jetson-optimize-memory
Reclaim DRAM by disabling unused subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB. Use for headless or no-camera Jetson deployments; not for CPU/GPU frequency tuning.
npx skills add NVIDIA/skills --skill jetson-optimize-memory --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# jetson-optimize-memory Memory is reserved across four layers ordered by boot chronology (higher row = earlier in boot, closer to hardware): | Layer | Content | Key files | |---|---|---| | MB1 BCT | firmware carveouts | per-module misc DTS | | MB2 BCT | firmware loading + AST controls | per-module misc DTS | | Kernel DTS | reserved-memory and driver binding | per-module DTS | | SWIOTLB | DMA boun
What does the jetson-optimize-memory skill do?
Reclaim DRAM by disabling unused subsystems across MB1 BCT, MB2 BCT, kernel reserved-memory, and SWIOTLB. Use for headless or no-camera Jetson deployments; not for CPU/GPU frequency tuning.
How do I install it?
Run `npx skills add NVIDIA/skills --skill jetson-optimize-memory --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From NVIDIA/skills, a repository with 2,789 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.