Sap Hana CLI
secondsky/sap-skills
Assists with SAP HANA Developer CLI (hana-cli) for database development and administration.
Query Auto Ontology and validate generated SQL, rows, and answers.
Install Nvidia Ontology Query
$ npx skills add NVIDIA/skills --skill nvidia-ontology-query -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills nvidia-ontology-query --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/nvidia-ontology-query .claude/skills/nvidia-ontology-query && 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 "nvidia-ontology-query" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nvidia-ontology-query into .claude/skills/nvidia-ontology-query/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nvidia-ontology-query", 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/nvidia-ontology-queryType 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 nvidia-ontology-query -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills nvidia-ontology-query --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/nvidia-ontology-query .agents/skills/nvidia-ontology-query && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "nvidia-ontology-query" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nvidia-ontology-query into .agents/skills/nvidia-ontology-query/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nvidia-ontology-query", 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 nvidia-ontology-query -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills nvidia-ontology-query --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/nvidia-ontology-query .cursor/skills/nvidia-ontology-query && 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 "nvidia-ontology-query" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nvidia-ontology-query into .cursor/skills/nvidia-ontology-query/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nvidia-ontology-query", 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/nvidia-ontology-query--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 nvidia-ontology-query -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills nvidia-ontology-query --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/nvidia-ontology-query .gemini/skills/nvidia-ontology-query && 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 "nvidia-ontology-query" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nvidia-ontology-query into .gemini/skills/nvidia-ontology-query/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nvidia-ontology-query", 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 nvidia-ontology-queryInstalls 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 nvidia-ontology-query -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/nvidia-ontology-query .github/skills/nvidia-ontology-query && 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 "nvidia-ontology-query" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nvidia-ontology-query into .github/skills/nvidia-ontology-query/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nvidia-ontology-query", 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 nvidia-ontology-query -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 nvidia-ontology-query --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/nvidia-ontology-query .opencode/skills/nvidia-ontology-query && 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 "nvidia-ontology-query" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nvidia-ontology-query into .opencode/skills/nvidia-ontology-query/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nvidia-ontology-query", 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.
Facts
- Skill name
nvidia-ontology-query- GitHub stars
- 3.5k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 703 words
- Files
- 8 (incl. references, assets)
- Skills in repo
- 386
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Query Auto Ontology and validate generated SQL, rows, and answers.
- Works in 4 steps: Call readiness before asking a question. → Discover terms and confirm answerability… → Ask through MCP, or use the… → …
- Grounded questions
- SKILL.md covers Purpose, Prerequisites, Limitations and Instructions, plus 7 more sections
- Needs AUTO_ONTOLOGY_API_TOKEN
What it does
Nvidia Ontology Query is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Query Auto Ontology and validate generated SQL, rows, and answers. Use for MCP or REST access, readiness, authentication, conversations, and grounded questions.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including reference files and assets (for example `BENCHMARK.md`, `assets/runtime-contract.yaml` and `evals/evals.json`).
It sits in Databases, covering SQL and MCP servers. It works with NVIDIA AI Platform, Model Context Protocol and SQL. 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
- Grounded questions
- Tasks that involve SQL
- Tasks that involve MCP servers
Example prompts
- “/nvidia-ontology-query”
Requirements
- Python 3
- A credential in AUTO_ONTOLOGY_API_TOKEN
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Call readiness before asking a question.
- Discover terms and confirm answerability through the semantic layer.
- Ask through MCP, or use the authenticated Next.js REST gateway as fallback.
- Validate material intent, SQL, rows, truncation, and prose before presenting
What it can do on your machine
Read from SKILL.md and the folder at commit dfdd080. 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 (its code samples are python).
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 these keys or tokens, usually read from environment variables:
AUTO_ONTOLOGY_API_TOKEN
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Nvidia Ontology Query loads about 1.7k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 46 tokens; SKILL.md has 703 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.
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 dfdd080, republished under its Apache-2.0 licence (© NVIDIA). 703 words, ~1,653 tokens.
Download SKILL.mdSave it as
.claude/skills/nvidia-ontology-query/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- nvidia-ontology-query
- description
- Query Auto Ontology and validate generated SQL, rows, and answers. Use for MCP or REST access, readiness, authentication, conversations, and grounded questions.
- license
- Apache-2.0
- metadata.version
- 0.2.1
- metadata.author
- NVIDIA <opensource@nvidia.com>
- metadata.tags
- nvidia-ontology, mcp, api, agents, aiq
<!--
SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES.
All rights reserved.
SPDX-License-Identifier: Apache-2.0
-->Auto Ontology grounded queries
Purpose
Query the current Auto Ontology implementation from an agent without prescribing a harness, SDK, or LangGraph template. Typed workflow artifacts, statuses, gates, and handoffs are in runtime-contract.yaml.
Prerequisites
Use a running Auto Ontology deployment and an authenticated MCP or REST client. If Auto Ontology is
not running, use nvidia-ontology-setup first.
Limitations
This skill reads and validates answers. It does not expose FastAPI directly,
write semantic definitions, publish governed results, or provide a raw-schema
browser. Hand mutations and publication to nvidia-ontology-management.
Instructions
- Call readiness before asking a question.
- Discover terms and confirm answerability through the semantic layer.
- Ask through MCP, or use the authenticated Next.js REST gateway as fallback.
- Validate material intent, SQL, rows, truncation, and prose before presenting a claim-bearing answer.
Prefer MCP for reads
When the harness speaks MCP, use it. The handshake tool list is the live
source of truth (mcp/auto_ontology_mcp/tools.py). Do not copy a tool table into the
session; if the handshake and this skill disagree, trust the handshake.
Sequence (the server also advertises this so models do not jump straight to
ask_question):
check_readiness— compiled semantic layer and a live DB connection.search_terms— what the nouns mean here.check_answerable— cheap coverage check.ask_question— full text-to-SQL; tens of seconds; answer + SQL + rows (capped at 100 rows, withrow_count/truncated).- For a claim-bearing answer, validate material intent, generated SQL, returned rows, truncation, and prose using query-validation.md.
Everything on MCP reads. There is no MCP tool for raw databases, schemas,
or columns on purpose. describe_table is the legitimate table view: terms
and SQL attributes the table participates in.
Connect: AUTO_ONTOLOGY_API_URL is the web app. Run auto-ontology-mcp, point the client at
…/mcp, user signs in. No token on the MCP server. Details:
repository mcp/README.md and docs/mcp.md.
REST fallback and write handoff
When MCP is unavailable, public clients call the authenticated Next.js
gateway, not FastAPI
:3001. FastAPI trusts x-auto-ontology-user-id and must not be exposed. A direct
FastAPI request without conversation_id is stateless; with
conversation_id it requires that internal header.
Auth (any of these; resolved in one place):
- Browser session cookie
- API token:
x-api-key: $AUTO_ONTOLOGY_API_TOKEN(Authorization: Bearerworks forauto_ontology_…tokens too). Token acts as its owner. - OAuth bearer issued by Auto Ontology (MCP sign-in)
- SSO id token (
Authorization: Bearer <jwt>), e.g. AI-Q — see stack.md
Shapes: docs/openapi/auto-ontology-api.json. Do not scrape all ~87 operations. A write
request hands off to nvidia-ontology-management; this skill does not turn a read-only
question into a mutation.
Show full SKILL.md (291 more words)Show less
Chat
POST /api/chat/completions — SSE step / result / error / charts,
then [DONE]. Permission chat:use; a request with conversation_id also
needs conversation:write, or it gets 403.
- Omit
conversation_idfor a one-shot (no history, no chart step). - Supply a client-generated UUID to create or continue a thread (needs
chat:useandconversation:write). - Wait for
[DONE]before the next turn; overlapping requests return409 Conversation in progress. - A 404 on a conversation id means it belongs to another user.
Python (from the Auto Ontology README; no extra SDK):
import os
import requests
session = requests.Session()
session.headers["x-api-key"] = os.environ["AUTO_ONTOLOGY_API_TOKEN"]
terms = session.get("https://ontology.example.com/api/terms").json()
answer = session.post(
"https://ontology.example.com/api/chat/completions",
json={"question": "How many orders shipped last week?"},
)
# response is SSE, not a single JSON objectOpen an API-created thread in the UI at /chat?focus=<conversation_id> when
it belongs to the signed-in user.
Discovery (semantic layer, not a catalog browser)
MCP first. REST fallback:
GET /api/terms(query,skip,limit)GET /api/terms/{term_id}GET /api/exploration/tables/{table_id}/detailsGET /api/exploration/graph
Writes and compile reset: nvidia-ontology-management.
Validate claim-bearing answers
Do not treat successful execution as sufficient evidence. Before presenting a decision-facing result, apply query-validation.md to check population, measure, unit, grain, joins, lineage, status, validity, rows, truncation, and answer prose. If a material constraint cannot be verified, return the gap rather than a stronger claim.
Empty or failed answers
ask_question / chat returned nothing useful → check_readiness (or
GET /api/semantic-compilation/status) before rewriting the question. A
named database is not proof SQL can execute. See nvidia-ontology-setup
troubleshooting.
Examples
- For a grounded count, run
check_readiness,search_terms,check_answerable, and thenask_question. - If a response is truncated or violates measure grain, refuse the complete claim and follow query-validation.md.
Troubleshooting
For empty or failed answers, check readiness and compilation status before rephrasing. For REST errors, verify the public web origin, authentication, and conversation ownership before retrying.
See also
- stack.md — AI-Q, Nemotron, cuOpt (what exists vs what partners wire)
- query-validation.md — material intent, SQL, rows, and answer checks
nvidia-ontology-management— model, modify, and publish through the layernvidia-ontology-setup— bring-up and MCP OAuth discovery
© 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 7 other files (references, assets) in skills/nvidia-ontology-query of NVIDIA/skills.
- SKILL.md
- BENCHMARK.md
- assets/runtime-contract.yaml
- evals/evals.json
- references/query-validation.md
- references/stack.md
- skill-card.md
- skill.oms.sig
Open the folder on GitHubat commit dfdd080
Compare with similar skills
Nvidia Ontology Query 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 |
|---|---|---|---|---|---|---|
| Nvidia Ontology Query this skillNVIDIA/skills | 3.5k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Sap Hana CLIsecondsky/sap-skills | 462 | — | ~3.1k | Automated safety check: Notes | GPL-3.0 | |
| Tune Monitorsickn33/agentic-awesome-skills | 47k | 1 repos | ~3.3k | Automated safety check: Pass | Apache-2.0 | |
| Dune MCP Skillholon-run/uxc | 116 | — | ~1.2k | Automated safety check: Pass | MIT | |
| Semantic Analystsidequery/sidemantic | 129 | — | ~982 | Automated safety check: Pass | AGPL-3.0 | |
| Pytorch Clickhousepytorch/test-infra | 113 | — | ~2.8k | Automated safety check: Pass | Custom licence |
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Related
Categories
Questions about Nvidia Ontology Query
What does Nvidia Ontology Query do?
Query Auto Ontology and validate generated SQL, rows, and answers. Nvidia Ontology Query is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Query Auto Ontology and validate generated SQL, rows, and answers.
When should I use Nvidia Ontology Query?
Nvidia Ontology Query fits situations like: grounded questions; tasks that involve SQL; tasks that involve MCP servers.
How do I install Nvidia Ontology Query in Claude Code?
Run `npx skills add NVIDIA/skills --skill nvidia-ontology-query -a claude-code`. Or copy the skill folder (skills/nvidia-ontology-query in NVIDIA/skills) into .claude/skills/nvidia-ontology-query in your project. Claude Code loads it when a task matches its description.
How do I install Nvidia Ontology Query in Codex?
Run `npx skills add NVIDIA/skills --skill nvidia-ontology-query -a codex`. Or copy the skill folder (skills/nvidia-ontology-query in NVIDIA/skills) into .agents/skills/nvidia-ontology-query in your project. Codex loads it when a task matches its description.
Can I use Nvidia Ontology Query 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-query -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-query, .gemini/skills/nvidia-ontology-query, .github/skills/nvidia-ontology-query and .opencode/skills/nvidia-ontology-query in your project.
What does Nvidia Ontology Query need to run?
Going by SKILL.md and its folder, Nvidia Ontology Query needs credentials named AUTO_ONTOLOGY_API_TOKEN. Our summary lists: Python 3; A credential in AUTO_ONTOLOGY_API_TOKEN.
Does Nvidia Ontology Query 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 Query 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 Query use?
Nvidia Ontology Query 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 Query use?
About 1.7k tokens (SKILL.md is roughly 6.6k 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 1.6k tokens, read only when the agent opens those files.
What are the alternatives to Nvidia Ontology Query?
Skills that share tags, products or a category with Nvidia Ontology Query: Sap Hana CLI (secondsky/sap-skills, 462 stars), Tune Monitor (sickn33/agentic-awesome-skills, 47k stars), Dune MCP Skill (holon-run/uxc, 116 stars) and Semantic Analyst (sidequery/sidemantic, 129 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Nvidia Ontology Query?
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,546 GitHub stars. The repository holds 386 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.