Chroma Vector Database
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
Run TAO Data Services TMM nearest-neighbor mining from embedding parquet files.
$ npx skills add NVIDIA/skills --skill tao-mine-nearest-neighbors -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills tao-mine-nearest-neighbors --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/tao-mine-nearest-neighbors .claude/skills/tao-mine-nearest-neighbors && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "tao-mine-nearest-neighbors" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-mine-nearest-neighbors into .claude/skills/tao-mine-nearest-neighbors/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-mine-nearest-neighbors", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/tao-mine-nearest-neighborsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill tao-mine-nearest-neighbors -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills tao-mine-nearest-neighbors --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/tao-mine-nearest-neighbors .agents/skills/tao-mine-nearest-neighbors && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tao-mine-nearest-neighbors" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-mine-nearest-neighbors into .agents/skills/tao-mine-nearest-neighbors/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-mine-nearest-neighbors", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill tao-mine-nearest-neighbors -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills tao-mine-nearest-neighbors --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/tao-mine-nearest-neighbors .cursor/skills/tao-mine-nearest-neighbors && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "tao-mine-nearest-neighbors" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-mine-nearest-neighbors into .cursor/skills/tao-mine-nearest-neighbors/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-mine-nearest-neighbors", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/tao-mine-nearest-neighbors--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill tao-mine-nearest-neighbors -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills tao-mine-nearest-neighbors --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/tao-mine-nearest-neighbors .gemini/skills/tao-mine-nearest-neighbors && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "tao-mine-nearest-neighbors" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-mine-nearest-neighbors into .gemini/skills/tao-mine-nearest-neighbors/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-mine-nearest-neighbors", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills tao-mine-nearest-neighborsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill tao-mine-nearest-neighbors -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/tao-mine-nearest-neighbors .github/skills/tao-mine-nearest-neighbors && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "tao-mine-nearest-neighbors" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-mine-nearest-neighbors into .github/skills/tao-mine-nearest-neighbors/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-mine-nearest-neighbors", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill tao-mine-nearest-neighbors -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills tao-mine-nearest-neighbors --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/tao-mine-nearest-neighbors .opencode/skills/tao-mine-nearest-neighbors && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "tao-mine-nearest-neighbors" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/tao-mine-nearest-neighbors into .opencode/skills/tao-mine-nearest-neighbors/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tao-mine-nearest-neighbors", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
tao-mine-nearest-neighborsRun TAO Data Services TMM nearest-neighbor mining from embedding parquet files.
Tao Mine Nearest Neighbors is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. 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.
Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts, reference files and assets (for example `BENCHMARK.md`, `assets/default_nearest_neighbors.yaml` and `config/skillspector-baseline.yaml`). Compatibility notes: Requires docker, nvidia-container-toolkit, one or more CUDA GPUs, and the TAO data-services container pinned in versions.yaml.
It sits in AI & LLM Engineering, covering DataFrames and Embeddings. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.
Pre-approves these tools, so the agent can use them without asking each time:
ReadBashFrom allowed-tools in the SKILL.md frontmatter.
Ships 2 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
dockerpython3From the folder's file list and the shell code blocks in SKILL.md.
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.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
Tao Mine Nearest Neighbors loads about 1.6k tokens when it runs, and up to ~1.7k if it reads all its reference files. Until then it costs about 46 tokens; SKILL.md has 590 words of instructions outside code blocks.
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.
The automated check noted patterns worth knowing about, such as sudo or a known installer.
allowed-tools: Read, BashAutomated 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.
The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 590 words, ~1,565 tokens.
.claude/skills/tao-mine-nearest-neighbors/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.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.yamlTAO Data Services requires -e/--experiment_spec_file. The tmm console script converts that YAML into Hydra --config-path and --config-name arguments internally.
The user can provide either an existing nearest-neighbors YAML spec or the fields needed to generate one.
Required spec fields:
| Field | Meaning |
|---|---|
source_parquet | Absolute path to the candidate/source embeddings parquet. |
target_parquet | Absolute path to the target/query embeddings parquet. |
output_parquet | Absolute path where TAO Data Services should write mined source filepaths. |
Common optional fields:
| Field | Default | Meaning |
|---|---|---|
topn | 5 | Number of nearest source samples to retrieve per target sample. |
knn_metric | cosine | One of cosine, euclidean, or manhattan. |
source_embed_column_name | embedding | Embedding column in source_parquet. |
target_embed_column_name | embedding | Embedding 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 | -1.0 | Maximum 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.
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.
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 -1.0The generated YAML uses absolute paths. Keep the spec, input parquets, and output directory under RUN_ROOT so the same paths resolve inside the container.
Before launching Docker:
docker info > /dev/null
nvidia-smi -LDS_IMAGE="$(scripts/resolve_versions_key.py images.tao_toolkit.data_services)"
docker image inspect "$DS_IMAGE" > /dev/null || docker pull "$DS_IMAGE"python3 skills/data/tao-mine-nearest-neighbors/scripts/verify_nearest_neighbors_spec.py \
--spec "$SPEC"RUN_ROOT contains the spec, both input parquets, and the output directory. Mount RUN_ROOT to the same absolute path inside Docker.The skill promises the artifacts named by the spec:
| Artifact | Location |
|---|---|
| mined parquet | output_parquet |
| mining summary | mining_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.
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.
© 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
SKILL.md and 9 other files (scripts, references, assets) in skills/tao-mine-nearest-neighbors of NVIDIA/skills.
Open the folder on GitHubat commit 14a98ae
Tao Mine Nearest Neighbors next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Tao Mine Nearest Neighbors this skillNVIDIA/skills | 3.6k | — | ~1.6k | Automated safety check: Notes | Apache-2.0 | |
| Chroma Vector DatabaseOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~2.3k | Automated safety check: Pass | MIT | |
| SageMaker Serving Image Selectionhuggingface/skills | 11k | 1 repos | ~4.6k | Automated safety check: Pass | Apache-2.0 | |
| Codebase Managementgiancarloerra/SocratiCode | 3.3k | 1 repos | ~1.8k | Automated safety check: Pass | AGPL-3.0 | |
| CLIP Image-Text MatchingOrchestra-Research/AI-Research-SKILLs | 13k | 7 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Sentence-Transformers Training Routerhuggingface/skills | 11k | 1 repos | ~2.6k | Automated safety check: Pass | Apache-2.0 |
Orchestra-Research/AI-Research-SKILLs
Shows how to store documents and embeddings in Chroma, query them by similarity with metadata filters, and persist them to disk for RAG and semantic search projects.
huggingface/skills
Chooses the right serving container and current image URI for deploying a Hugging Face model to a SageMaker endpoint, preferring Hugging Face images over generic ones.
giancarloerra/SocratiCode
Set up, index, and manage SocratiCode codebase indexing. An agent skill from giancarloerra/SocratiCode.
Orchestra-Research/AI-Research-SKILLs
Explains OpenAI's CLIP model for zero-shot image classification, image-text similarity, semantic image search and content moderation, with install steps and code patterns.
huggingface/skills
Routes a sentence-transformers training task to the right model type and required reference docs and example scripts, covering bi-encoders, rerankers, sparse and multi-vector models.
rehan-remade/universal-modder
Build cross-game mashups and total conversions, the "Minecraft inside Elden Ring" or "skateboarding in MW2" kind.
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
Categories
Run TAO Data Services TMM nearest-neighbor mining from embedding parquet files. Tao Mine Nearest Neighbors is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Run TAO Data Services TMM nearest-neighbor mining from embedding parquet files.
Tao Mine Nearest Neighbors fits situations like: A workflow needs to mine source samples closest to target samples; tasks that involve DataFrames; tasks that involve Embeddings.
Run `npx skills add NVIDIA/skills --skill tao-mine-nearest-neighbors -a claude-code`. Or copy the skill folder (skills/tao-mine-nearest-neighbors in NVIDIA/skills) into .claude/skills/tao-mine-nearest-neighbors in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill tao-mine-nearest-neighbors -a codex`. Or copy the skill folder (skills/tao-mine-nearest-neighbors in NVIDIA/skills) into .agents/skills/tao-mine-nearest-neighbors in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill tao-mine-nearest-neighbors -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tao-mine-nearest-neighbors, .gemini/skills/tao-mine-nearest-neighbors, .github/skills/tao-mine-nearest-neighbors and .opencode/skills/tao-mine-nearest-neighbors in your project.
Going by SKILL.md and its folder, Tao Mine Nearest Neighbors needs Python for the scripts in its folder and the command-line tools its instructions call (docker and python3). Our summary lists: Python 3; Docker. Its frontmatter pre-approves these tools: Read, Bash. Compatibility (from SKILL.md): Requires docker, nvidia-container-toolkit, one or more CUDA GPUs, and the TAO data-services container pinned in versions.yaml..
SKILL.md contains no URLs. Its commands use docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Tao Mine Nearest Neighbors is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 141 tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Tao Mine Nearest Neighbors: Chroma Vector Database (Orchestra-Research/AI-Research-SKILLs, 13k stars), SageMaker Serving Image Selection (huggingface/skills, 11k stars), Codebase Management (giancarloerra/SocratiCode, 3.3k stars) and CLIP Image-Text Matching (Orchestra-Research/AI-Research-SKILLs, 13k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.