NVIDIA Elements is a design system and agent-oriented toolkit for building operational interfaces for AI/ML, robotics, and autonomous-vehicle software. Its CLI and MCP tools provide component APIs, design tokens, examples, imports, validation, and setup guidance across several web frameworks.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA/elements --skill audit-cigit clone --depth 1 https://github.com/NVIDIA/elementsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia/elements/audit-ci)<a href="https://agentmods.dev/skills/nvidia/elements/audit-ci"><img src="https://agentmods.dev/badge/skills/nvidia/elements/audit-ci/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia/elements/audit-ci"><img src="https://agentmods.dev/badge/skills/nvidia/elements/audit-ci.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 114 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00078 | $0.01411 |
| Opus 5.5 | $0.00031 | $0.00564 |
| Sonnet 5.5 | $0.00016 | $0.00282 |
| Haiku 4.5 | $0.00008 | $0.00141 |
Grade A, and why
audit-ci scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit CI
Produce a repeatable cold-CI profile, explain what controls wall-clock completion, and turn the evidence into prioritized, testable recommendations.
Required context
- Read the repository
AGENTS.md. - Read
projects/internals/BUILD.md. - Inspect the current root
ci:profilescript andprojects/internals/ci/ci-profile.js. Treat the script as the profiling source of truth. - Read a project's
DEVELOPMENT.mdbefore running project-specific commands when that file exists.
Do not copy profiling logic into this skill. Update the repository profiler when its behavior needs to change.
Choose the workflow
- Run a new profile when the user asks to rerun, benchmark current changes, refresh an audit, or verify an optimization.
- Analyze existing
.metrics/ci-profile.{json,md}artifacts without rerunning when the user asks only for interpretation and the artifacts match the intended commit and worktree state. - Create recommendations without implementing them unless the user also asks for the changes.
Profile safely
Run all repository commands through mise.
-
Inspect
git status --short. -
Preview ignored files that reset would delete with
git clean -ndX. -
Stop and ask before profiling if that preview includes user data, local assets, secrets, or other non-reproducible files. The profiler runs
pnpm run ci:reset, which deletes ignored files and reinstalls dependencies before every sample. -
Never stash, commit, discard, or clean tracked changes merely to make the profiler accept the worktree.
-
Use the clean command when the worktree is clean:
mise exec -- pnpm run ci:profile -
When the dirty changes are the intentional subject of the audit, preserve them and run:
CI_PROFILE_ALLOW_DIRTY=1 mise exec -- pnpm run ci:profileRecord the dirty-worktree condition in the report. Do not use this override for unrelated or unexplained changes.
Allow all three cold samples to finish. Dependency installation or browser setup can require network access. If a run fails, inspect the copied .metrics/ci-profile-run-*.log and reset logs, report the incomplete profile, and do not invent missing samples.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 139 lines · 78 tokens per session scan A e8050249f9a1
audit-ci is a skill published in the GitHub repository NVIDIA/elements (102 stars, last pushed today), licensed Apache-2.0. It adds 78 tokens to every session and 1,411 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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