Official agent skill

Nvidia Ontology Management

by NVIDIA in NVIDIA/skills

Model and publish semantic definitions in Auto Ontology. An agent skill from NVIDIA/skills.

OfficialApache-2.0Auto-check passedDevOps & Cloud

Install Nvidia Ontology Management

skills CLI
$ npx skills add NVIDIA/skills --skill nvidia-ontology-management -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NVIDIA/skills nvidia-ontology-management --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nvidia-ontology-management .claude/skills/nvidia-ontology-management && rm -rf skills-src

Use ~/.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/

Facts

Skill name
nvidia-ontology-management
GitHub stars
3.6k
Token cost
~1.6k tokens
SKILL.md length
644 words
Files
9 (incl. references, assets)
Skills in repo
390
Repo updated
First seen
Licence
Apache-2.0

At a glance

Model and publish semantic definitions in Auto Ontology. An agent skill from NVIDIA/skills.

  • Works in 7 steps: Discover current meaning via MCP… → Resolve the semantic contract when… → State the proposed change to the user… → …
  • Governed results—not deployment
  • SKILL.md covers Purpose, Prerequisites, Limitations and Auth, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Nvidia Ontology Management is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Model and publish semantic definitions in Auto Ontology. Use for terms, relationships, measures, imports, and governed results—not deployment or querying.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 11 other files, including reference files and assets (for example `BENCHMARK.md`, `assets/runtime-contract.yaml` and `evals/evals.json`).

It sits in DevOps & Cloud. It works with NVIDIA AI Platform and Model Context Protocol. 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.

When your agent uses it

  • Governed results—not deployment

Example prompts

  • “/nvidia-ontology-management”

Workflow steps

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

  1. Discover current meaning via MCP (search_terms, get_term,
  2. Resolve the semantic contract when meaning changes. Record source
  3. State the proposed change to the user (term rename, new SQL attribute,
  4. Validate SQL before create/update: POST /api/sql-attributes/validate.
  5. Apply the smallest write that matches the request (see
  6. Compilation is not a hidden side effect. Check
  7. Verify the persisted change, not merely request success. Model imports

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 nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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.

Context cost

Nvidia Ontology Management loads about 1.6k tokens when it runs, and up to ~5.1k if it reads all its reference files. Until then it costs about 45 tokens; SKILL.md has 644 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~45
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.1k

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

Auto-check passed

The automated check found no risky patterns in SKILL.md.

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 644 words, ~1,571 tokens.

Download SKILL.mdSave it as .claude/skills/nvidia-ontology-management/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
nvidia-ontology-management
description
Model and publish semantic definitions in Auto Ontology. Use for terms, relationships, measures, imports, and governed results—not deployment or querying.
license
Apache-2.0
metadata.version
0.2.1
metadata.author
NVIDIA <opensource@nvidia.com>
metadata.tags
nvidia-ontology, ontology, glossary, sql-attributes, catalog
<!--
SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES.
All rights reserved.
SPDX-License-Identifier: Apache-2.0
-->

Auto Ontology management and modeling

Purpose

Inspect and change Auto Ontology's semantic layer without confusing a successful API call with correct business meaning or durable publication. The machine-readable inputs, outputs, statuses, gates, and handoffs are in runtime-contract.yaml.

Choose the focused workflow before acting:

  • routine inspection or metadata edits: continue below and use write-api.md;
  • concepts, relationships, measures, units, grain, or policies: use modeling.md;
  • model promotion or reusable analytical results: use publication.md.

MCP tools only read. Create, patch, import, and compile-reset go through the Next.js /api/... gateway. Governed result rows require an approved source writer; Auto Ontology has no generic result-row writeback endpoint. Request bodies and field types live in docs/openapi/auto-ontology-api.json; this skill names operations and permissions only.

Prerequisites

Use a running, authenticated Auto Ontology deployment. Use nvidia-ontology-setup when the deployment or semantic layer is not ready.

Limitations

MCP is read-only, Auto Ontology has no generic result-row writeback endpoint, and current model import and certification APIs do not prove atomic promotion. Follow the publication gates below and fail closed when exact readback is unavailable.

Auth

Scripts use an API token (x-api-key or Authorization: Bearer). Mint it in the UI (user menu → API Tokens). A token acts as its owner — a viewer's token cannot do admin things. Creating and revoking tokens requires a signed-in session; a token cannot mint another token.

Writes below need catalog:edit unless noted. 403 means the owner's role lacks that permission, not that the path is wrong.

Instructions

  1. Discover current meaning via MCP (search_terms, get_term, get_term_columns, get_term_sql_attributes, describe_table) or REST if MCP is absent (GET /api/terms, GET /api/terms/{term_id}, GET /api/exploration/tables/{table_id}/details).
  2. Resolve the semantic contract when meaning changes. Record source binding, identity, population, relationship cardinality, measure expression, unit, grain, denominator, time, validity, and counterexamples; see modeling.md.
  3. State the proposed change to the user (term rename, new SQL attribute, import, and so on). Do not silently rewrite the glossary.
  4. Validate SQL before create/update: POST /api/sql-attributes/validate. A parse failure is HTTP 422, not valid: false.
  5. Apply the smallest write that matches the request (see write-api.md).
  6. Compilation is not a hidden side effect. Check GET /api/semantic-compilation/status. Do not call POST /api/semantic-compilation/reset as cleanup — it is an asynchronous, destructive rebuild of every database's compiled layer, returns 202, and requires semanticCompilation:manage.
  7. Verify the persisted change, not merely request success. Model imports require a scoped backup and exact re-export comparison; see publication.md. Then use MCP check_answerable / ask_question (or REST POST /api/question-entity-coverage and POST /api/chat/completions) for positive and negative behavior checks.
Show full SKILL.md (233 more words)Show less

Semantic relationships and "what does this dataset mean"

Stay on the semantic layer. Do not browse raw schemas to answer meaning.

  • Term → columns: MCP get_term_columns or GET /api/terms/{term_id}/column-attributes
  • Term → derived SQL: MCP get_term_sql_attributes
  • Table → terms and SQL attributes: MCP describe_table or GET /api/exploration/tables/{table_id}/details
  • Semantic hop chain between two terms: GET /api/exploration/terms/{term_id}/path/{other_term_id}
  • Semantic graphs: GET /api/exploration/graph, GET /api/exploration/semantic-graph

These are semantic relationship paths, not generation provenance, source revision, certification history, or version lineage.

Bulk import / export

  • POST /api/model/export — YAML of catalog + semantic layer. Permission modelInterchange:export. Body may set catalog database IDs in databases (empty = all) and format (auto_ontology or ossie).
  • POST /api/model/import — multipart YAML; native Auto Ontology (data_layer / semantic_layer) or Apache Ossie (version, name, datasets at the root). Query replace (default true) and embed (default true). Permission modelInterchange:import. Can replace existing data — confirm with the user before replace=true, apply first in isolation, and use the exact readback workflow in publication.md.

Examples

  • To define a run-grain measure, follow the evidence and counterexample workflow in modeling.md.
  • To revise and promote a model, back up the exact scope and follow the staged readback workflow in publication.md.

Troubleshooting

For write failures, verify the authenticated owner's permission, distinguish HTTP 422 parse failures from validation results, and inspect compilation status before changing the model or resetting compilation.

See also

  • write-api.md
  • modeling.md
  • publication.md
  • nvidia-ontology-query — how to call Auto Ontology and validate query results
  • nvidia-ontology-setup — deployment not ready
  • mcp/auto_ontology_mcp/tools.py — live read-tool allow-list

© 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 (references, assets) in skills/nvidia-ontology-management of NVIDIA/skills.

  • SKILL.md
  • BENCHMARK.md
  • assets/runtime-contract.yaml
  • evals/evals.json
  • references/modeling.md
  • references/publication.md
  • references/write-api.md
  • skill-card.md
  • skill.oms.sig

Open the folder on GitHubat commit 14a98ae

Compare with similar skills

Nvidia Ontology Management 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.

Nvidia Ontology Management compared with similar skills
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Categories

Questions about Nvidia Ontology Management

What does Nvidia Ontology Management do?

Model and publish semantic definitions in Auto Ontology. An agent skill from NVIDIA/skills. Nvidia Ontology Management is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Model and publish semantic definitions in Auto Ontology.

When should I use Nvidia Ontology Management?

Nvidia Ontology Management fits situations like: governed results—not deployment.

How do I install Nvidia Ontology Management in Claude Code?

Run `npx skills add NVIDIA/skills --skill nvidia-ontology-management -a claude-code`. Or copy the skill folder (skills/nvidia-ontology-management in NVIDIA/skills) into .claude/skills/nvidia-ontology-management in your project. Claude Code loads it when a task matches its description.

How do I install Nvidia Ontology Management in Codex?

Run `npx skills add NVIDIA/skills --skill nvidia-ontology-management -a codex`. Or copy the skill folder (skills/nvidia-ontology-management in NVIDIA/skills) into .agents/skills/nvidia-ontology-management in your project. Codex loads it when a task matches its description.

Can I use Nvidia Ontology Management in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill nvidia-ontology-management -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nvidia-ontology-management, .gemini/skills/nvidia-ontology-management, .github/skills/nvidia-ontology-management and .opencode/skills/nvidia-ontology-management in your project.

What does Nvidia Ontology Management need to run?

SKILL.md names no scripts, command-line tools or credentials: Nvidia Ontology Management is instructions for the agent only.

Does Nvidia Ontology Management access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Nvidia Ontology Management safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Nvidia Ontology Management use?

Nvidia Ontology Management 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.

How many tokens does Nvidia Ontology Management use?

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 3.6k tokens, read only when the agent opens those files.

What are the alternatives to Nvidia Ontology Management?

Skills that share tags, products or a category with Nvidia Ontology Management: Skill Inspector (NVIDIA/SkillSpector, 20k stars), AWS Cdk Development (zxkane/aws-skills, 367 stars), Terravision Cloud Diagrams (patrickchugh/terravision, 1.6k stars) and Rocketmq Rust Local Cluster (mxsm/rocketmq-rust, 1.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nvidia Ontology Management?

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.