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Tao Mine Nearest Neighbors

NVIDIAOfficialCreated by NVIDIA Corporation

  • Skill
  • Workflow
  • Data and AI
  • Apache-2.0
  • Medium risk
  • Has scripts
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From the author

Run TAO Data Services TMM nearest-neighbor mining from embedding parquet files. Use when a workflow needs to mine source samples closest to target samples.
— the description by NVIDIA Corporation

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides an interface to execute the TAO Data Services TMM nearest-neighbor mining task. It processes source and target embedding parquet files to identify and retrieve the closest samples based on specified metrics. The skill includes utilities for generating and verifying experiment configuration files, ensuring that input paths and parameters are correctly formatted for the containerized execution environment.

What it does

  • Generates experiment configuration files for nearest-neighbor mining.
  • Validates configuration specs before execution.
  • Executes the TMM nearest-neighbor mining task using Docker.
  • Supports configurable distance metrics including cosine, euclidean, and manhattan.
  • Allows optional filtering of neighbors by label.
  • Produces a mined parquet file and a summary report of the mining process.

When to use it

  • When you need to identify source samples that are closest to specific target samples.
  • When working with embedding data stored in parquet format.
  • When you need to perform similarity searches within a dataset using TAO Data Services.
  • When you need to filter neighbor search results based on labels or distance thresholds.

Advantages

  • Automates the creation and validation of experiment specifications.
  • Provides a structured approach to managing input and output paths for containerized tasks.
  • Includes built-in verification steps to ensure environment and configuration readiness.
  • Offers flexible configuration for distance metrics and neighbor retrieval counts.

Usage examples

“Generate a nearest-neighbor mining spec for my source and target embeddings.”
The agent will run the preparation script to create a YAML configuration file with your specified source, target, and output paths.
“Run the nearest-neighbor mining task using my existing spec file.”
The agent will validate the spec and execute the TAO Data Services container to perform the mining and generate the results.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/nvidia/tao-mine-nearest-neighbors/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip tao-mine-nearest-neighbors.zip -d .agents/skills

Documentation by NVIDIA Corporation

TAO Mine Nearest Neighbors

Use this skill to run TAO Data Services TMM nearest-neighbor mining. The skill consumes embedding parquets and writes a mined source-sample parquet plus a mining summary. It does not compute embeddings; upstream steps must produce the source and target embedding parquets first.

The container entrypoint is:

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

TAO Data Services requires -e/--experiment_spec_file. The tmm console script converts that YAML into Hydra --config-path and --config-name arguments internally.

Inputs

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

Required spec fields:

FieldMeaning
source_parquetAbsolute path to the candidate/source embeddings parquet.
target_parquetAbsolute path to the target/query embeddings parquet.
output_parquetAbsolute path where TAO Data Services should write mined source filepaths.

Common optional fields:

FieldDefaultMeaning
topn5Number of nearest source samples to retrieve per target sample.
knn_metriccosineOne of cosine, euclidean, or manhattan.
source_embed_column_nameembeddingEmbedding column in source_parquet.
target_embed_column_nameembeddingEmbedding column in target_parquet.
filter_by_label"false"String flag. When "true", TAO DS filters neighbors by matching label columns when both parquets provide labels.
distance_threshold-1.0Maximum distance to keep. Negative disables thresholding.

Both input parquets must contain a filepath column and a list-like embedding column. If filter_by_label is "true", both parquets should also contain label.

The default template is assets/default_nearest_neighbors.yaml.

Quick Start

Run from the tao-skill-bank repo root. Resolve the pinned TAO Data Services image from versions.yaml, verify the spec, mount the run root with identical host/container paths, and stream the Docker logs.

SPEC=/absolute/path/to/nearest_neighbors.yaml
RUN_ROOT=/absolute/path/that/contains/specs/data/and/results
GPU_COUNT=1

python3 skills/data/tao-mine-nearest-neighbors/scripts/verify_nearest_neighbors_spec.py \
  --spec "$SPEC"

DS_IMAGE="$(scripts/resolve_versions_key.py images.tao_toolkit.data_services)"

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

Use at least one GPU. Choose GPU_COUNT from the hardware available to the host or platform that will run the container. If the user does not know the right value, inspect the host with nvidia-smi -L or ask which GPU allocation the run should use.

Do not pass --user $(id -u):$(id -g) to the TAO data-services container unless you have verified the image supports that UID. Some TAO DS images import Python packages that call getpass.getuser() at startup and fail when the UID is not present in /etc/passwd.

Generate A Spec

If the user provides source/target/output parquet paths instead of a ready spec, generate a spec from the default template:

python3 skills/data/tao-mine-nearest-neighbors/scripts/prepare_nearest_neighbors_spec.py \
  --source-parquet /absolute/path/source_embeddings.parquet \
  --target-parquet /absolute/path/target_embeddings.parquet \
  --output-parquet /absolute/path/results/mined.parquet \
  --output-spec /absolute/path/specs/nearest_neighbors.yaml \
  --topn 5 \
  --knn-metric cosine \
  --filter-by-label false \
  --distance-threshold -1.0

The generated YAML uses absolute paths. Keep the spec, input parquets, and output directory under RUN_ROOT so the same paths resolve inside the container.

Preflight

Before launching Docker:

  1. Verify Docker and GPU access:

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

  1. Resolve and pull the data-services image if needed:

DS_IMAGE="$(scripts/resolve_versions_key.py images.tao_toolkit.data_services)"
docker image inspect "$DS_IMAGE" > /dev/null || docker pull "$DS_IMAGE"

  1. Validate the spec:

python3 skills/data/tao-mine-nearest-neighbors/scripts/verify_nearest_neighbors_spec.py \
  --spec "$SPEC"

  1. Confirm RUN_ROOT contains the spec, both input parquets, and the output directory. Mount RUN_ROOT to the same absolute path inside Docker.

Outputs

The skill promises the artifacts named by the spec:

ArtifactLocation
mined parquetoutput_parquet
mining summarymining_summary.txt next to output_parquet

The current TAO Data Services nearest_neighbors task writes a mined parquet with unique source filepath rows. The summary file reports mining counts such as queries processed, neighbors considered, duplicates removed, and any label/distance filtering.

Troubleshooting

The subtask nearest_neighbors requires -e/--experiment_spec_file: rerun with tmm nearest_neighbors -e "$SPEC". Hydra overrides alone are not enough.

Input parquet not found inside Docker: the YAML path must be visible inside the container. Use a RUN_ROOT mount where the host and container paths are identical.

Output directory is not writable after Docker exits: the TAO DS container may have written files as root. Inform the user, report which artifacts were produced, and ask whether to repair permissions on the output directory before continuing.

No GPU or cuDF/cuML errors: nearest-neighbor mining requires at least one CUDA GPU. Check nvidia-smi -L, the Docker --gpus flag, and the NVIDIA container toolkit installation.

Files

skill-card.md

Description: <br>

Run TAO Data Services TMM nearest-neighbor mining from embedding parquet files. <br>

This skill is ready for commercial/non-commercial use. <br>

Owner

NVIDIA <br>

License/Terms of Use: <br>

Apache 2.0 <br>

Use Case: <br>

Developers and engineers use this skill to mine the source samples closest to target samples from embedding parquet files using TAO Data Services nearest-neighbor mining, producing a mined source-sample parquet and a mining summary. <br>

Deployment Geography for Use: <br>

Global <br>

Requirements / Dependencies: <br>

Requires API Key or External Credential: [Not Specified] <br> Credential Type(s): [None identified] <br>

Do not include secrets in prompts/logs/output; use least-privilege credentials; rotate keys as appropriate. <br>

Known Risks and Mitigations: <br>

Risk: Review before execution as proposals could introduce incorrect or misleading guidance into skills. <br> Mitigation: Review and scan skill before deployment. <br>

Reference(s): <br>

Skill Output: <br>

Output Type(s): [Files, Shell commands] <br> Output Format: [Parquet file and text mining summary produced via Docker container execution] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [None] <br>

Evaluation Agents Used: <br>

  • Claude Code (aws/anthropic/bedrock-claude-opus-4-8) <br>

  • Codex (openai/openai/gpt-5.5) <br>

Evaluation Tasks: <br>

1 evaluation task (1 positive), 1 attempt per task, each run in an isolated k8s-sandbox pod; evaluator version 1.5.6. <br>

Evaluation Metrics Used: <br>

Reported benchmark dimensions: <br>

  • Security: Is it safe to use? Scored from the security signal. <br>

  • Correctness: Is the answer correct? Scored from the accuracy signal. <br>

  • Discoverability: Was the right skill loaded when needed? Scored from the skill_execution signal. <br>

  • Effectiveness: Did the skill help complete the task? Equal-weight mean of goal_accuracy and behavior_check. <br>

  • Efficiency: Did it avoid wasted tool calls and token usage? Equal-weight mean of skill_efficiency and token_efficiency. <br>

Underlying evaluation signals used in this run: <br>

  • security: Unsafe operations, secret leakage, and unauthorized access. <br>

  • skill_execution: Whether the expected skill was selected, decoys were avoided, and the workflow executed. <br>

  • skill_efficiency: Tool-call productivity (legacy wire id; routing is scored under Discoverability). <br>

  • accuracy: Final-answer correctness against the reference answer. <br>

  • goal_accuracy: Whether the user's goal was achieved. <br>

  • behavior_check: Whether the expected workflow behavior was followed. <br>

  • token_efficiency: Actual uncached prompt plus completion usage. <br>

Evaluation Results: <br>

MeasureClaude Code (Baseline → Skill Uplift)Codex (Baseline → Skill Uplift)
Overall94.6% — baseline ran, but no comparable score was available; uplift unavailable64.7% — baseline ran, but no comparable score was available; uplift unavailable
Security100.0% → 100.0% (±0.0 points)100.0% → 100.0% (±0.0 points)
Correctness0.0% → 100.0% (+100.0 points)20.0% → 80.0% (+60.0 points)
Discoverability100.0% — baseline ran, but no comparable score was available; uplift unavailable0.0% — baseline ran, but no comparable score was available; uplift unavailable
Effectiveness12.5% → 100.0% (+87.5 points)40.0% → 45.0% (+5.0 points)
Efficiency73.1% — baseline ran, but no comparable score was available; uplift unavailable99.7% → 98.6% (-1.1 points)

Skill Version(s): <br>

0.1.0 (source: frontmatter) <br>

Ethical Considerations: <br>

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal team to ensure this skill meets requirements for the relevant industry and use case and addresses unforeseen product misuse. <br>

(For Release on NVIDIA Platforms Only) <br> Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here. <br>

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs a command-line tool, Needs Python

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

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs
  • References to credential files
  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
NVIDIA Corporation
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/tao-mine-nearest-neighbors · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA Corporation or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download tao-mine-nearest-neighbors.zip