nemoclaw-user-agent-skills

$npx mdskill add NVIDIA/skills/nemoclaw-user-agent-skills

Expose NemoClaw agent capabilities via cloned repository skills.

  • Helps developers understand available AI agent support and coding tools.
  • Integrates with cursor, Claude Code, and Copilot workflows.
  • Decides recommendations by referencing the .agents/skills/ directory.
  • Delivers clear instructions on cloning the repository for access.

SKILL.md

.github/skills/nemoclaw-user-agent-skillsView on GitHub ↗
---
name: "nemoclaw-user-agent-skills"
description: "Describes the agent skills shipped with NemoClaw and how to access them by cloning the repository. Use when users ask about AI agent support, coding assistant integration, or the .agents/skills/ directory. Trigger keywords - nemoclaw agent skills, ai coding assistant, cursor, claude code, copilot."
---

<!-- SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -->
<!-- SPDX-License-Identifier: Apache-2.0 -->

# NemoClaw Agent Skills for Your AI Coding Assistant

## References

- **Load [references/agent-skills.md](references/agent-skills.md)** when users ask about AI agent support, coding assistant integration, or the .agents/skills/ directory. Describes the agent skills shipped with NemoClaw and how to access them by cloning the repository.

More from NVIDIA/skills

SkillDescription
accessing-mlflowQuery and browse evaluation results stored in MLflow. Use when the user wants to look up runs by invocation ID, compare metrics across models, fetch artifacts (configs, logs, results), or set up the MLflow MCP server. ALWAYS triggers on mentions of MLflow, experiment results, run comparison, invocation IDs in the context of results, or MLflow MCP setup.
ad-add-fusion-transformation>
ad-conf-check>
ad-graph-dump>
ad-model-onboard>
ad-pipeline-failure-pr>
add-benchmark>
aiq-deploy|
aiq-research|
byobCreate custom LLM evaluation benchmarks using the BYOB decorator framework. Use when the user wants to (1) create a new benchmark from a dataset, (2) pick or write a scorer, (3) compile and run a BYOB benchmark, (4) containerize a benchmark, or (5) use LLM-as-Judge evaluation. Triggers on mentions of BYOB, custom benchmark, bring your own benchmark, scorer, or benchmark compilation.