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Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.
Install
Run this in your project folder. It works with Claude Code, Cursor, Codex and dozens of other agents.
$ npx skills add wshobson/agents --skill spark-memory-thermal-opsNeeds Node.js. Add -g to install for every project, or -a claude-code to skip the agent picker.
How to use Spark Memory Thermal Ops
Once it's installed, Claude reads this skill on its own whenever a request matches what it's for, so you don't have to mention it by name. In Claude Code you can also run it directly by typing /spark-memory-thermal-ops.
It includes scripts that Claude may run on your computer, so skim them on GitHub before you install.
What the instructions cover
- 1Common Issues Quick Reference
- 2When to Use This Skill
- 3UMA Memory Model
- 4The OOM Ladder
- 5Thermal Monitoring
- 6Concurrent Workloads
Section headings from the skill's SKILL.md file.
What's inside · 3 files
- SKILL.md
- assets/thermal-sample.sh
- references/uma-accounting.md
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