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.
npx skills add jaccen/Awesome-Gaussian-Skills --skill 3dgs-compression-deploygit clone --depth 1 https://github.com/jaccen/Awesome-Gaussian-SkillsWrote 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/3dgs-compression-deploy)<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/3dgs-compression-deploy"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/3dgs-compression-deploy/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/3dgs-compression-deploy"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/3dgs-compression-deploy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00145 | $0.06155 |
| Opus 5.5 | $0.00058 | $0.02462 |
| Sonnet 5.5 | $0.00029 | $0.01231 |
| Haiku 4.5 | $0.00015 | $0.00615 |
Grade A, and why
3dgs-compression-deploy 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 16d 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 — 448 lines — stays where its author put it; the contents beside it link to each section on GitHub.
3DGS Compression & Deployment
End-to-end pipeline from raw 3DGS model to deployed application. Covers 6 compression categories + 4 deployment targets + hardware acceleration.
Capabilities
- Analyze 3DGS model attributes (position, SH, opacity, scale, rotation) and recommend compression strategy
- Select quantization method and bit-width per attribute (scalar, VQ, mixed-precision)
- Design pruning pipeline (coreset, adaptive, variational, merge-based)
- Plan VQ codebook architecture and residual coding
- Architect progressive streaming and LoD systems (static and 4D dynamic)
- Guide platform-specific deployment (WebGL, WebGPU, iOS/Android, desktop)
- Evaluate hardware acceleration paths (Tensor Core, GEMM, FPGA, ASIC)
- Estimate compression ratio, quality loss, and rendering speed for each method combination
Compression Pipeline
Raw 3DGS Model
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▼
[Step 1] Analysis ── attribute profiling, bottleneck identification
│
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[Step 2] Strategy Selection ── target platform → compression recipe
│
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[Step 3] Pruning ── reduce Gaussian count (coreset / adaptive / variational / merge)
│
▼
[Step 4] Quantization ── reduce per-attribute bit-width (scalar / VQ / mixed-precision)
│
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[Step 5] Vector Quantization ── codebook-based attribute compression (optional, replaces/augments Step 4)
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[Step 6] Streaming & LoD ── progressive loading structure for network delivery
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[Step 7] Deployment ── platform-specific renderer and runtime
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Deployed Application (Web / Mobile / Desktop / Edge)
Step 1: Analysis
Profile the 3DGS model before selecting compression methods:
| Attribute | FP32 Size | Typical Range | Sensitivity to Quantization |
|---|---|---|---|
| Position (μ) | 12B/Gaussian | Scene bounds | High — direct geometry impact |
| SH (degree 0–3) | 48B/Gaussian | [-1, 1] per coeff | Medium-High — visual quality driver |
| Opacity (α) | 4B/Gaussian | [0, 1] | Medium — pruning signal |
| Scale (s) | 12B/Gaussian | [1e-5, 1e2] | Medium — anisotropy sensitive |
| Rotation (q) | 16B/Gaussian | Unit quaternion | Low-Medium — can tolerate 8-bit |
What ships with it
1 file 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.
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.
- 16d ago Changed · +19 lines bb4b4e79495b
- 1mo ago Changed 7952bcf57af4
- 2mo ago First seen · 429 lines · 145 tokens per session scan A d9f02d3caa02
3dgs-compression-deploy is a skill published in the GitHub repository jaccen/Awesome-Gaussian-Skills (178 stars, last pushed yesterday), licensed Apache-2.0. It adds 145 tokens to every session and 6,155 once invoked, about $0.0006 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.
3dgs-compression-deploy compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| 3dgs-compression-deployjaccen/Awesome-Gaussian-Skills | Skill | 178 | yesterday | Apache-2.0 | A | 145 | Claude Code |
| img2threejsimg2threejs/img2threejs | Skill | 20k | yesterday | Apache-2.0 | A | 63 | Claude Code, Codex |
| earth2037-gameLeoYeAI/openclaw-master-skills | Skill | 2.2k | 6d ago | MIT | A | 76 | Claude Code, Codex |
| unity-pluginTomLeeLive/openclaw-unity-skill | Skill | 7 | 1mo ago | Apache-2.0 | A | 0 | Claude Code, Codex |
| spine-animationGenielabsOpenSource/spine-animation-ai | Skill | 643 | 2mo ago | none | A | 181 | Claude Code, Codex |
| unity-mcpDqz00116/skill-lib | Skill | 22 | 5mo ago | MIT | A | 25 | Claude Code, Codex |
| ue5-umgDqz00116/skill-lib | Skill | 22 | 5mo ago | MIT | A | 78 | Claude Code, Codex |