dgx-spark-playbooks/nvidia/station-ai-skills

DGX Station AI Skills for Coding Agents

Give your coding agent (Claude Code, Codex, Gemini CLI, Cursor) DGX Station expertise via an AGENTS.md and on-demand Agent Skills

Table of Contents

• Overview
  • AGENTS.md vs Agent Skill — why split?
• Instructions
  • Project-specific
• Troubleshooting

---

Overview

Basic idea

Modern coding agents — Claude Code, OpenAI Codex CLI, Gemini CLI, Cursor — all support two extension mechanisms: a project-level context file that's loaded into every conversation, and on-demand procedural workflows (called skills, prompts, commands, or rules depending on the harness). This playbook ships both for DGX Station:

• An AGENTS.md with the critical DGX Station constraints your agent should always know (mixed coherency, GPU targeting, common pitfalls). AGENTS.md is the cross-harness standard; an install.sh lays it down as CLAUDE.md, GEMINI.md, or AGENTS.md depending on the agent you use.
• Four Agent Skills — vllm-setup, sglang-setup, mig-configure, dgx-diagnose — authored once in the Anthropic Agent Skills format and installed into the right per-harness location (.claude/skills/, .codex/prompts/, .gemini/commands/, or .cursor/rules/).

This approach keeps your agent's context lean in every conversation while giving it deep procedural knowledge on demand, regardless of which agent you use.

AGENTS.md vs Agent Skill — why split?

	AGENTS.md	Agent Skill
Loaded	Every conversation, automatically	Only when invoked by name (or matched by description, in Claude)
Best for	Constraints, pitfalls, "never do X" rules	Step-by-step workflows, deployment procedures
Context cost	Consumed every time	Zero until invoked

The DGX Station mixed-coherency constraint (--gpus all will crash) should be in every conversation. The full vLLM deployment procedure should not.

What you'll accomplish

• Install the AGENTS.md and four Agent Skills into your project directory for your chosen agent (Claude Code, Codex, Gemini CLI, or Cursor).
• Verify the agent loads the constraints automatically and the skills on demand.
• Invoke vllm-setup to deploy a vLLM inference server with validated configuration.
• Invoke sglang-setup to deploy an SGLang inference server.
• Invoke mig-configure to partition the GB300 into MIG instances.
• Invoke dgx-diagnose to troubleshoot common DGX Station issues.

What to know before starting

• Basic familiarity with one supported coding agent (running it, giving it prompts, using slash commands or rule references)
• General understanding of DGX Station (two GPUs, Docker-based workflows)

Prerequisites

• NVIDIA DGX Station with GB300
• One of the supported coding agents installed:
  • Claude Code: curl -fsSL https://claude.ai/install.sh | sh
  • OpenAI Codex CLI: npm i -g @openai/codex
  • Gemini CLI: npm i -g @google/gemini-cli
  • Cursor: download from https://cursor.com/
• A project directory where you do DGX Station work

Ancillary files

• assets/AGENTS.md — canonical context file with critical constraints, GPU targeting, software versions, and common pitfalls. Cross-harness standard.
• assets/skills/vllm-setup/SKILL.md — skill: deploy vLLM with validated configuration.
• assets/skills/sglang-setup/SKILL.md — skill: deploy SGLang with validated configuration.
• assets/skills/mig-configure/SKILL.md — skill: configure MIG partitions on the GB300.
• assets/skills/dgx-diagnose/SKILL.md — skill: troubleshoot common DGX Station issues.
• assets/install.sh — per-harness installer (claude, codex, gemini, cursor, or all).

Time & risk

• Duration: 10-15 minutes
• Risk level: Low — this playbook copies markdown files into your project directory
• Rollback: Delete the context file (AGENTS.md / CLAUDE.md / GEMINI.md) and the harness-specific skill directory (.claude/skills/, .codex/prompts/, .gemini/commands/, or .cursor/rules/) from your project directory
• Last Updated: 05/18/2026
  • Restructured as harness-agnostic Agent Skills (Claude Code, Codex, Gemini CLI, Cursor)

Instructions

Step 1. Install your coding agent

Pick whichever agent you prefer — the rest of this playbook works the same regardless. Install commands:

Agent	Install
Claude Code	curl -fsSL https://claude.ai/install.sh | sh
OpenAI Codex CLI	npm i -g @openai/codex
Gemini CLI	npm i -g @google/gemini-cli
Cursor	Download from https://cursor.com/

Verify with claude --version, codex --version, gemini --version, or by launching Cursor.

Step 2. Install the skills into your project

Navigate to the project where you want DGX Station expertise, then run the installer with the harness you use:

cd ~/your-project

## Pick one:
/path/to/this/playbook/assets/install.sh claude
/path/to/this/playbook/assets/install.sh codex
/path/to/this/playbook/assets/install.sh gemini
/path/to/this/playbook/assets/install.sh cursor

## Or install for all four at once:
/path/to/this/playbook/assets/install.sh all

If you downloaded the playbook as a zip, the path is relative to the extracted directory:

station-ai-skills/assets/install.sh claude ~/your-project

The installer is additive for skill directories (won't clobber existing skills you've written) and refuses to overwrite an existing context file (AGENTS.md, CLAUDE.md, GEMINI.md) unless you pass --force.

Resulting layout (per harness):

your-project/
  AGENTS.md   or  CLAUDE.md   or  GEMINI.md      # context file (named for your agent)
  .claude/skills/<name>/SKILL.md                  # claude
  .codex/prompts/<name>.md                        # codex
  .gemini/commands/<name>.md                      # gemini
  .cursor/rules/<name>.mdc                        # cursor

Where <name> is each of vllm-setup, sglang-setup, mig-configure, dgx-diagnose.

Note

Every supported agent automatically reads the context file from the working directory at startup. Skills/prompts/rules in the harness-specific directory are discovered automatically — no additional configuration needed.

Step 3. Verify the setup

Start your agent in the project directory and ask a question that requires constraint knowledge:

Can I use --gpus all to run my CUDA workload on DGX Station?

The agent should immediately warn about the mixed-coherency constraint and recommend --gpus '"device=N"' targeting. If you don't get the warning, the context file isn't being loaded — see Troubleshooting.

Then verify the skills are discoverable:

Agent	How to check
Claude Code	Type / — vllm-setup, sglang-setup, mig-configure, dgx-diagnose should appear in the autocomplete
Codex CLI	Type /prompts: — same four names appear
Gemini CLI	Type / — same four names appear
Cursor	Open the Rules panel — same four rules appear

Step 4. Use vllm-setup to deploy an inference server

Invoke the skill in your agent:

Agent	Invocation
Claude Code	/vllm-setup (slash command) or just describe the task ("deploy vllm with Qwen3-8B")
Codex CLI	/prompts:vllm-setup
Gemini CLI	/vllm-setup
Cursor	In chat: "use the vllm-setup rule to deploy a vllm server"

The agent will walk you through deploying a vLLM server with a validated container image, correct GPU targeting, and recommended parameters. It will check your GPU index, ask which model you want to serve, and generate the full docker run command.

Step 5. Use sglang-setup to deploy SGLang

Same invocation pattern, but for SGLang with the cu130 container, RadixAttention prefix caching, and structured JSON output support.

Step 6. Use mig-configure to partition the GB300

The agent will query your current MIG state, show available profiles, help you choose a layout for your workloads, and execute the partitioning commands.

Step 7. Use dgx-diagnose to troubleshoot issues

If you encounter problems, invoke dgx-diagnose. The agent will check GPU status, driver version, running processes, MIG state, and Fabric Manager to identify the issue.

Step 8. Customize

Both the AGENTS.md and the skills are plain markdown — extend them freely.

Add project-specific constraints to AGENTS.md (or your harness-specific context file):

### Project-specific

- Our production MIG layout is 3g.139gb + 2g.70gb + 2g.70gb
- Always use port 8080 for inference (nginx proxy on 443)
- Model weights are cached at /data/models, mount with -v /data/models:/root/.cache/huggingface/hub

Create new skills by adding a directory and SKILL.md to assets/skills/, then re-run install.sh:

mkdir -p assets/skills/run-benchmarks
cat > assets/skills/run-benchmarks/SKILL.md << 'EOF'
---
name: run-benchmarks
description: Run our standard inference benchmark suite against the running vLLM or SGLang server and compare against the baseline.
---

## Run benchmarks

1. Check which inference server is running (vLLM on port 8000 or SGLang on port 30000)
2. Run the appropriate benchmark script from ./benchmarks/
3. Report throughput (tokens/sec), latency (TTFT, ITL), and memory utilization
4. Compare against the baseline in ./benchmarks/baseline.json
EOF

Tip

Keep AGENTS.md focused on constraints and pitfalls (things that break). Put procedural workflows in skills (things you do step-by-step).

Troubleshooting

Skills don't appear in autocomplete / aren't discoverable

Each agent discovers skills from a harness-specific directory in the current directory (or a parent). Check the right one:

Agent	Expected location
Claude Code	.claude/skills/<name>/SKILL.md
Codex CLI	.codex/prompts/<name>.md
Gemini CLI	.gemini/commands/<name>.md
Cursor	.cursor/rules/<name>.mdc

## Examples — check the directory for your agent
ls -la .claude/skills/
ls -la .codex/prompts/
ls -la .gemini/commands/
ls -la .cursor/rules/

You should see entries for vllm-setup, sglang-setup, mig-configure, and dgx-diagnose.

Check you're in the right directory:

pwd

The agent must be started from the directory containing the harness directory, or a subdirectory of it.

Context file not loaded

If the agent gives generic answers without DGX Station awareness, the context file isn't being picked up. Each agent reads a different filename — verify the one for your agent exists:

Agent	Expected filename
Claude Code	CLAUDE.md (also reads AGENTS.md as fallback)
Codex CLI	AGENTS.md
Gemini CLI	GEMINI.md
Cursor	AGENTS.md

## Verify the file exists for your agent
cat AGENTS.md | head -5
cat CLAUDE.md | head -5
cat GEMINI.md | head -5

## Restart the agent in the correct directory
cd ~/your-project
claude    # or codex, gemini, etc.

All four agents read the context file from the working directory (and parent directories up to the project root).

Skill gives outdated information

The skills contain validated container versions and parameters as of the publication date. If a newer container is available, edit the canonical source and re-install:

nano /path/to/playbook/assets/skills/vllm-setup/SKILL.md
/path/to/playbook/assets/install.sh all --force

Or edit the installed copy directly:

## Claude Code
nano .claude/skills/vllm-setup/SKILL.md
## Codex
nano .codex/prompts/vllm-setup.md
## Gemini CLI
nano .gemini/commands/vllm-setup.md
## Cursor
nano .cursor/rules/vllm-setup.mdc

Tip

Skills are plain markdown — you can version them in git alongside your project code.

"Both GPUs cannot be used" errors

This is the mixed-coherency constraint working as intended. If you see CUDA errors when using --gpus all:

## Find the GB300 index
nvidia-smi --query-gpu=index,name --format=csv,noheader

## Use device-specific targeting
docker run --gpus '"device=1"' ...

The AGENTS.md covers this constraint, but if you removed that section, add it back — it's the most important piece of DGX Station knowledge.

Skills conflict with existing project directory

If your project already has a .claude/, .codex/, .gemini/, or .cursor/ directory with its own contents, install.sh is additive for skill directories — it adds the new skill files alongside whatever you already have and warns on collision rather than overwriting.

For context files (AGENTS.md, CLAUDE.md, GEMINI.md), the installer refuses to overwrite an existing file. Pass --force to override, or merge the new content manually:

## See what would be written
diff /path/to/playbook/assets/AGENTS.md ./AGENTS.md

## Force overwrite
/path/to/playbook/assets/install.sh claude . --force

Installer reports "WROTE" for some files but "SKIP" for others

That's the safe-by-default behavior. The installer skips any file that already exists, prints a warning, and continues with the rest. To get a clean install, either:

1. Delete the existing files first: rm -rf .claude/skills/{vllm-setup,sglang-setup,mig-configure,dgx-diagnose}
2. Or pass --force (only affects context files; skill files are still skipped if present)