An image-processing tool for improving rendered views from 3D reconstructions such as NeRF, 3D Gaussian Splatting, or NuRec. It can also evaluate or fine-tune the DiffusionHarmonizer model, which adjusts image quality across frames.

Use it after rendering reconstruction frames to improve realism, reduce artifacts, harmonize inserted moving objects, evaluate results, or fine-tune the model.

Neural 3D reconstructions can contain visual defects or inserted objects that do not match the surrounding scene. This tool addresses those post-processing problems.

nurec-fixer — Skill for Claude Code and Codex by NVIDIA

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 · 37 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add NVIDIA/nurec-skills --skill nurec-fixer

Clone the repo

git clone --depth 1 https://github.com/NVIDIA/nurec-skills

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/nvidia/nurec-skills/nurec-fixer/github.svg)](https://agentmods.dev/skills/nvidia/nurec-skills/nurec-fixer)

Your own site

<a href="https://agentmods.dev/skills/nvidia/nurec-skills/nurec-fixer"><img src="https://agentmods.dev/badge/skills/nvidia/nurec-skills/nurec-fixer/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/nvidia/nurec-skills/nurec-fixer"><img src="https://agentmods.dev/badge/skills/nvidia/nurec-skills/nurec-fixer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 87 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,445 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 322
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.

Model	Per session	Once invoked
Fable 5.1	$0.00087	$0.04445
Opus 5	$0.00044	$0.02223
Sonnet 5	$0.00017	$0.00889
Haiku 4.5	$0.00009	$0.00445

Measured 10d ago against content hash 2abf11f8ffcf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

nurec-fixer 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 10d 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/nurec-fixer/SKILL.md · 393 lines

How it starts

The opening of the file, as written. The whole thing — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.

NVIDIA DiffusionHarmonizer (NuRec post-processing)

Purpose

Run NVIDIA DiffusionHarmonizer on rendered images from neural reconstructions. DiffusionHarmonizer is a single-step, temporally-aware image diffusion enhancer for NeRF / 3DGS / NuRec-style renderings. It improves realism, reduces reconstruction artifacts, and harmonizes inserted dynamic objects with the surrounding scene.

When to Use / When NOT to Use

Use this skill when the user has rendered frames from NRE, NuRec, 3DGS, NeRF, or a similar reconstruction pipeline and wants to enhance, harmonize, evaluate, or optionally fine-tune the DiffusionHarmonizer model.

Do NOT use this skill when:

The user wants to train or render the 3D reconstruction itself (use nre).
The user wants to convert raw sensor data to NCore V4 (use ncore).
The user wants a generic photo enhancer. DiffusionHarmonizer is tuned for neural-reconstruction artifacts and object-insertion failures.
The user only wants NRE inline rendering with --enable-difix. That remains an NRE runtime feature; use the nre skill for the complete serve-grpc / render-grpc command shape.

What changed from the older Fixer skill

This skill follows the public NVIDIA/harmonizer release, not the older NGC JIT .pt artifact recipe. Use these public release artifacts:

Code: https://github.com/NVIDIA/harmonizer
Model: nvidia/Harmonizer on Hugging Face (the paper checkpoint models/diffusion_harmonizer.pkl), plus the base nvidia/Cosmos-Predict2-0.6B-Text2Image model that inference also requires.
Checkpoint download: ./download_checkpoints.sh from the repo root. It fetches the Harmonizer checkpoints into models/ (diffusion_harmonizer.pkl, harmonizer_nontemporal.pt) and the base Cosmos DiT + tokenizer into src/checkpoints/nvidia/Cosmos-Predict2-0.6B-Text2Image/.
Runtime: the harmonizer-cosmos-env image built from Dockerfile.cosmos (base nvcr.io/nvidia/pytorch:25.10-py3).
Inference entry: src/inference_pix2pix_turbo_harmonizer.py, run from inside /work/src so it can import its sibling modules.
Evaluation entry: src/evaluate_test_dataset.py
Training entry: src/train_pix2pix_turbo_harmonizer.py

Read the full file on GitHub · 393 lines

Files

What ships with it

13 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.

10d ago First seen · 393 lines · 87 tokens per session scan A 2abf11f8ffcf

Subscribe to this mod's changes

nurec-fixer is a skill published in the GitHub repository NVIDIA/nurec-skills (37 stars, last pushed 7d ago), licensed Apache-2.0. It adds 87 tokens to every session and 4,445 once invoked, about $0.0004 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.

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