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 | $0.04186 |
| Opus 5.5 | $0.00042 | $0.01674 |
| Sonnet 5.5 | $0.00021 | $0.00837 |
| Haiku 4.5 | $0.00011 | $0.00419 |
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 6d 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 — 369 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Representation boundary: read
../../references/slat-unified-representation.mdbefore claiming learned encoding or conversion capabilities. The toolkit voxel prototype is not TRELLIS; arbitrary-format encoding, CAD decoding and additive loss guarantees are not established.
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
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.
- 6d ago Changed · +12 lines 258481ad6707
- 8d ago Changed 2df76707c068
- 14d ago Changed 6551c9ea8117
- 1mo ago Changed 27356d59d727
- 1mo ago Changed 096c2d7631d6
- 1mo ago Changed 1befe2f11e17
- 1mo ago Changed e391ff68109d
- 1mo 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 (174 stars, last pushed today), licensed Apache-2.0. It adds 106 tokens to every session and 4,186 once invoked, about $0.0004 per session on Opus 5.5. 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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