DGX Station AI Skills for Coding Agents

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?

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)