Nvidia Ontology Management
NVIDIAOfficialCreated by NVIDIA
- Skill
- Workflow
- Data and AI
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured workflow for modeling, inspecting, and publishing semantic definitions in Auto Ontology. It emphasizes maintaining a rigorous semantic layer by separating routine metadata edits from complex modeling tasks and governed publication. The skill guides users through discovering current meanings, defining concepts and relationships, validating SQL attributes, and performing controlled model imports or exports. It is designed for environments where semantic accuracy and durable, governed changes are critical, ensuring that all modifications are backed by evidence and verified through exact readback procedures.
What it does
- Inspects and edits semantic metadata like terms, relationships, and measures.
- Validates SQL attributes and semantic definitions before application.
- Manages bulk model imports and exports for catalog and semantic layer updates.
- Enforces publication gates to ensure changes are durable and verified.
- Provides workflows for modeling concepts, relationships, and reusable measures.
- Facilitates verification of persisted changes through exact readback and comparison.
When to use it
- When you need to define or update business concepts, relationships, or measures in Auto Ontology.
- When performing bulk updates to the semantic model via import/export.
- When you need to ensure a semantic change is governed, validated, and durable.
- When troubleshooting semantic model inconsistencies or validation failures.
Advantages
- Ensures semantic consistency through rigorous modeling and validation workflows.
- Provides a clear path for governed publication and rollback of semantic models.
- Reduces risk by requiring verification of persisted changes rather than relying on API success.
- Supports complex semantic structures like derived SQL attributes and relationship paths.
Usage examples
- “Define a new run-grain measure for revenue in Auto Ontology.”
- The agent will guide you through the modeling.md workflow, requiring you to define the expression, grain, units, and additive dimensions before applying the change.
- “Promote a revised semantic model to production.”
- The agent will initiate the publication.md workflow, including backing up the current state, applying the change in isolation, and performing an exact readback comparison.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nvidia-ontology-management/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nvidia-ontology-management.zip -d .agents/skills
Documentation by NVIDIA
<!-- 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
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).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.
State the proposed change to the user (term rename, new SQL attribute, import, and so on). Do not silently rewrite the glossary.
Validate SQL before create/update:
POST /api/sql-attributes/validate. A parse failure is HTTP 422, notvalid: false.Apply the smallest write that matches the request (see write-api.md).
Compilation is not a hidden side effect. Check
GET /api/semantic-compilation/status. Do not callPOST /api/semantic-compilation/resetas cleanup — it is an asynchronous, destructive rebuild of every database's compiled layer, returns 202, and requiressemanticCompilation:manage.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 RESTPOST /api/question-entity-coverageandPOST /api/chat/completions) for positive and negative behavior checks.
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_columnsorGET /api/terms/{term_id}/column-attributesTerm → derived SQL: MCP
get_term_sql_attributesTable → terms and SQL attributes: MCP
describe_tableorGET /api/exploration/tables/{table_id}/detailsSemantic 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. PermissionmodelInterchange:export. Body may set catalog database IDs indatabases(empty = all) andformat(auto_ontologyorossie).POST /api/model/import— multipart YAML; native Auto Ontology (data_layer/semantic_layer) or Apache Ossie (version,name,datasetsat the root). Queryreplace(default true) andembed(default true). PermissionmodelInterchange:import. Can replace existing data — confirm with the user beforereplace=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
nvidia-ontology-query— how to call Auto Ontology and validate query resultsnvidia-ontology-setup— deployment not readymcp/auto_ontology_mcp/tools.py— live read-tool allow-list
Files
- ATTRIBUTION.mdAdded by AutoGineer936 B
- BENCHMARK.md9.7 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md6.2 KiB
- assets/runtime-contract.yaml2.4 KiB
- evals/evals.json10.4 KiB
- references/modeling.md3.8 KiB
- references/publication.md4.7 KiB
- references/write-api.md5.4 KiB
- skill-card.md4.6 KiB
- skill.oms.sig5.4 KiB
skill-card.md
Description: <br>
Model and publish semantic definitions in Auto Ontology, covering terms, relationships, measures, imports, and governed results (not deployment or querying). <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 data engineers use this skill to have an agent inspect and change the Auto Ontology semantic layer: editing terms and SQL attributes, defining source-grounded concepts, relationships, and measures, importing or exporting models with exact readback, and publishing governed definitions or results. <br>
Deployment Geography for Use: <br>
Global <br>
Requirements / Dependencies: <br>
Requires API Key or External Credential: [Yes] <br> Credential Type(s): [API key] <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>
Runtime contract <br>
Skill Output: <br>
Output Type(s): [API Calls, Analysis, Configuration instructions] <br> Output Format: [Markdown with inline HTTP request examples against the Next.js /api gateway] <br> Output Parameters: [1D] <br> Other Properties Related to Output: [MCP access is read-only; writes need the token owner's permissions, and unsupported publication stops at an explicit receipt] <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>
13 positive evaluation tasks, one attempt per task, each run in an isolated k8s sandbox pod and compared against a no-skill baseline. <br>
Evaluation Metrics Used: <br>
Reported benchmark dimensions: <br>
Security: Is it safe to use? (scored from
security) <br>Correctness: Is the answer correct? (scored from
accuracy) <br>Discoverability: Was the right skill loaded when needed? (scored from
skill_execution) <br>Effectiveness: Did the skill help complete the task? (50%
goal_accuracy+ 50%behavior_check) <br>Efficiency: Did it avoid wasted tool calls and token usage? (50%
skill_efficiency+ 50%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. <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>
| Measure | Claude Code (Baseline → Skill Uplift) | Codex (Baseline → Skill Uplift) |
|---|---|---|
| Overall | 94.9% (no comparable baseline; uplift unavailable) | 85.3% (no comparable baseline; uplift unavailable) |
| Security | 100.0% → 100.0% (±0.0 points) | 84.6% → 76.9% (-7.7 points) |
| Correctness | 29.2% → 100.0% (+70.8 points) | 47.7% → 89.2% (+41.5 points) |
| Discoverability | 93.7% (no comparable baseline; uplift unavailable) | 85.8% (no comparable baseline; uplift unavailable) |
| Effectiveness | 42.4% → 89.3% (+46.9 points) | 33.9% → 81.1% (+47.2 points) |
| Efficiency | 91.4% (no comparable baseline; uplift unavailable) | 93.3% (no comparable baseline; uplift unavailable) |
Skill Version(s): <br>
0.2.1 (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.
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
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
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/nvidia-ontology-management · 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 or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nvidia-ontology-management.zip