Official agent skill

Jetson Memory Audit

Measure Jetson DRAM/NvMap usage and verify before/after memory reclamation with live audit data.

OfficialApache-2.0Auto-check: notesAI & LLM Engineering

Install Jetson Memory Audit

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-memory-audit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-memory-audit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/jetson-memory-audit .claude/skills/jetson-memory-audit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "jetson-memory-audit" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-memory-audit into .claude/skills/jetson-memory-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-memory-audit", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/jetson-memory-audit

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-memory-audit -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-memory-audit --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/jetson-memory-audit .agents/skills/jetson-memory-audit && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "jetson-memory-audit" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-memory-audit into .agents/skills/jetson-memory-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-memory-audit", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-memory-audit -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-memory-audit --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/jetson-memory-audit .cursor/skills/jetson-memory-audit && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "jetson-memory-audit" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-memory-audit into .cursor/skills/jetson-memory-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-memory-audit", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/jetson-memory-audit

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-memory-audit -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-memory-audit --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/jetson-memory-audit .gemini/skills/jetson-memory-audit && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "jetson-memory-audit" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-memory-audit into .gemini/skills/jetson-memory-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-memory-audit", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-memory-audit

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-memory-audit -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/jetson-memory-audit .github/skills/jetson-memory-audit && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "jetson-memory-audit" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-memory-audit into .github/skills/jetson-memory-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-memory-audit", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill jetson-memory-audit -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills jetson-memory-audit --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/jetson-memory-audit .opencode/skills/jetson-memory-audit && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "jetson-memory-audit" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/jetson-memory-audit into .opencode/skills/jetson-memory-audit/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "jetson-memory-audit", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: jetson-memory-audit
GitHub stars: 3.5k
Used in: 1 other repo
Token cost: ~2.3k tokens
SKILL.md length: 1,144 words
Files: 8 (incl. scripts, references)
Skills in repo: 380
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Measure Jetson DRAM/NvMap usage and verify before/after memory reclamation with live audit data.

Works in 4 steps: Before the change, run scripts/audit.sh… → Make the user-approved change (stop the… → On JetPack below 7.2 / L4T below r39.0,… → …
Tasks that involve GPU and accelerator computing
SKILL.md covers Purpose, CRITICAL: Memory appears stuck…, When to use and Prerequisites, plus 8 more sections
Runs Shell scripts from its folder

What it does

Jetson Memory Audit is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Measure Jetson DRAM/NvMap usage and verify before/after memory reclamation with live audit data.

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `BENCHMARK.md`, `evals/evals.json` and `references/DESIGN.md`).

It sits in AI & LLM Engineering, covering GPU and accelerator computing. It works with NVIDIA AI Platform, CUDA, SGLang and vLLM. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

Tasks that involve GPU and accelerator computing

Example prompts

“/jetson-memory-audit”

Requirements

A Bash shell

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

Before the change, run scripts/audit.sh and save the JSON baseline.
Make the user-approved change (stop the container, switch mode, apply a tuning recommendation, etc.).
On JetPack below 7.2 / L4T below r39.0, or when the same stuck-memory symptom is observed on a newer release, flush reclaimable page cache…
Re-run scripts/audit.sh and compare memory_kb.available before vs after — that delta is the real reclamation.

What it can do on your machine

Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 2 files in scripts/ (Shell), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Jetson Memory Audit loads about 2.3k tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 29 tokens; SKILL.md has 1,144 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~29
When it runs · the whole SKILL.md, loaded when a task matches: ~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~2.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NoteRuns commands with sudoSKILL.md:31

sudo sync && sudo sysctl -w vm.drop_caches=3

NoteRuns commands with sudoSKILL.md:33

a container. The important operation is `sudo sysctl -w vm.drop_caches=3`; keep `sudo sync` immediately before it so dir

NoteRuns commands with sudoSKILL.md:54

aches.sh` requires root or passwordless `sudo -n`; run it only after the user explicitly authorizes cache dropping.

NoteRuns commands with sudoSKILL.md:75

`scripts/drop_caches.sh` (equivalent to `sudo sync && sudo sysctl -w vm.drop_caches=3` by default) and report its before

NoteRuns commands with sudoSKILL.md:94

sudo sync && sudo sysctl -w vm.drop_caches=3

NoteRuns commands with sudoSKILL.md:129

p_caches.sh` lacks root or passwordless `sudo -n`, report that cache dropping must be run on the host with sudo approval

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 1,144 words, ~2,276 tokens.

Download SKILL.mdSave it as .claude/skills/jetson-memory-audit/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.

name: jetson-memory-audit
description: Measure Jetson DRAM/NvMap usage and verify before/after memory reclamation with live audit data.
version: 0.0.1
license: Apache-2.0
metadata.author: Jetson Team
metadata.tags: jetson, memory, audit
metadata.languages: bash
metadata.data-classification: public

Jetson Memory Audit

Read-only memory-focused snapshot for a Jetson, plus the drop_caches verify-loop helper that confirms freed memory actually shows up as free instead of cached.

Purpose

Measure current Jetson memory consumers, capture before/after baselines, and verify whether user-approved changes actually reclaimed DRAM. Use live device data rather than estimates from container size, model size, or generic process memory.

CRITICAL: Memory appears stuck after stopping vLLM / sglang (JetPack below 7.2 / L4T below r39.0)

This is the most common memory confusion on Jetson releases before JetPack 7.2 or before L4T r39.0.

After you stop a vLLM, sglang, or Ollama server (or any CUDA workload), the memory shown as free by free -h or tegrastats may not recover — even though the process is gone. nvidia-smi may also show misleadingly low free GPU memory.

Root cause: The Thor RM (resource manager) holds freed sysmem pages in its own pool after a CUDA context exits. On Unified Memory Architecture (UMA) devices like Jetson, cudaMemGetInfo reads RM pool state and reports far less free memory than is actually available to a new process.

Workaround (for JetPack below 7.2 or L4T below r39.0):

bash

sudo sync && sudo sysctl -w vm.drop_caches=3

Run this on the host, not inside a container. The important operation is sudo sysctl -w vm.drop_caches=3; keep sudo sync immediately before it so dirty data is flushed before reclaimable page/dentry/inode caches are dropped. After running it, free -h and tegrastats will reflect the true available memory.

For affected releases, recommend this command when a user says:

"Memory didn't free after I stopped vLLM/sglang"
"Why does tegrastats still show high usage after my container exited?"
"OOM even though nothing is running"
"Memory was fine yesterday but now it's full"

On JetPack below 7.2 or L4T below r39.0, drop_caches is the reliable workaround when memory appears stuck after a CUDA workload exits; on newer releases, use it only if the same symptom is observed and the user approves.

When to use

"How much memory is in use on this Jetson? What's holding it?"
"I disabled the GUI / stopped vLLM / quit my container — did memory actually free?"
"Why does free -h still show low free memory after I stopped my workload?"
As the baseline before applying jetson-headless-mode or other memory-related changes, and again after to compute the actual delta.

Prerequisites

Run on the Jetson host, or in a sandbox/container with host-visible /proc, /etc/nv_tegra_release, tegrastats, and process data.
NvMap debugfs reads may require root. If unavailable, report that GPU memory attribution is limited rather than guessing.
drop_caches.sh requires root or passwordless sudo -n; run it only after the user explicitly authorizes cache dropping.

Available Scripts

Script	Purpose	Arguments
`scripts/audit.sh`	Emits a JSON snapshot from `jetson-diagnostic/scripts/snapshot.sh` for memory audit workflows.	No arguments.
`scripts/drop_caches.sh`	Flushes reclaimable page/dentry/inode caches and prints before/after memory deltas.	`--mode 1\|2\|3`, `--quiet`.

If your agent runtime supports run_script, use it to run scripts/audit.sh or scripts/drop_caches.sh and summarize the returned output. Otherwise run the scripts with bash from the repository root.

Instructions

For "how much memory is in use right now?" questions, run scripts/audit.sh and report only values from the JSON snapshot.

Reporting guidance

Do not only print or mention the path to a helper. Invoke the helper and then summarize the returned data.

For "how much memory is in use" prompts, run scripts/audit.sh and quote mem_total_gb, memory_kb.available, and the leading procrank_top process or nvmap.top_clients consumer.
For GUI/desktop memory prompts, run scripts/audit.sh and report default_systemd_target plus any display manager in candidate_services (gdm3, gdm, lightdm, sddm, or display-manager). Do not disable anything; hand off to jetson-headless-mode for a plan.
For prompts that explicitly authorize cache dropping after a stopped workload, run scripts/drop_caches.sh (equivalent to sudo sync && sudo sysctl -w vm.drop_caches=3 by default) and report its before/after free, available, and cached deltas. If root is unavailable, explain that it must be run on the host with sudo.

If your agent runtime does not execute helper scripts relative to this skill directory, resolve script paths with the AgentSkills {baseDir} placeholder:

bash

{baseDir}/scripts/audit.sh
{baseDir}/scripts/drop_caches.sh

Do not call jetson-memory-audit as a tool name unless the runtime explicitly registers skills as callable tools; Agent Skills are normally instructions plus files, not direct tool functions.

Sandbox note for agents: seeing this skill file does not guarantee access to Jetson host memory data. If /proc/device-tree/model, /etc/nv_tegra_release, tegrastats, /sys/kernel/debug/nvmap, or host process data are missing inside a NemoClaw/OpenClaw sandbox, say the sandbox lacks Jetson host visibility and ask the user to run on the Jetson host or relaunch with a host-visible sandbox profile. Do not fabricate memory totals, available memory, PSS, NvMap, or reclamation deltas.

Show full SKILL.md (403 more words)Show less

For "how much memory did this change free?" questions, use a before/after delta. Do not estimate freed memory from container size, image size, RSS, or a single post-change snapshot.

Before the change, run scripts/audit.sh and save the JSON baseline.
Make the user-approved change (stop the container, switch mode, apply a tuning recommendation, etc.).
On JetPack below 7.2 / L4T below r39.0, or when the same stuck-memory symptom is observed on a newer release, flush reclaimable page cache on the host (not inside a container) so freed pages show up as free instead of cached:
bash
```
sudo sync && sudo sysctl -w vm.drop_caches=3
```
Re-run scripts/audit.sh and compare memory_kb.available before vs after — that delta is the real reclamation.

If the user already made the change and no baseline exists, say that the exact freed amount cannot be recovered from the current snapshot alone. Capture a new baseline now so the next change can be measured.

Use live audit data as the source of truth. Memory totals, available memory, NvMap totals, PSS values, display-manager state, and savings deltas must come from scripts/audit.sh, free -h, or tegrastats on the actual device. If a number is not present in those outputs, do not guess it.

Output contract for `audit.sh`

json

{
  "sku": "orin-nano",
  "variant": "orin-nano-8gb",
  "mem_total_gb": 8,
  "l4t_version": "36.4.0",
  "product_model": "nvidia jetson orin nano developer kit",
  "memory_kb": { "total": 8123456, "available": 4123456, "free": 1023456, "cached": 1234567, "swap_total": 0, "swap_free": 0 },
  "default_systemd_target": "graphical.target",
  "candidate_services": { "gdm3": { "active": "active", "enabled": "enabled" } },
  "tegrastats_sample": "RAM 4011/8138MB (lfb 8x4MB) ...",
  "nvmap": { "readable": false, "total_kb": 0, "top_clients": [] },
  "procrank_top": [ { "pid": 4321, "pss_kb": 4000000, "cmd": "vllm" } ]
}

Limitations

Exact freed-memory deltas require a before snapshot, the user-approved change, cache flush when appropriate, and an after snapshot.
NvMap attribution depends on host-visible debugfs access; if it is unavailable, report limited GPU memory attribution instead of guessing.
Sandbox/container runs may not see host /proc, tegrastats, systemd, or NvMap data unless the runtime exposes them.

Error handling

If scripts/audit.sh cannot access host Jetson data, report the missing visibility and ask to rerun on the Jetson host or in a host-visible sandbox.
If scripts/drop_caches.sh lacks root or passwordless sudo -n, report that cache dropping must be run on the host with sudo approval.
If no before snapshot exists, say the exact reclaimed amount cannot be recovered from the current state alone and capture a new baseline for the next change.

Safety

Read-only. drop_caches is non-destructive (kernel only releases pages it could reclaim under pressure anyway; sync runs first to preserve dirty data).

Hand off to

jetson-headless-mode — biggest single user-space win on systems still booting graphical.target.
jetson-inference-mem-tune — when a model server is the top NvMap / PSS consumer.
If runtime changes cannot hit the target, report that further reclamation is outside this skill's scope rather than suggesting unsafe boot-time edits.

Files

SKILL.md and 7 other files (scripts, references) in skills/jetson-memory-audit of NVIDIA/skills.

SKILL.md
BENCHMARK.md
evals/evals.json
references/DESIGN.md
scripts/audit.sh
scripts/drop_caches.sh
skill-card.md
skill.oms.sig

Open the folder on GitHubat commit 0e0d506

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in NVIDIA/skills, which our catalogue first saw on October 7, 2026.

NVIDIA-AI-IOT/jetson-device-skillsskills/jetson-memory-audit

Compare with similar skills

Jetson Memory Audit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Jetson Memory Audit compared with similar skills
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Magpie Kernel Evaluatoramd/skills	395	—	~2.3k	Automated safety check: Pass	MIT	today
Dstack Prototypingdstackai/dstack	2.3k	—	~1.6k	Automated safety check: Pass	MPL-2.0	today
Add Jit Kernelguqiong96/Lsglang	143	1 repos	~10k	Automated safety check: Pass	Apache-2.0	3 days ago

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Works with

Questions about Jetson Memory Audit

What does Jetson Memory Audit do?

Measure Jetson DRAM/NvMap usage and verify before/after memory reclamation with live audit data. Jetson Memory Audit is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Measure Jetson DRAM/NvMap usage and verify before/after memory reclamation with live audit data.

When should I use Jetson Memory Audit?

Jetson Memory Audit fits situations like: tasks that involve GPU and accelerator computing.

How do I install Jetson Memory Audit in Claude Code?

Run `npx skills add NVIDIA/skills --skill jetson-memory-audit -a claude-code`. Or copy the skill folder (skills/jetson-memory-audit in NVIDIA/skills) into .claude/skills/jetson-memory-audit in your project. Claude Code loads it when a task matches its description.

How do I install Jetson Memory Audit in Codex?

Run `npx skills add NVIDIA/skills --skill jetson-memory-audit -a codex`. Or copy the skill folder (skills/jetson-memory-audit in NVIDIA/skills) into .agents/skills/jetson-memory-audit in your project. Codex loads it when a task matches its description.

Can I use Jetson Memory Audit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill jetson-memory-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jetson-memory-audit, .gemini/skills/jetson-memory-audit, .github/skills/jetson-memory-audit and .opencode/skills/jetson-memory-audit in your project.

What does Jetson Memory Audit need to run?

Going by SKILL.md and its folder, Jetson Memory Audit needs a shell for the scripts in its folder. Our summary lists: A Bash shell.

Does Jetson Memory Audit access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Jetson Memory Audit safe to install?

Our automated static check of SKILL.md found notes only (runs commands with sudo), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Jetson Memory Audit use?

Jetson Memory Audit is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Jetson Memory Audit use?

About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 657 tokens, read only when the agent opens those files.

What are the alternatives to Jetson Memory Audit?

Skills that share tags, products or a category with Jetson Memory Audit: Graphsignal (graphsignal/graphsignal, 257 stars), LLM Torch Profiler Trace Analysis (BBuf/AI-Infra-Auto-Driven-SKILLS, 900 stars), Magpie Kernel Evaluator (amd/skills, 395 stars) and Dstack Prototyping (dstackai/dstack, 2.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jetson Memory Audit?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Jetson Memory Audit

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Jetson Memory Audit

Purpose

CRITICAL: Memory appears stuck after stopping vLLM / sglang (JetPack below 7.2 / L4T below r39.0)

When to use

Prerequisites

Available Scripts

Instructions

Reporting guidance

Output contract for audit.sh

Limitations

Error handling

Safety

Hand off to

Files

Used in 1 other repository

Compare with similar skills

Similar skills

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Related

Questions about Jetson Memory Audit

Output contract for `audit.sh`