Official agent skill

Nemoclaw Maintainer Audit E2E Assertions

by NVIDIA in NVIDIA/NemoClaw

Triage, diagnose, debug, or fix failing or flaky NemoClaw E2E tests.

OfficialApache-2.0Auto-check passedTesting & QA

Install Nemoclaw Maintainer Audit E2E Assertions

skills CLI
$ npx skills add NVIDIA/NemoClaw --skill nemoclaw-maintainer-audit-e2e-assertions -a claude-code

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

GitHub CLI
$ gh skill install NVIDIA/NemoClaw nemoclaw-maintainer-audit-e2e-assertions --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/NemoClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/nemoclaw-maintainer-audit-e2e-assertions .claude/skills/nemoclaw-maintainer-audit-e2e-assertions && 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
nemoclaw-maintainer-audit-e2e-assertions
GitHub stars
23k
Token cost
~2.5k tokens
SKILL.md length
1,340 words
Files
2
Skills in repo
30
Repo updated
First seen
Licence
Apache-2.0

At a glance

Triage, diagnose, debug, or fix failing or flaky NemoClaw E2E tests.

  • Tasks that involve End-to-end testing
  • SKILL.md covers When to Use This Skill, Keep the Requested Boundary, Establish What Actually Failed and Enumerate the Entire Remaining…, plus 4 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Nemoclaw Maintainer Audit E2E Assertions is an agent skill from NVIDIA/NemoClaw, published by the product's own GitHub organization. Triage, diagnose, debug, or fix failing or flaky NemoClaw E2E tests. Trace every assertion and downstream gate before a repair push or rerun. Excludes status-only and dispatch-only requests.

Its SKILL.md is about 2.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Testing & QA, covering End-to-end testing. The repository describes itself as: Run agents like Hermes, LangChain Deep Agents, and OpenClaw more securely inside NVIDIA OpenShell with managed inference. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve End-to-end testing

Example prompts

  • “/nemoclaw-maintainer-audit-e2e-assertions”

What it can do on your machine

Read from SKILL.md and the folder at commit 5f0b6b6. 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

Nemoclaw Maintainer Audit E2E Assertions loads about 2.5k tokens when it runs. Until then it costs about 58 tokens; SKILL.md has 1,340 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~2.5k

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/NemoClaw at commit 5f0b6b6, republished under its Apache-2.0 licence (© NVIDIA). 1,340 words, ~2,476 tokens.

Download SKILL.mdSave it as .claude/skills/nemoclaw-maintainer-audit-e2e-assertions/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
nemoclaw-maintainer-audit-e2e-assertions
description
Triage, diagnose, debug, or fix failing or flaky NemoClaw E2E tests. Trace every assertion and downstream gate before a repair push or rerun. Excludes status-only and dispatch-only requests.
license
Apache-2.0
<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->
<!-- SPDX-License-Identifier: Apache-2.0 -->

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.

Read the complete failed-job logs, including setup, earlier warnings, the failure, teardown, and upload. Read large logs in sequential chunks. Do not substitute a tail excerpt, search matches, or an automated summary for the complete log. Retain useful evidence with secrets redacted. If logs are missing or incomplete, record the gap instead of claiming they were reviewed.

Find the first causal failure. Separate it from later cleanup errors and failures caused by missing outputs. Mark the last completed assertion and the first unreached assertion. An unreached assertion has supplied no execution evidence.

Enumerate the Entire Remaining Contract

Read each in-scope test from setup through final cleanup. Follow its helpers, fixtures, wrappers, shell scripts, and workflow steps. Continue beyond the currently failing line and the current job.

Assign a stable ID to every assertion and independently meaningful predicate. Include:

  • Compound success expressions, helper assertions, exit codes, output parsing, and polling deadlines.
  • Scenario variants, matrix inputs, loops, alternate agent/runtime paths, and negative checks.
  • Restore, restart, rejection without mutation, destruction, retention, and repeated cleanup.
  • Evidence serialization, target completion, artifact upload, and dependent qualification/publication gates.
  • Prerequisite gates affected by the proposed diff, including test parity, build inputs, and generated files.

Preserve the user's checklist IDs when provided. Expand combined entries into explicit subitems. Count predicate rows, not only calls to expect. A shared helper requires each distinct caller's inputs to be checked. Reuse common reasoning with links; do not mark an entire scenario covered by its name.

Trace Every Predicate Through Its Actual Inputs

For each row, work backward from the expected condition to the code that produces its values. Then walk forward with the fixture's concrete inputs through the selected branches and state changes. Record the resulting value or invariant and why it meets the predicate.

Inspect overrides, defaults, legacy/null values, persisted state, environment propagation, and argument order wherever they affect that result. Check the meaning of an argument as well as its type. For example, an inference serving port and a gateway state namespace can both be numbers but are not interchangeable.

At an external boundary, inspect the pinned dependency or image version when its behavior controls the assertion. Do not rely on remembered APIs or the latest upstream implementation for a pinned runtime. Trace parsing and status propagation back to the actual producer, including stdout/stderr separation.

For lifecycle assertions, record state before and after the operation and the resources owned by each cleanup path. Follow the state into later assertions: successful creation alone does not establish successful restore or destruction.

For each predicate, identify a concrete way it could be false under those inputs. Check that path. Resolve contradictions supported by the source; label remaining external conditions precisely. Do not invent unrelated failure scenarios or broaden the repair into hardening work.

Show full SKILL.md (576 more words)Show less

Maintain the Assertion Ledger

Use one row per predicate and scenario, or linked subrows where needed:

ID and assertion locationExpected conditionActual inputs and stateProducing path and reasoningCorrectionEvidence status

Include file/line references and commit or run identities for the evidence. Itemize what you inspected, what you concluded, and what changed. Entries such as “reviewed,” “same helper,” or “should pass” are insufficient.

Keep source reasoning and runtime evidence separate:

  • Source supported: the traced inputs and implementation satisfy the predicate under named runtime assumptions.
  • Observed on candidate: retained execution evidence establishes the predicate for the stated candidate and scenario.
  • Observed previously: supporting evidence from another commit or scenario; it does not establish a candidate pass.
  • Runtime pending: name the unavailable observation, such as measured GPU memory, network response, or process exit.
  • Unresolved: a contradiction, unknown input, or unread code path prevents a supported conclusion.

A row can be source supported and runtime pending. Do not use runtime pending to hide code you have not inspected. Successful aggregate output proves component predicates only after checking the conjunction and all assignments and return paths that can produce that output.

Batch the Supported Repairs

When repairs are authorized, fix the demonstrated cause and other source-supported blockers within the requested boundary. Attach each change to the ledger rows it resolves. Avoid speculative refactors.

Use the existing assertions for this review. Do not write new regression tests as a substitute for understanding the remaining path. Honor restrictions on test changes. If a harness input is wrong, explain the mismatch against the product contract before correcting it within the authorized scope. Preserve the asserted behavior, thresholds, and deadlines; do not weaken them to obtain a pass.

After edits, inspect every affected caller and ledger row again. Review changed-file prerequisite gates before publication. Use applicable existing fast checks when permitted; they supplement the source review. Do not start a live environment or an expensive CI run merely to discover the next source-visible failure.

Finish the Review Before the Next Push or Run

Before any repair push or expensive rerun, establish all of these conditions:

  • Every in-scope assertion and prerequisite has a ledger entry, including unreached paths and cleanup.
  • Each entry has concrete inputs, producing code, and a supported conclusion or precisely named runtime dependency.
  • No source contradiction, unknown code path, or unreviewed affected caller remains.
  • The final diff and applicable prerequisite gates have been checked; every fix maps to evidence.
  • Remaining runtime uncertainty and the authorized execution needed to resolve it are stated explicitly.

Do not claim “last assertion” until the remaining helpers, teardown, workflow steps, and dependent jobs have been accounted for through the user's completion boundary.

If the request is only an audit, deliver the ledger and findings here. If publication and validation are already authorized, continue without requesting the same permission again. Use the repository E2E execution workflow only when execution is requested; this skill does not grant additional environment access.

Judge the subsequent run against the completed ledger. A failure requires new causal evidence and an update to all affected rows before another repair push. Distinguish a missed source obligation from a runtime condition that inspection could not establish. Do not automatically retry unchanged failures.

Report source-review completion separately from execution success. Declare the requested suite green only when all required jobs and scenario variants pass for the final candidate. Identify skipped, missing, cancelled, or older results explicitly. Retain historical evidence as historical when the candidate changes.

© 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 1 other file in .agents/skills/nemoclaw-maintainer-audit-e2e-assertions of NVIDIA/NemoClaw.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 5f0b6b6

Compare with similar skills

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Categories

Questions about Nemoclaw Maintainer Audit E2E Assertions

What does Nemoclaw Maintainer Audit E2E Assertions do?

Triage, diagnose, debug, or fix failing or flaky NemoClaw E2E tests. Nemoclaw Maintainer Audit E2E Assertions is an agent skill from NVIDIA/NemoClaw, published by the product's own GitHub organization. Triage, diagnose, debug, or fix failing or flaky NemoClaw E2E tests.

When should I use Nemoclaw Maintainer Audit E2E Assertions?

Nemoclaw Maintainer Audit E2E Assertions fits situations like: tasks that involve End-to-end testing.

How do I install Nemoclaw Maintainer Audit E2E Assertions in Claude Code?

Run `npx skills add NVIDIA/NemoClaw --skill nemoclaw-maintainer-audit-e2e-assertions -a claude-code`. Or copy the skill folder (.agents/skills/nemoclaw-maintainer-audit-e2e-assertions in NVIDIA/NemoClaw) into .claude/skills/nemoclaw-maintainer-audit-e2e-assertions in your project. Claude Code loads it when a task matches its description.

How do I install Nemoclaw Maintainer Audit E2E Assertions in Codex?

Run `npx skills add NVIDIA/NemoClaw --skill nemoclaw-maintainer-audit-e2e-assertions -a codex`. Or copy the skill folder (.agents/skills/nemoclaw-maintainer-audit-e2e-assertions in NVIDIA/NemoClaw) into .agents/skills/nemoclaw-maintainer-audit-e2e-assertions in your project. Codex loads it when a task matches its description.

Can I use Nemoclaw Maintainer Audit E2E Assertions 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/NemoClaw --skill nemoclaw-maintainer-audit-e2e-assertions -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nemoclaw-maintainer-audit-e2e-assertions, .gemini/skills/nemoclaw-maintainer-audit-e2e-assertions, .github/skills/nemoclaw-maintainer-audit-e2e-assertions and .opencode/skills/nemoclaw-maintainer-audit-e2e-assertions in your project.

What does Nemoclaw Maintainer Audit E2E Assertions need to run?

SKILL.md names no scripts, command-line tools or credentials: Nemoclaw Maintainer Audit E2E Assertions is instructions for the agent only.

Does Nemoclaw Maintainer Audit E2E Assertions 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 Nemoclaw Maintainer Audit E2E Assertions 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 Nemoclaw Maintainer Audit E2E Assertions use?

Nemoclaw Maintainer Audit E2E Assertions 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 Nemoclaw Maintainer Audit E2E Assertions use?

About 2.5k tokens (SKILL.md is roughly 9.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Nemoclaw Maintainer Audit E2E Assertions?

Skills that share tags, products or a category with Nemoclaw Maintainer Audit E2E Assertions: Web Application Testing (anthropics/skills, 180k stars), TDD Workflow (hellangleZ/burn-in-cceverywhere-ralph, 112 stars), Uloop Replay Input (kurotu/VRCQuestTools, 373 stars) and Ui4 Convert Tests (payloadcms/payload, 45k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nemoclaw Maintainer Audit E2E Assertions?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/NemoClaw, which has 22,686 GitHub stars. The repository holds 30 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA/NemoClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.