Maintainer of this skill?
Automated security scan — not a certification.
by jaccen
1000+ arXiv-verified 3DGS methods · 15 AI agent skills · MCP server (34 tools) · open ebook — the AI-native entry to 3D Gaussian Splatting research
# Add to your Claude Code skills
git clone https://github.com/jaccen/Awesome-Gaussian-SkillsGuides for using ai agents skills like Awesome-Gaussian-Skills.
Last scanned: 6/13/2026
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}See how Awesome-Gaussian-Skills compares with popular alternatives.
Awesome-Gaussian-Skills is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by jaccen. 1000+ arXiv-verified 3DGS methods · 15 AI agent skills · MCP server (34 tools) · open ebook — the AI-native entry to 3D Gaussian Splatting research. It has 175 GitHub stars.
Yes. Awesome-Gaussian-Skills passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/jaccen/Awesome-Gaussian-Skills" and add it to your Claude Code skills directory (see the Installation section above).
Awesome-Gaussian-Skills is primarily written in TypeScript. It is open-source under jaccen on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh Awesome-Gaussian-Skills against similar tools.
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⚠️ Third-Party Software Notice
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Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
Automated security scan — not a certification.
See comparison
You shouldn't search 20 repos for 3DGS papers. This is the only one you need.
Other awesome lists give you paper titles. We give you paper titles + an AI toolkit that makes you faster. The canonical lists catalog the ecosystem for humans; this repo makes it consumable by AI agents — let your AI read the papers for you.
| What You Need | Other Lists | This Repo |
|---|---|---|
| Browse papers | Static markdown table | Interactive explorer: search, filter, sort |
| Compare methods | Open 2 papers side by side | 10+ dimension auto-comparison |
| Avoid code bugs | Discover after submission | 104 known bug pattern detection |
| Design experiments | Guess baselines & ablations | Venue-tailored experiment plan |
| NeRF → 3DGS | Trial-and-error porting | Step-by-step migration guide |
| CAD → 3DGS | No coverage | 40+ method conversion pipeline |
| Patent filing | Manual from scratch | Auto-generated claims & specs |
| Write a 3DGS paper | Trial-and-error writing | Full-lifecycle engine: idea → skeleton → draft → figures → experiments → polish → review → rebuttal |
Try the Interactive Method Explorer →
Search 1011 Methods instantly, filter by category, sort by citations, click any method card for details.
Each skill is a standalone SKILL.md file —copy it to your Agent's skills directory.
3 commands to your first AI-powered 3DGS workflow:
git clone https://github.com/jaccen/Awesome-Gaussian-Skills.git
# Option 1: Claude Code
cp -r Awesome-Gaussian-Skills/skills/* .claude/
# Option 2: Cursor
cp -r Awesome-Gaussian-Skills/skills/* .cursor/rules/
# Option 3: One-Click Install
curl -sSL https://raw.githubusercontent.com/jaccen/Awesome-Gaussian-Skills/main/scripts/setup.sh | bash
Then ask your Agent: "Compare 3DGS and 2DGS rendering formulations"
This repo ships as both AI agent skills and an MCP server, so it works across agent runtimes and MCP clients — the knowledge base is consumable by your AI agent, not just by you:
| Client | How to use |
|---|---|
| Claude Code / OpenClaw | /plugin marketplace add jaccen/Awesome-Gaussian-Skills → install gaussian-research, gaussian-engineering, or gaussian-domain (18 skills in 3 plugins); or load skills/ directly (see CLAUDE.md); MCP server via the config below |
| DSH (DeepSeek Harness) | dsh plugin --profile web add jaccen/Awesome-Gaussian-Skills — the 18 skills + MCP renderer auto-mount via cordis.patch.yml |
| Claude Desktop / Cursor / Cline | copy the MCP stdio config from .marketplace/mcp-servers/client-configs.md |
| Any MCP client | command: node, args: [/path/to/mcp-server/dist/index.js], transport: stdio — 34 tools (21 core + 13 experimental) |
The MCP server exposes 34 tools — 21 core (import_scene, set_camera, modify_gaussians, render_frame, sculpt_pipeline, SLAT latent editing, …) + 13 experimental stubs — see mcp-server/README.md and .marketplace/mcp-servers/mcp-metadata.json.
A full open-source technical book, built around 3D Gaussian Splatting as the spine and weaving together spatial intelligence and embodied intelligence into one closed loop: representation → perception → planning → action.
Core formula (echoing Agent = LLM + Context + Tools):
Embodied Agent = Spatial Representation × Perception × Planning × Action
| # | Chapter | Focus |
|---|---|---|
| 引言 | Why this book | Why 3DGS is the key puzzle piece of Physical AI |
| 前 | Landing prelude: should you, can you | Tech-selection matrix, eight cases where you should NOT use 3DGS, three reading paths |
| CH 01 | NeRF → 3DGS: A paradigm leap | Explicit vs implicit, the three innovations, the alpha-compositing formula |
| CH 02 | The math & engineering core | Anisotropic Gaussians, differentiable rasterization, adaptive density control, CUDA |
| 操 | Data-capture SOP | Device selection, trajectory design, seven exposure hard rules, scale calibration —the first step that decides your quality ceiling |
| CH 03 | From scene to world | Large-scale, dynamic/4D, GS-SLAM, compression & deployment |
| CH 04 | Semantic Gaussians | CLIP/DINO feature distillation, open-vocabulary 3D segmentation |
| CH 05 | Editing · Generation · Asset-ization | Feed-forward reconstruction, SDS generation, animatable assets, PBR relighting |
| CH 06 | Embodied intelligence basics | VLA lineage (RT/π0/GR00T/ReconVLA), simulation, Sim2Real |
| CH 07 | 3DGS as robot spatial memory | GS-SLAM, map-as-renderer, three tiers of spatial memory |
| CH 08 | Object-level & articulated understanding | Part-level Gaussians, URDF bridging, the CAD·Mesh·3DGS triangle |
| CH 09 | Agent-driven digital twins | MCP rendering pipeline, gesture interaction, the perception-action loop |
| CH 10 | World models & the future | Six schools of world models, 3DGS×World Model, spatial foundation models, Physical AI |
| CH 11 | Security, provenance & copyright | 3DGS watermarking, GaussTrace forensics, adversarial attacks, IP management |
| CH 12 | Capture-to-delivery: the end-to-end pipeline | Ten-stage tool selection & pitfalls, formats & coordinate systems, cost/duration estimates, project failure modes |
| CH 13 | Industry playbooks: six verticals | Museums & tourism, e-commerce, industrial inspection, AD simulation, embodied robots, surveying & mapping |
| 附 | Appendix | Datasets, evaluation metrics, delivery-metric conversion & acceptance-report templates |
| 实 | Practice index: from book to engineering | 18 engineering skills · 34 MCP tools · 1011+ method explorer, chapter-to-runnable-resource mapping |
| 后记 | Will 3DGS be eaten? | Why explicit representations will be compressed, not consumed |
Each chapter ends with hands-on exercises and links back to the repo's method tables, references/, and skills —so reading the book and doing the engineering are one seamless flow.
Highlights you won't find in a paper list:
GS-World, ManiGaussian, and OrbiSim turn 3DGS into a differentiable simulation engine.