Why This Project?

3D Gaussian Splatting (3DGS) is one of the most active research areas in computer vision and graphics, with 500+ papers published since 2023. Yet the AI Agent ecosystem has a glaring gap:

ClawHub has 13,000+ skills, but almost ZERO for 3D reconstruction / computer graphics.

Meanwhile, every 3DGS researcher faces the same repetitive tasks:

Awesome Gaussian Skills solves all of these — just describe what you need in natural language, and the AI Agent handles the rest.

Features

• 7 Research-Grade Skills: Paper reading, method comparison, code review, experiment planning, NeRF-to-3DGS migration, CAD/Mesh-3DGS bridge, and CG paper writing
• Zero Setup: Pure SKILL.md files — no Python packages, no dependencies, no installation. Just drop into your Agent's skill directory
• Cross-Platform Compatible: Works with OpenClaw, Claude Code, Cursor, Windsurf, and any Agent that supports the SKILL.md / CLAUDE.md format
• Domain Expert Knowledge: Built-in knowledge base covering 105+ 3DGS variants across 31 categories, with domain-specific terminology conventions
• Actively Maintained: Daily updates to track the latest arXiv papers and community developments

Quick Start

Option 1: OpenClaw

# Clone this repo
git clone https://github.com/jaccen/Awesome-Gaussian-Skills.git
cd Awesome-Gaussian-Skills

# Copy all skills to OpenClaw skills directory
cp -r skills/* ~/.openclaw/skills/

# Restart OpenClaw
openclaw restart

Option 2: Claude Code / Cursor

# Clone to your project
git clone https://github.com/jaccen/Awesome-Gaussian-Skills.git

# Copy the skills you need into your project's CLAUDE.md directory
cp -r skills/3dgs-paper-reader/SKILL.md .claude/
cp -r skills/3dgs-code-reviewer/SKILL.md .claude/

Option 3: One-Click Install Script

curl -sSL https://raw.githubusercontent.com/jaccen/Awesome-Gaussian-Skills/main/scripts/setup.sh | bash

Skills Overview

1. 3dgs-paper-reader — Paper Reading & Summarization

Read any 3DGS paper and extract structured insights in seconds.

You: "帮我读一下这篇论文 2401.01345，总结核心方法和实验结果"
Agent: [Extracts method name, core innovation, architecture, 
        training strategy, datasets, metrics, and key findings]

Capabilities:

• Parse arXiv papers (by ID or URL) and local PDFs
• Extract method architecture, loss functions, training details
• Generate structured summaries with tables
• Identify novelty and limitations

2. 3dgs-method-compare — Method Comparison Engine

Compare any 3DGS variants across 10+ dimensions.

You: "对比 ***和 **** 的核心差异"
Agent: [Generates comparison table across: primitive representation,
        opacity handling, color mechanism, frequency modeling,
        geometric boundary, training strategy, etc.]

Capabilities:

• Multi-method side-by-side comparison
• 10+ comparison dimensions (rendering formula, primitive, loss, speed, ...)
• Identify design trade-offs
• Built-in knowledge of 105+ methods

3. 3dgs-code-reviewer — Implementation Code Review

Catch common 3DGS implementation bugs before submission.

You: "审查我的 3DGS CUDA 渲染 kernel，看看有没有性能问题"
Agent: [Checks: alpha compositing order, tile-based rendering,
        CUDA memory coalescing, gradient computation, etc.]

Capabilities:

• Review CUDA kernels for correctness and performance
• Check rendering pipeline (differentiable rasterization, alpha blending)
• Validate loss function implementations
• Common bug pattern detection (42+ known patterns)

4. 3dgs-experiment-planner — Experiment Design Assistant

Design rigorous experiments that satisfy top-venue reviewers.

You: "我要写一篇关于高频边界建模的3DGS论文，帮我设计实验"
Agent: [Recommends datasets, baselines, metrics, ablation matrix,
        rendering comparisons, and efficiency analysis plan]

Capabilities:

• Dataset and baseline recommendation (tailored to your method)
• Ablation study matrix design
• Metric selection guidance (PSNR/SSIM/LPIPS/FID/CHF/etc.)
• Efficiency analysis framework
• Visualization plan for paper figures

5. nerf-to-3dgs-migrator — NeRF-to-3DGS Migration Guide

Migrate your NeRF method to 3DGS with step-by-step guidance.

You: "我的 NeRF 方法用了 hash encoding + deformable field，
     怎么迁移到 3DGS？"
Agent: [Provides migration plan: feature encoding → per-Gaussian 
        attributes, deformation → position/rotation offsets, 
        with code templates]

Capabilities:

• Component-level migration analysis
• Code templates for common migration patterns
• Identify incompatibilities and workarounds
• Performance comparison estimation

6. cad-mesh-3dgs — CAD, Mesh & 3DGS Bridge

Navigate the mesh↔3DGS pipeline, CAD reverse engineering, and surface extraction.

You: "我训练了一个3DGS模型，怎么提取高质量的mesh？"
Agent: [Recommends SuGaR or 2DGS pipeline, provides TSDF extraction
        steps, Marching Cubes parameters, and quality evaluation code]

You: "如何把CAD模型（STEP格式）转换为3DGS表示？"
Agent: [Provides mesh→Gaussian conversion pipeline, covariance 
        initialization from mesh normals, and curvature-aware sampling]

Capabilities:

• Mesh→3DGS conversion (sampling, initialization, optimization)
• 3DGS→Mesh extraction (SuGaR, 2DGS, TSDF+Marching Cubes)
• CAD reverse engineering pipeline (mesh→B-rep via primitive fitting)
• Hybrid representation analysis (MaGS, UniMGS, 2DGS)
• Geometry quality evaluation (Chamfer Distance, F-Score, Normal Consistency)
• Debugging common mesh-Gaussian conversion issues

7. cg-paper-writing — CG Paper Writing Assistant

Write publication-ready papers for CVPR/ICCV/ECCV/SIGGRAPH/TVCG.

You: "帮我写一篇关于 3DGS的论文引言，要和 ****GS 做对比"
Agent: [Generates academic introduction with proper structure,
        domain terminology, and argumentation flow]

Capabilities:

• Venue-specific writing conventions (CVPR vs SIGGRAPH vs TVCG)
• Domain terminology database (3DGS, NeRF, rendering, geometry)
• De-AI-ification (remove AI writing patterns)
• Section-by-section writing (Abstract → Introduction → Related Work → Method → Experiments → Conclusion)
• Mathematical notation conventions

Architecture

Awesome-Gaussian-Skills/
├── skills/
│   ├── 3dgs-paper-reader/       # Paper reading & summarization
│   │   └── SKILL.md
│   ├── 3dgs-method-compare/     # Method comparison engine
│   │   └── SKILL.md
│   ├── 3dgs-code-reviewer/      # Code review for 3DGS implementations
│   │   └── SKILL.md
│   ├── 3dgs-experiment-planner/ # Experiment design assistant
│   │   └── SKILL.md
│   ├── nerf-to-3dgs-migrator/  # NeRF-to-3DGS migration guide
│   │   └── SKILL.md
│   ├── cad-mesh-3dgs/          # CAD, Mesh & 3DGS bridge
│   │   └── SKILL.md
│   └── cg-paper-writing/        # CG paper writing assistant
│       └── SKILL.md
├── references/
│   └── 3dgs-methods-overview.md # Built-in knowledge base (105+ methods)
├── scripts/
│   └── setup.sh                 # Quick install script
├── README.md
├── README_CN.md
├── CONTRIBUTING.md
└── LICENSE

Each skill follows the SKILL.md standard (YAML frontmatter + Markdown instructions), compatible with:

• OpenClaw (ClawHub ecosystem)
• Claude Code (.claude/ directory)
• Cursor (.cursor/rules/)
• Windsurf and other AI Agent frameworks

Covered Methods (Partial)

The full knowledge base covers 105+ methods across 31 categories with detailed technical analysis. See references/3dgs-methods-overview.md.

Roadmap

• v0.1 — Initial release with 6 core skills (Apr 2026)
• v0.1.1 — Add cad-mesh-3dgs skill for CAD/Mesh↔3DGS bridge (Apr 2026)
• v0.1.2 — Knowledge base expansion: 50→100+ methods, 31 categories, daily auto-update workflow (Apr 2026)
• v0.2 — Add 3dgs-visualizer skill (Web-based rendering comparison)
• v0.3 — Add 3dgs-benchmark-runner skill (automated benchmark execution)
• v1.0 — ClawHub official listing + CI/CD integration
• v1.1 — Multi-language support (Chinese, Japanese, Korean)
• v2.0 — Agent-to-Agent collaboration (multi-agent paper discussion)

Contributing

Contributions are welcome! Please read our Contributing Guide for details.

Ways to contribute:

• Add new skills for uncovered scenarios
• Expand the methods knowledge base
• Report bugs or suggest improvements
• Share your use cases and success stories

Citation

If you find this project helpful in your research, please consider citing:

@misc{awesome-gaussian-skills,
  author = {jaccen},
  title = {Awesome Gaussian Skills: AI Agent Skill Pack for 3D Gaussian Splatting Research},
  year = {2026},
  url = {https://github.com/jaccen/Awesome-Gaussian-Skills}
}

Acknowledgments

• 3D Gaussian Splatting — The foundational work
• OpenClaw — The AI Agent framework and Skills ecosystem
• awesome-3D-gaussian-splatting — The awesome list that inspired this project
• All 3DGS researchers whose papers form our knowledge base

License

This project is licensed under the MIT License — see the LICENSE file for details.