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nemo-evaluator-plugin
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
审核状态：已审核Quality 72Security 70
项目 README
来源文件：README.md
NVIDIA Agent Skills
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
📖 Docs: docs.nvidia.com/skills · 📺 Livestream: From Vulnerable to Verified · 📝 Blog: NVIDIA Verified Agent Skills: Capability Governance for AI Agents
Skills are portable instruction sets that teach AI agents how to use NVIDIA software optimally: Physical AI and robotics workflows, simulation, CUDA-X libraries, RAG and AI Blueprints, and platform tools. This repository is a catalog: skills are maintained in their respective product repos, and mirrored here daily via an automated sync pipeline. Skills are being added continuously, so check back for updates. We are building this infrastructure in the open, and contributions are welcome. See the Roadmap for what is planned next.
Quickstart
Install NVIDIA skills with the default skills CLI flow:
npx skills add nvidia/skills
The CLI runs through npx and prompts you to choose a skill and install destination. You do not need to clone this repo or copy skill folders by hand.
Requires a current
skillsCLI (v1.5.16 or newer). Installing vianpx skills@latest add nvidia/skillsalways uses the latest. On older CLIs (v1.5.15 and earlier), skills may install but not appear in Claude Code — see Troubleshooting.
The skill is available the next time your agent loads skills and encounters a relevant task. For example, ask your agent to "solve a linear programming problem with cuOpt" and the skill guides it through the cuOpt Python API. In Claude Code, run /reload-skills to load newly installed skills in your current session.
Install One Skill Without Prompts
Use this when you already know the skill name and want to skip prompts.
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --yes
Replace cuopt-numerical-optimization-api with any skill name from the Skill Catalog.
Install for a Specific Agent
Use --agent to target a specific AI coding agent. Initially, we'll support common client targets, expanding the list over time. For the full list of clients supported by the spec, see the skills CLI Supported Agents table.
Claude Code
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent claude-code
Codex
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent codex
Snowflake CoCo
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cortex
Cursor
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent cursor
Kiro
npx skills add nvidia/skills --skill cuopt-numerical-optimization-api --agent kiro-cli
Use --agent more than once to install the same skill into multiple agents.
npx skills add nvidia/skills \
--skill cuopt-numerical-optimization-api \
--agent claude-code \
--agent codex \
--agent cursor \
--agent kiro-cli
Keep Skills Up to Date
New skills land continuously, and existing ones are revised, renamed, or consolidated as the catalog evolves. Refresh what you have installed with:
npx skills update
Run it interactively and the CLI also flags skills that were removed or merged upstream (for example, when several skills are consolidated into one) and offers to remove the stale local copies. Use npx skills list to see what is installed and npx skills check to preview what is out of date first.
Browse the Catalog
Use this when you want to see available NVIDIA skills before installing anything.
npx skills add nvidia/skills --list
For non-interactive installs, global installs, agent-specific installs, updates, removals, and fallback manual copying, see Advanced installation.
Skill Catalog
Getting Help & Contributing
Where to file an issue depends on what's broken:
- Skill content issues (a specific skill has a bug, missing functionality, or incorrect content) — file in the source repo for that product, using the per-product table below.
- Catalog issues (catalog README errors, sync workflow problems, distribution channels, signing/verification flow, docs in this repo) — file here using the catalog issue templates: Bug Report, Feature Request, or Documentation Request or Correction.
- Questions or general discussion — use Discussions. The issue tracker is reserved for bug reports, feature proposals with a design, and documentation issues.
- Security vulnerabilities — follow the disclosure process in SECURITY.md; do not open a public issue.
Per-product source repo links:
For issues with this catalog repo itself (README, structure, listing a new product): open an issue here.
Verifying Skills
Every published skill ships with a detached OMS signature (skill.oms.sig). The sync pipeline drops any skill missing the required artifacts before publishing, so every skill in the catalog carries:
SKILL.md— the skill instructions consumed by the agentskill-card.md— skill identity and governance cardskill.oms.sig— detached OMS signature (verifiable againstnv-agent-root-cert.pem)- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json BENCHMARK.md— generated benchmark report capturing verifiable uplift data
Verify a skill against the NVIDIA trust anchor nv-agent-root-cert.pem:
pip install model-signing
model_signing verify certificate SKILL_DIR \
--signature SKILL_DIR/skill.oms.sig \
--certificate_chain nv-agent-root-cert.pem \
--ignore_unsigned_files
A successful verification confirms that the skill contents have not been modified since signing by NVIDIA.
See Verify Signed Agent Skills for signature layout, the trust pipeline, and policy options.
Roadmap
- ✅ Public skills catalog with NVIDIA-verified skills across multiple products
- ✅ Automated sync pipeline with skills mirrored from product repos daily
- ✅ Security scanning for all published skills covering instruction safety and supply-chain integrity
- ✅ Skills signing so every published skill carries a verifiable NVIDIA signature
- ✅ Skills universal evaluation criteria and task-specific criteria
- ✅ Skill Card with machine-readable metadata for identity, provenance, quality, and behavioral boundaries
- ✅ Sync-time compliance gates — signature drift detection and missing-artifact enforcement
- ✅ Syndication to external marketplaces — Skills.sh, Codex plugin, Claude Code plugin, ClawHub, Hermes Hub
- 🔲 Syndication to additional MCP hubs and partner channels
Repository Structure
NVIDIA/skills/
├── skills/ # NVIDIA-verified skills (count grows continuously),
│ │ synced from upstream product repos
│ ├── README.md # Browser-facing install guidance
│ ├── <product-prefix>-*/ # Flat layout — one dir per skill, product-prefixed
│ │ # e.g. aiq-*, cuopt-*, cupynumeric-*,
│ │ # dali-*, deepstream-*, dicom-*, digital-health-*,
│ │ # dynamo-*, earth2studio-*, holoscan-*, hsb-*,
│ │ # jetson-*, launch-nemo-rl, mcore-*,
│ │ # nemo-automodel-*, nemo-data-designer-plugin,
│ │ # nemo-evaluator-plugin, nemo-mbridge-* (20 skills),
│ │ # nemo-retriever, nemo-rl-* (4 skills),
│ │ # nemoclaw-user-guide, nemotron-*, nemotron-speech,
│ │ # nv-* (medical AI), physicsnemo-*, rag-*,
│ │ # skill-card-generator, tao-*, tilegym-*,
│ │ # vss-* (15 skills), accelerated-computing-cudf,
│ │ # cudaq-guide, portfolio-optimization
│ ├── omniverse-*/ # Physical AI — manually staged (see manual-components.yml)
│ └── physical-ai-*/ # Physical AI — manually staged
├── components.d/ # Product registry — one file per component, teams onboard here
│ ├── README.md # Schema and onboarding instructions
│ └── <product>.yml # one file per registered product
├── plugins/ # Packaged plugin distributions
│ └── nvidia-skills/ # Curated NVIDIA skills bundle (Claude Code, Codex)
├── plugins.d/ # Plugin build registry — config for `build-plugins.py`
│ ├── README.md
│ ├── _defaults.yml
│ └── nvidia-skills.yml
├── .claude-plugin/ # Claude Code marketplace metadata
│ └── marketplace.json
├── .agents/plugins/ # Agent marketplace metadata (other clients)
│ └── marketplace.json
├── docs/ # Long-form documentation (published via Fern)
│ ├── README.md # How to build the docs locally
│ ├── index.mdx
│ ├── advanced-install.mdx
│ ├── agent-skill-trust-pipeline.mdx
│ ├── release-checklist.mdx
│ ├── scanning-agent-skills.mdx
│ ├── signing-agent-skills.mdx
│ └── skill-cards.mdx
├── fern/ # Fern docs site configuration
├── .github/
│ ├── workflows/ # Sync pipeline, plugin validation, DCO check, author verify
│ └── scripts/ # regenerate-readme.sh, build-plugins.py,
│ # manual-components.yml (temp Physical AI catalog
│ # exception, removed after Computex 2026),
│ # marketplace/metadata.json (skill metadata sidecar)
├── nv-agent-root-cert.pem # Trust anchor for OMS signature verification
├── skills.sh.json # Skills.sh marketplace grouping config
├── CHANGELOG.md
├── CONTRIBUTING.md # Contribution guidelines
├── SECURITY.md # Security reporting policy
├── CODE_OF_CONDUCT.md # Community code of conduct
├── LICENSE-APACHE # Apache 2.0 (source code)
└── LICENSE-CC-BY-4.0 # CC BY 4.0 (documentation/skills)
Skills are maintained in their respective product repos (see the Source column in the Skill Catalog) and synced to this repo daily. Products only appear under skills/ after the sync pipeline confirms each skill carries:
skill.oms.sig— detached OMS-format signature (verifiable againstnv-agent-root-cert.pem)skill-card.md— skill identity and governance card- A Tier-3 evaluation dataset — accepted at
evals/evals.json,evals/*.json,eval/*.json, orbenchmark/evals.json
When evaluation runs produce a BENCHMARK.md, it ships alongside the skill so consumers can see verifiable benchmark uplift data.
Standards & Compatibility
This repository adheres to the Agent Skills specification:
- Skills are portable directories with a
SKILL.mdfile at their root. - Metadata uses YAML frontmatter with required
nameanddescriptionfields. - Skills follow a progressive disclosure model — lightweight metadata loads at startup, full instructions load on activation.
- Validate your skill using the
skills-refreference library.
License
Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
This code is dual-licensed with documentation/skills under the CC-BY-4.0 AND source code under Apache-2.0 license terms. The full license texts can be found in LICENSE-APACHE and LICENSE-CC-BY-4.0 respectively.
研究与检索Agent / MCP / Skill 创作
中风险
- 来源需自行核对维护者身份。
- 包含脚本或命令调用，安装前请复核。
- 可能需要外部 token、网络权限或第三方服务。
- 未检测到高风险命令。
- 扫描发现：1 条。
Codex — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/nemo-evaluator-plugin" 文件夹复制到 Codex 的 skills 目录中。
- 重启 Codex 让新的 skill 生效。
Codex — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Codex 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Codex 让新的 skill 生效。
Claude Code — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/nemo-evaluator-plugin" 文件夹复制到 Claude Code 的 skills 目录中。
- 重启 Claude Code 让新的 skill 生效。
Claude Code — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Claude Code 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Claude Code 让新的 skill 生效。
Cursor — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/nemo-evaluator-plugin" 文件夹复制到 Cursor 的 skills 目录中。
- 重启 Cursor 让新的 skill 生效。
Cursor — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Cursor 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Cursor 让新的 skill 生效。
GitHub Copilot — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/nemo-evaluator-plugin" 文件夹复制到 GitHub Copilot 的 skills 目录中。
- 重启 GitHub Copilot 让新的 skill 生效。
GitHub Copilot — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 GitHub Copilot 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 GitHub Copilot 让新的 skill 生效。
Windsurf — Git Clone 安装
- 安装前请先查看来源仓库和风险报告。
- 克隆仓库：git clone https://github.com/NVIDIA/skills.git
- 将 "skills/nemo-evaluator-plugin" 文件夹复制到 Windsurf 的 skills 目录中。
- 重启 Windsurf 让新的 skill 生效。
Windsurf — 手动复制安装
- 安装前请先查看来源仓库和风险报告。
- 从源仓库下载 SKILL.md 及相关文件。
- 在 Windsurf 的 skills 目录中创建新文件夹。
- 将所有 skill 文件复制到新文件夹中。
- 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: nemo-evaluator-plugin
description: Evaluate models, datasets, and agents with the NeMo Evaluator plugin. Use for metric selection, SDK checks, platform jobs, and result retrieval.
license: Apache-2.0
metadata:
owner: nemo-platform
author: nemo-platform
maturity: active
tags: [evaluation, metrics, agent-eval, nemo-platform]Evaluator Plugin
The Plugin CLI entrypoint is uv run nemo evaluator.
Purpose
Use this skill to choose an evaluation interface and metric, validate a minimal example, submit a NeMo Platform evaluation job, and retrieve its results.
Inputs
Establish these inputs before building an evaluation:
- Evaluation interface: dataset-driven vs. task-driven agentic evaluation
- Execution interface: standalone SDK evaluation or a durable NeMo Platform job.
- Pass/fail dataset examples: the smallest representative pass and failure cases.
- Metrics: the behaviors to score and the template fields they consume.
- Target: no target for offline scoring, or the model, agent, runner, or precomputed trials that produce outputs.
Instructions
- Clarify whether the input is dataset-driven rows or task-driven agent work.
- Choose the simplest metric that measures the requested behavior. Prefer deterministic metrics when possible.
- Build a tiny smoke case with one expected pass and one expected failure.
- Validate metric behavior with the standalone SDK and inspect row-level output plus aggregates.
- Fix field mappings, prompts, parsers, or task definitions before scaling.
- Submit the platform job only after the input and scoring shape works.
Read Metric Selection before choosing a metric for a rubric, RAG workflow, or tool-calling evaluation.
Choose the execution interface
| Need | Interface |
|---|---|
| Fast metric iteration without NeMo Platform | nemo_evaluator_sdk.Evaluator |
| Dataset-driven platform job | client.evaluator.submit(...) or nemo evaluator evaluate submit |
| Multiple inline/stored metric refs in one job | nemo evaluator evaluate submit with an EvaluateInputSpec |
| Task-driven platform job | nemo evaluator agent-evaluate submit |
| Reusable platform definitions and result indexes | client.evaluator.metrics, .tasks, .tasksets, .eval_results, .agent_eval_results |
Default to submit for every plugin evaluation. The plugin's local execution
path — client.evaluator.run() and the nemo evaluator ... run CLI verb — is
being retired, so do not build on it even though --help still lists it. For
fast metric iteration without the platform, use the standalone
nemo_evaluator_sdk.Evaluator instead.
- Read SDK Execution for datasets, targets, configuration, field mapping, job lifecycle, and custom metric packaging.
- Read Stored Resources for persisted definitions and result queries.
Limitations
api_key_secretis an environment-variable name standalone but a NeMo Platform secret name onsubmit. See API Auth.- HTTP 409 from a submission often means a referenced platform secret is missing, not a duplicate job. Read the response body.
intentis grader metadata and is never shown to the agent; onlyinputsreaches it.- Metric templates use
item.*for dataset rows butreference.*,sample.*, andinputs.*in agent evaluation. - Metric progress can reach 100 percent before the platform job is terminal.
Always call
job.wait_until_done()before retrieving results or downloading artifacts.
CLI Interface
Prerequisites
All commands in this file assume that the shell's working directory is the root of the NVIDIA-NeMo/nemo-platform repository.
In a NeMo Platform repository checkout, run commands through the workspace:
# confirms plugin readiness and lists the registered evaluator jobs.
uv run nemo evaluator info
# lists available metric names; add a metric name to print its schema.
uv run nemo evaluator metric-types
# next two commands print the dataset-driven and task-driven job input and
# output schemas - can be very large, use with caution to avoid filling up the context window.
uv run nemo evaluator evaluate explain
uv run nemo evaluator agent-evaluate explain
When the skill and plugin are installed, use the installed nemo command
without assuming a repository root or manually activating .venv.
Resolve bundled assets relative to this skill directory. In this repository the
canonical path is skills/nemo-evaluator-plugin; an installed skill may live
under a different skills root.
Bundled assets
| Path | Use |
|---|---|
assets/specs/exact_match_metric.json | Two-row offline smoke spec; submit as-is |
assets/specs/llm_as_judge.json | Online generation + judge; local-first (NVIDIA_API_KEY) |
assets/specs/fabric_agent_eval.json | Task-driven Fabric runner spec |
assets/examples/plugin_sdk_examples.py | Copyable SDK snippets for each plugin surface |
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/generate_example_specs.py | Generate or drift-check bundled specs | --check, --write |
In this repository, NeMo uses the displayed workspace command:
uv run --frozen python skills/nemo-evaluator-plugin/scripts/generate_example_specs.py --check
Do not assume a client-specific run_script() helper; use the displayed
uv run command.
Examples
Dataset-driven evaluation examples
- Follow Validate standalone, then submit to the platform. for the two-row pass/fail smoke test and its CLI submission.
- Follow Map noncanonical fields
when dataset columns need
field_mapping. - Follow Getting job results for submission, terminal waiting, result retrieval, and artifact download.
- Follow Store a metric, task, and taskset for reusable definitions, and Query persisted results for result lookup.
Task-driven agent evaluation examples
Standalone SDK evaluation
Use AgentEvaluator().run(...) for standalone task-driven SDK evaluation. Its
target can be a Model, a GenericAgent, or a direct AgentTaskRunner.
Platform job evaluation
Use the plugin agent-evaluate submit job for platform task evaluation. Its
target is a ModelTarget, AgentTarget, CodexRunnerTarget,
FabricRunnerTarget, or HarborRunnerTarget; alternatively provide
precomputed trials. Provide exactly one of target or trials.
Submission accepts inline tasks or a stored TasksetRef. Stored tasksets are
resolved in the target workspace.
Read Agent Evaluation for inline tasks,
TasksetRef, concurrency, fail-fast behavior, result artifacts, and runner
configuration.
Prepare Fabric in a repository checkout
Fabric runner examples and tests need the optional harness adapters and the matching Relay gateway:
uv sync --frozen --package nemo-evaluator-sdk --extra fabric --inexact
script/dev-install-fabric.sh
The install script downloads the checksum-verified nemo-relay binary that
matches the locked Python bindings. Add its reported directory to PATH, then
use uv run --frozen --no-sync ... for Fabric checks so uv does not remove the
optional adapters.
Output Format
Report a completed platform evaluation in this form:
Job: <job-name>
Status: <terminal-status>
Metrics: <metric-names>
Mean: <aggregate-mean>
Artifacts: <downloaded result or artifact location>
Errors: <error messages>
Read specialized references
- Read Evaluator API Auth before using a model, agent, remote metric, or durable submission.
- Read LLM Judge before writing judge scores, prompts, or parsers.
Troubleshooting
Read Evaluator troubleshooting when schema, authentication, job, result, or runner behavior fails.
Follow security best practices
Never print, serialize, or commit secret values. Store only environment-variable names or platform secret references in specs and examples.
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