`""`	Comma-separated rare class names, e.g. `"person,bicycle"`. Required for `class_stratified`.
`save_embeddings`	`false`	Include embeddings in per-iteration parquet outputs.
`visualize`	`false`	Save per-class visualization grids (requires Pillow and matplotlib).

Read the full file on GitHub · 187 lines

Files

What ships with it

5 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.

1mo ago Changed a4af7fb58f44
1mo ago First seen · 187 lines · 74 tokens per session scan A 776b3d5a6dcd

Subscribe to this mod's changes

tao-mine-od-images 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 74 tokens to every session and 2,060 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 2026-08-30.

Data and AI Scientific data mining object-detection tao tmm unique-neighbors

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