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.
git clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-Skillsnpx agentmods add skills/jaccen/awesome-gaussian-skills/nerf-to-3dgs-migratorWrote 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.
[](https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/nerf-to-3dgs-migrator)<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/nerf-to-3dgs-migrator"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/nerf-to-3dgs-migrator/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.
<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/nerf-to-3dgs-migrator"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/nerf-to-3dgs-migrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00106 | $0.04151 |
| Opus 5 | $0.00053 | $0.02076 |
| Sonnet 5 | $0.00021 | $0.00830 |
| Haiku 4.5 | $0.00011 | $0.00415 |
Grade A, and why
nerf-to-3dgs-migrator 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 7d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NeRF-to-3DGS Migration Guide
You are a 3D reconstruction expert with deep knowledge of both NeRF and 3D Gaussian Splatting paradigms. Help users migrate their NeRF-based methods to 3DGS, or design new methods that combine insights from both.
Core Paradigm Differences
Before any migration, understand these fundamental differences:
| Aspect | NeRF | 3DGS |
|---|---|---|
| Representation | Continuous (MLP + volumetric) | Discrete (explicit Gaussians) |
| Rendering | Volume rendering (ray marching) | Splatting (α-compositing) |
| Sampling | Along rays (coarse-to-fine) | Point-based (all Gaussians) |
| Query | Point sampling + MLP forward | Direct attribute lookup |
| Density control | Implicit (MLP output) | Explicit (clone/split/prune) |
| Memory | Bounded (MLP params) | Unbounded (grows during training) |
| Speed | Slow (per-pixel ray march) | Fast (parallel rasterization) |
| Quality ceiling | High (continuous) | High (adaptive density) |
SLAT: Why NeRF→3DGS Migration Works
v1.6.0 upgrade: This skill's migration workflow is now grounded in the SLAT (Structured LATent representation) framework. See
../../references/slat-unified-representation.mdfor the full theory.
The SLAT Perspective on NeRF→3DGS
NeRF and 3DGS are not two unrelated representations — they are two decodings of the same structured latent. This is why migration is possible at all:
NeRF (continuous MLP field)
│
▼ ENCODE: sample density + color on voxel grid
┌──────────────────────┐
│ SLAT │
│ (sparse voxel │
│ latent) │
└──────┬───────────────┘
│
├── DECODE → 3D Gaussians (discrete, explicit)
└── DECODE → NeRF (continuous, implicit) ← original source
Under SLAT, NeRF→3DGS migration is a re-decode operation: encode the NeRF's continuous field into structured latent (by sampling on a voxel grid), then decode to discrete Gaussians. Each component migration step in this skill corresponds to a SLAT feature channel mapping:
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.
- 7d ago Changed e391ff68109d
- 12d ago First seen · 357 lines · 106 tokens per session scan A c544097f05e4
nerf-to-3dgs-migrator is a skill published in the GitHub repository jaccen/Awesome-Gaussian-Skills (151 stars, last pushed 7d ago), licensed Apache-2.0. It adds 106 tokens to every session and 4,151 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.
Other skills, from other repositories
usgs-lidar-dem
Download USGS 3DEP LiDAR DEM data, colorize with satellite imagery, and import into Metashape as laser scan ground truth. Use when extending photogrammetry environments with surrounding terrain.
sky-artifact-prevention
Prevent and remove sky/tunnel mesh artifacts in road corridor photogrammetry. Covers five strategies from Metashape-side prevention (height field, region crop, point cloud classification, source selection) to post-mesh cleanup. The.
metashape-reconstruction
Guide dense reconstruction, mesh building, and texturing in Metashape MCP. Covers depth maps, point cloud, mesh, texture, DEM, and orthomosaic generation with correct GPU/CPU settings and quality parameters. Works through the Metashape MCP server.
photo-import-setup
Set up a new Metashape project from scratch — import photos, load GPS reference, configure sensors (fisheye, rolling shutter, multi-camera), import EXR alpha masks, run image quality analysis, and disable bad frames. The first skill to use on any new capture. Works through the Metashape MCP server.
corridor-alignment-pipeline
Orchestrate incremental alignment of long road corridor captures with automatic drift detection and QA gates. Prevents alignment divergence by checking GPS deviation gradients and continuity between batches. Works through the Metashape MCP server.
metashape-api-lookup
Look up Metashape Python API parameters and enums from local reference files when building or fixing MCP tools. Use when you need to verify parameter names, types, defaults, or enum values for Metashape API calls.