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asset-harvester

asset-harvester is a skill for Claude Code, Codex from NVIDIA/nurec-skills. It costs 93 tokens per session (3,522 once invoked), scanned A, original, Apache-2.0.

A tool for turning images or short multi-view vehicle clips into separate 3D models of objects, represented as 3D Gaussian Splats. It can also create metadata for placing those objects into NVIDIA Omniverse NuRec simulations.

In plain words
What is it for?
Use it to create per-object .ply 3D assets from NCore V4 driving-log clips or masked single images, and optionally prepare them for NuRec object insertion. It is not for reconstructing an entire scene or creating text-to-3D models.
Why use it?
It avoids building a complete 3D scene when you only need individual cars, people, or other tracked objects. It also provides a defined workflow for inputs such as object masks and autonomous-vehicle recordings.

Skill for Claude CodeCodex ✓ vendor

Written for no agent in particular: nothing here depends on one.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is bash scripts/run_ncore_parser.sh --component-store <clip.json>.

not rated 38repo +3 8d ago A scan SkillSpector: warn 93 tokens original Apache-2.0

Good fit Use it to create per-object .ply 3D assets from NCore V4 driving-log clips or masked single images, and optionally prepare them for NuRec object insertion. It is not for reconstructing an entire scene or creating text-to-3D models.

Compare 6 skills from other repositories ↓
View source ↗ NVIDIA/nurec-skills
About the project

NVIDIA NuRec Skills is a set of agent instructions for running neural reconstruction and rendering workflows in NVIDIA Omniverse NuRec. It targets autonomous-vehicle and robotics simulation, guiding agents across the public containers, repositories, and artifacts that make up the NuRec stack.

NVIDIA/nurec-skills · 38 stars · on GitHub

Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/NVIDIA/nurec-skills
agentmods
npx agentmods add skills/nvidia/nurec-skills/asset-harvester

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for asset-harvester

README.md
[![agentmods](https://agentmods.dev/badge/skills/nvidia/nurec-skills/asset-harvester/github.svg)](https://agentmods.dev/skills/nvidia/nurec-skills/asset-harvester)
Your own site
<a href="https://agentmods.dev/skills/nvidia/nurec-skills/asset-harvester"><img src="https://agentmods.dev/badge/skills/nvidia/nurec-skills/asset-harvester/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for asset-harvester

Your own site · 80×15
<a href="https://agentmods.dev/skills/nvidia/nurec-skills/asset-harvester"><img src="https://agentmods.dev/badge/skills/nvidia/nurec-skills/asset-harvester.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,522 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Privilege Escalation · line 114
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00093 $0.03522
Opus 5 $0.00046 $0.01761
Sonnet 5 $0.00019 $0.00704
Haiku 4.5 $0.00009 $0.00352

Measured 12d ago against content hash 0c6b386f0315, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

asset-harvester scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/validate_setup.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/asset-harvester/SKILL.md · 298 lines

How it starts

The opening of the file, as written. The whole thing — 298 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Asset Harvester

Purpose

Install and drive NVIDIA Asset Harvester to extract per-object 3D Gaussian Splat assets from sparse autonomous-vehicle (AV) object observations — either a multi-view crop pulled from an NCore V4 driving log or a single masked image. The output is a simulation-ready gaussians.ply plus optional metadata.yaml that NVIDIA Omniverse NuRec can ingest as an external asset. Apache-2.0 upstream code lives at https://github.com/NVIDIA/asset-harvester.

When to Use / When NOT to Use

Use this skill when:

  • The user has AV clips or masked single images and wants per-object 3D assets via the SparseViewDiT + TokenGS pipeline.
  • The user has NCore V4 driving-log clips and wants per-track 3D assets for simulation.
  • The user asks about SparseViewDiT, TokenGS, or wants to reproduce the Asset Harvester paper / HF Space demo locally.
  • The user wants .ply Gaussians + metadata.yaml suitable for NVIDIA Omniverse NuRec object insertion.

Do NOT use this skill when:

  • The user wants a full-scene reconstruction (use the nre skill).
  • The user has no per-object masks or AV-style object crops.
  • The user wants text-to-3D, indoor scans, or non-AV imagery — out of distribution.
  • The user wants to ingest raw sensor data into NCore V4 (use the ncore skill first).
  • The user wants to re-train SparseViewDiT or TokenGS — this skill is install + inference only.
  • The user just wants the no-install demo: point them at https://huggingface.co/spaces/nvidia/asset-harvester.

Background

Open-source (Apache-2.0) image-to-3D pipeline pairing SparseViewDiT (multiview diffusion, 16 consistent views) with TokenGS (feed-forward Gaussian lifting):

NCore V4 clip ──► NCore parsing ──► SparseViewDiT (16-view diffusion)
              ──► TokenGS lifting ──► gaussians.ply
              ──► (optional) metadata.yaml for NuRec object insertion

Single HF repo nvidia/asset-harvester ships four checkpoints: AH_object_seg_jit.pt (AV-object Mask2Former), AH_multiview_diffusion.safetensors (SparseViewDiT), AH_camera_estimator.safetensors (camera pose, used when calibration is absent), and AH_tokengs_lifting.safetensors (TokenGS).

Read the full file on GitHub · 298 lines

Files

What ships with it

10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 12d ago First seen · 298 lines · 93 tokens per session scan A 0c6b386f0315

Subscribe to this mod's changes

asset-harvester is a skill published in the GitHub repository NVIDIA/nurec-skills (38 stars, last pushed 8d ago), licensed Apache-2.0. It adds 93 tokens to every session and 3,522 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Creative and mediaGame development
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