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SkillAtlasSkill 详情
kermt-monitor
Official, NVIDIA-verified Agent Skills for Claude Code, Codex, and other coding agents.
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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.
DevOps 与部署
高风险
- 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。
- 存在潜在风险命令，请谨慎安装。
- 扫描发现：3 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/bionemo-kermt-monitor" 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/bionemo-kermt-monitor" 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/bionemo-kermt-monitor" 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/bionemo-kermt-monitor" 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/bionemo-kermt-monitor" 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: kermt-monitor
description: Check progress for a detached KERMT run (pretrain, finetune, or any kermt_run_detached invocation). Reads run.json, queries docker for container state, tails the pretrain/finetune log, and parses progress lines (epoch, step, val loss).
license: Apache-2.0
compatibility: Requires docker and jq. Designed for Claude Code, Codex, and Nemotron.
metadata:
owner: evax@nvidia.com
classification: atomic-skill
risk_tier: skill
# Line/token budget: this file is targeted at ~120 lines / ~1500 tokens — well
# within the 500-line / 5000-token cap for skill files.kermt-monitor
Companion skill for any KERMT workflow that runs detached: the three pretrain
skills (kermt-continue-pretrain, kermt-pretrain-scratch,
kermt-add-cmim-pretrain) plus kermt-finetune. kermt-infer and
kermt-embed run blocking by default and don't need this skill, but if a
user launches them detached on purpose the monitor still works (the
workflow-dispatch in step 4 handles unknown workflows by tailing the
most-recent log file in the run dir). Reads the run directory's run.json,
queries docker for the container's state, surfaces the latest progress,
and either tails or follows the log.
Hardware requirements
None. This skill only reads disk + queries docker; no GPU compute.
Inputs
One of:
<run-dir>— a positional argument pointing at the directory containingrun.json(e.g.runs/continue-pretrain_2026-05-17T10-23Z). Preferred.--container <name-or-id>— direct container reference; the skill still readsrun.jsonfrom the run dir referenced inside the container's inspect output if available, but works degraded-mode without it.
Optional:
--lines N— number of trailing log lines to print (default 50).--follow— streamdocker logs -funtil ^C. Useful for "watch the loss". Without it, the skill is one-shot and exits.--json— emit a structured status report instead of human-readable text. Useful when the parent agent wants to take downstream action.
Workflow
Let RUN_DIR=$1 (or whatever path the user supplies).
-
Locate the manifest.
MANIFEST=$RUN_DIR/run.jsonRefuse to proceed if it doesn't exist; surface a helpful message pointing the user at the run-dir convention (
runs/<workflow>_<ts>/). -
Parse the manifest (Python helper):
workflow=$(jq -r .workflow $MANIFEST) container_name=... # not directly in run.json today; the skill that # launched stored it in run.json under # container.name during launch (see below note). logs_dir=$(jq -r .logs_dir $MANIFEST) image_tag=$(jq -r .container.image_tag $MANIFEST) started_at=$(jq -r .started_at $MANIFEST) -
Query docker for container state.
docker ps --filter "name=$container_name" --format \ '{{.ID}}\t{{.Status}}\t{{.CreatedAt}}'If absent, fall back to
docker inspect $container_name --format '{{.State.Status}} (exit {{.State.ExitCode}})'to see whether the container exited (ok or failed) or was removed (--rmafter exit). -
Find the live log file.
case "$workflow" in continue-pretrain|pretrain-scratch) LOG=$logs_dir/pretrain_ddp.log ;; finetune) LOG=$logs_dir/finetune.log ;; *) LOG=$(ls -1t $logs_dir/*.log 2>/dev/null | head -n 1) ;; esacThe manifest's
workflowfield disambiguates pretrain (pretrain_ddp.log) from finetune (finetune.log). Other workflows fall back to the most-recently-modified.login$logs_dir. -
Show the latest progress.
tail -n $LINES $LOGfor the raw recent output.- Parse the last few progress lines and surface a human-friendly
summary. The format differs per workflow:
- Pretrain: epoch / step / val_loss
Current epoch: 12/100 step: 4523/9000 val_loss: 0.832 (best 0.821 @ step 4100) - Finetune: fold / epoch / val_ (e.g. val_mae for regression,
val_auc for classification — read
args_applied.metricfrom run.json)Fold 0 epoch 12/30 val_mae 0.187 (best 0.182 @ epoch 9)
Wall-clock: 1h 23m since started_at; ETA ~6h remaining. - Pretrain: epoch / step / val_loss
-
Final test-metrics block (finetune, on completion). If
workflowisfinetuneAND the container has exited cleanly (State.Status=exited,ExitCode=0) AND$RUN_DIR/ckpt/fold_*/test_result.csvexists, parse it and emit a per-task metric table:Final test metrics (per task): Target MAE HLM_clearance 0.187 RLM_clearance 0.213 MDR1-MDCK_efflux 0.241 solubility_pH6.8 0.156The metric column matches
args_applied.metric(mae for regression, auc for classification, etc.). For multi-fold or ensemble runs, average across folds/models and note± stdif std > 0. Skip silently if notest_result.csvexists (run incomplete or no test split was emitted). -
If
--follow, stream live logs.docker logs -f $container_nameWraps until ^C.
-
Stop / cleanup hints (printed at end of one-shot mode):
To stop: docker stop $container_name To remove: docker rm $container_name To re-run: `$(jq -r .cmd_replay $MANIFEST)`
Hard rules
- Read-only on the user's data. Never modify
run.json, never touch the container's checkpoint dir. The monitor only inspects. - Don't kill the container without explicit user instruction. If the
user asks to stop, run
docker stop; if they ask to abandon, leave it running and just exit. - Don't pull or modify the kermt image. The monitor only reads.
- JSON output mode is non-interactive. Skip the "press ^C to exit" prompts and emit a single JSON document so the parent agent can pipe it.
Note on container_name plumbing
The run.json schema as currently written does not yet include the launched
container name — kermt_run_detached prints it to stdout but the runner
script doesn't capture it into run.json. The monitor falls back to a
filesystem-based lookup: list runs/<workflow>_*/ directories and match by
mtime; or accept --container <name> explicitly. Follow-up: have the
launching skill record container name into run.json before exiting.
Output (text mode, default)
KERMT continue-pretrain · runs/continue-pretrain_2026-05-17T10-23Z
Container : kermt-continue-pretrain-… (Up 1 hour, status: running)
Image : kermt:latest@sha256:…
Repo : 2fe00f9 (clean)
Started : 2026-05-17T10:23:14Z (1h 23m ago)
Workflow : continue-pretrain, pretrain_mode=hybrid, world_size=2
Latest log (last 50 lines from $LOG):
[Epoch 12/100] step 4523/9000 loss 0.832 lr 1.2e-4
[val] step 4100 val_loss 0.821 (new best)
...
Progress: epoch 12/100, ~12% done. ETA ~6h.
TensorBoard: tensorboard --logdir $RUN_DIR/logs/tb
Replay command: $(jq -r .cmd_replay $RUN_DIR/run.json)
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