NVIDIA/NemoClaw is an open-source reference stack for running supported AI agents inside NVIDIA OpenShell sandboxes with managed inference, network policies, integrations, snapshots, and lifecycle controls. It is used to run agents such as OpenClaw, Hermes, and LangChain Deep Agents with administrative and security controls. Catalogue add-ons guide coding agents through NemoClaw workflows.
Borrowing it
Nothing to install: this file belongs to NVIDIA/NemoClaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/NVIDIA/NemoClaw/main/.agents/skills/nemoclaw-maintainer-audit-e2e-assertions/SKILL.mdgit clone --depth 1 https://github.com/NVIDIA/NemoClawWrote 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/nemoclaw/nemoclaw-maintainer-audit-e2e-assertions)<a href="https://agentmods.dev/skills/nvidia/nemoclaw/nemoclaw-maintainer-audit-e2e-assertions"><img src="https://agentmods.dev/badge/skills/nvidia/nemoclaw/nemoclaw-maintainer-audit-e2e-assertions/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/nemoclaw/nemoclaw-maintainer-audit-e2e-assertions"><img src="https://agentmods.dev/badge/skills/nvidia/nemoclaw/nemoclaw-maintainer-audit-e2e-assertions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00057 | $0.01826 |
| Opus 5.5 | $0.00023 | $0.00730 |
| Sonnet 5.5 | $0.00011 | $0.00365 |
| Haiku 4.5 | $0.00006 | $0.00183 |
Grade A, and why
nemoclaw-maintainer-audit-e2e-assertions 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 27d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Audit Every E2E Assertion Before Rerunning
Use the existing suite as the specification for a complete source review. Find the visible failure and later source-visible failures before the next expensive run. Aim to batch the necessary repairs; source inspection cannot guarantee runtime success.
The deliverable is an itemized assertion ledger with concrete inputs, producing code, and evidence. Build the ledger yourself. Do not ask the user to enumerate assertions or supervise its completion.
Follow the shared writing and review contract for the ledger and reports.
When to Use This Skill
Use this skill whenever investigating a failing or flaky E2E test, including timeouts, setup failures, assertion failures, and cleanup failures. Apply it when E2E failures emerge during broader PR or CI work, even when the user does not name this skill. Begin the review before proposing a repair or rerun.
A log classifier can locate evidence, but its summary does not complete the assertion audit. The E2E execution skill continues to own dispatch and environment authorization. Status-only queries and requests only to start a run do not require an assertion audit.
Keep the Requested Boundary
Use the assigned branch and checkout. Preserve the user's restrictions on edits, tests, execution environments, command visibility, and publication. Reuse established authorization and context. An audit request alone does not authorize code changes, pushes, live execution, or merges.
Review the requested failing suite and the prerequisites and downstream jobs needed for its stated completion. Do not turn a scoped repair into repository-wide maintenance or add unrelated checks. For audit-only work, report proposed corrections without applying them.
Establish What Actually Failed
Record the candidate commit, run attempt, failing jobs, relevant artifact identities, and requested completion boundary. Reuse known values; verify identities when evidence might belong to another commit. Distinguish trusted workflow/controller code from the candidate code and images actually exercised.
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.
- 27d ago First seen · 154 lines · 57 tokens per session scan A 03dc667882ca
nemoclaw-maintainer-audit-e2e-assertions is a skill published in the GitHub repository NVIDIA/NemoClaw (22,871 stars, last pushed today), licensed Apache-2.0. It adds 57 tokens to every session and 1,826 once invoked, about $0.0002 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 2026-10-02.
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