Tao Mine Od Images

What it does

When your agent uses it

• An object detection workflow needs to mine a bijectively-assigned set of unique source images closest to target samples
• Tasks that involve DataFrames
• Tasks that involve Embeddings

Example prompts

• “/tao-mine-od-images”

Requirements

• Python 3
• Docker
• Compatibility (from SKILL.md): Requires docker, nvidia-container-toolkit, one or more CUDA GPUs, and the TAO data-services container pinned in versions.yaml.
• Pre-approved tools (allowed-tools): Read, Bash

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Verify Docker and GPU access
2. 2Resolve and pull the data-services image if needed
3. 3Validate the spec
4. 4Confirm RUN_ROOT contains the spec, both input parquets (or directories), and the output directory. Mount RUN_ROOT to the same absolute…

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Bash

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 1 file in scripts/ (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • docker
  • python3

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md. Its commands use docker, which can reach the network depending on how they are called.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires docker, nvidia-container-toolkit, one or more CUDA GPUs, and the TAO data-services container pinned in versions.yaml.

  From compatibility in the SKILL.md frontmatter.

Context cost

Tao Mine Od Images loads about 2.2k tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 96 tokens; SKILL.md has 774 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~96

When it runs · the whole SKILL.md, loaded when a task matches

~2.2k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Read, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 774 words, ~2,179 tokens.

name

tao-mine-od-images

description

Run TAO Data Services TMM unique-neighbor matching mining from embedding parquet files for object detection workflows. Use when an object detection workflow needs to mine a bijectively-assigned set of unique source images closest to target samples. Use global allocation when mining without class constraints. Use class_stratified when rare classes are specified.

allowed-tools

Read, Bash

compatibility

Requires docker, nvidia-container-toolkit, one or more CUDA GPUs, and the TAO data-services container pinned in versions.yaml.

license

Apache-2.0

metadata.author

NVIDIA Corporation

metadata.version

0.1.0

tags

tao, data, mining, object-detection, unique-neighbors, tmm

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

OUTPUT_DIR=/absolute/path/for/this/run           # output_dir in the spec
SPEC="$OUTPUT_DIR/unique_neighbor_matching.yaml" # spec lives beside its outputs
RUN_ROOT=/absolute/path/that/contains/specs/data/and/results
GPU_COUNT=1

python3 skills/data/tao-mine-od-images/scripts/verify_unique_neighbor_matching_spec.py \
  --spec "$SPEC"

DS_IMAGE=nvcr.io/nvidia/tao/tao-toolkit:7.2.0-data-services  # versions-key: images.tao_toolkit.data_services

docker run --rm --gpus "$GPU_COUNT" --shm-size=8g --network=host \
  -v "$RUN_ROOT:$RUN_ROOT" \
  -w "$RUN_ROOT" \
  "$DS_IMAGE" \
  tmm unique_neighbor_matching -e "$SPEC"

cp skills/data/tao-mine-od-images/assets/default_unique_neighbor_matching.yaml "$SPEC"

python3 skills/data/tao-mine-od-images/scripts/verify_unique_neighbor_matching_spec.py --spec "$SPEC"

source_path: /absolute/path/source_embeddings.parquet
target_path: /absolute/path/target_embeddings.parquet
output_dir: /absolute/path/results/mining_output
desired_unique_count: 500
allocation_policy: global          # or class_stratified — see below
distance_metric: euclidean

docker info > /dev/null
nvidia-smi -L

DS_IMAGE=nvcr.io/nvidia/tao/tao-toolkit:7.2.0-data-services  # versions-key: images.tao_toolkit.data_services
docker image inspect "$DS_IMAGE" > /dev/null || docker pull "$DS_IMAGE"

python3 skills/data/tao-mine-od-images/scripts/verify_unique_neighbor_matching_spec.py \
  --spec "$SPEC"

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (scripts, references, assets) in skills/tao-mine-od-images of NVIDIA/skills.

• SKILL.md
• BENCHMARK.md
• assets/default_unique_neighbor_matching.yaml
• config/skillspector-baseline.yaml
• evals/evals.json
• references/skill_info.yaml
• scripts/verify_unique_neighbor_matching_spec.py
• skill-card.md
• skill.oms.sig

Open the folder on GitHubat commit 14a98ae