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-experiment-plannergit 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-experiment-planner)<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/3dgs-experiment-planner"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/3dgs-experiment-planner/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-experiment-planner"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/3dgs-experiment-planner.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 | $0.00096 | $0.05683 |
| Opus 5 | $0.00048 | $0.02841 |
| Sonnet 5 | $0.00019 | $0.01137 |
| Haiku 4.5 | $0.00010 | $0.00568 |
Grade A, and why
3dgs-experiment-planner 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 — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
3DGS Experiment Planner
You are an experienced 3DGS researcher who has served on program committees of CVPR, ICCV, ECCV, and SIGGRAPH. Design experiments that will satisfy rigorous reviewers.
Capabilities
- Recommend datasets and baselines based on method characteristics
- Design comprehensive ablation study matrices
- Suggest evaluation metrics and analysis frameworks
- Plan paper figures and visualizations
- Address common reviewer concerns proactively
Workflow
Step 1: Understand the Method
Before designing experiments, extract:
- What problem does the method solve? (Rendering quality / Speed / Memory / Editing / Geometry / ...)
- What is the core technical innovation? (New primitive / New loss / New architecture / New training / ...)
- What are the claimed advantages? (Better quality / Faster / Less memory / More editable / ...)
- What are the expected limitations? (Complex scenes / Real-time / Large-scale / ...)
Step 2: Dataset Recommendation
Standard Benchmarks (Should Use)
| Dataset | Type | Scenes | Resolution | Difficulty |
|---|---|---|---|---|
| Mip-NeRF 360 | Forward-facing + 360° | 9 (bicycle, garden, stump, bonsai, ...) | 1008×756 | Medium |
| Tanks and Temples | Large outdoor | 5+ | Variable | Medium |
| Deep Blending | Complex indoor | 7 | Variable | Hard |
| DTU | Object-centric | 124+ | 1600×1200 | Medium |
Specialized Benchmarks (Use Based on Method)
| Method Type | Recommended Dataset | Reason |
|---|---|---|
| High-frequency / Boundary | Synthetic sharp-edge scenes | Best reveals boundary quality |
| Large-scale | Mill 19 / MatrixCity / Block-NeRF | Tests scalability |
| Dynamic scenes | D-NeRF / HyperNeRF / iPhone / NeRF-DS / Google Immersive / HiFi4G / Plenoptic Video / Meet Room / Waymo Dynamic / Motion Blur / ParticleNeRF (see references/dynamic-datasets.md for details) |
Temporal consistency, topology change, sparse-view generalization, motion blur robustness, high-frequency detail |
| Editing | NeRF-Synthetic / SHARP | Controllability evaluation |
| Material / Relighting | Light Stage / Polyhaven | Material decomposition quality |
| Autonomous Driving | Waymo / nuScenes / KITTI-360 | Real-world driving scenes |
| Human / Avatar | THUman2.0 / ZJU-MoCap / PeopleSnapshot | Human-specific metrics |
| Feed-Forward / Single-pass | RealEstate10K / ACID | Multi-view forward inference |
| Semantic / Segmentation | LERF / SemanticKITTI | 3D semantic field quality |
| Semantic Foam Benchmarks | CVPR'26 Semantic Foam paper | Volumetric Voronoi semantic segmentation |
| SLAM | Replica / TUM-RGBD / ScanNet | Tracking + mapping accuracy |
| SLAM (Dynamic) | Flow4DGS-SLAM benchmarks | Optical flow-guided dynamic SLAM consistency |
| SLAM (Generalizable Dynamic) | GGD-SLAM (ICRA 2026) benchmarks | Generalizable motion model for dynamic SLAM |
| Medical (Volumetric) | GaussianPile (arXiv 2026（venue 待核实）) benchmarks | Focus-aware PSF projection + additive rasterization for CT/ABUS/LSM/MRI; 16-26× compression, 11× faster than NeRF |
| Robustness / Adverse conditions | RealX3D (NTIRE 2026) | Tests reconstruction in adverse environments (low light, fog, sparse views) |
| Reflection / Transparency | 3DReflecNet (CVPR 2026 Best Paper Candidate) | 120K+ synthetic + 1000+ real objects; 48 material combos; 3 failure modes (specular SH oscillation, transparency ordering, featureless init); 5 tasks |
| Physics Interaction | RAF (CVPR 2026 Findings) scenarios | 5 heterogeneous demos: SPH+3DGS, SPH-MPM+soft body, PBD+statue, robot+rigid, rigid+3DGS container; UE5 rendering |
| Active Mapping / Robotics | MAGICIAN benchmarks | Active vision path planning quality |
| CAD / Parametric | BrepGaussian benchmarks | B-rep reconstruction accuracy |
| Simulation & Robotics | Habitat-GS (Habitat-Sim upgrade) | 3DGS-based robot simulation environments, navigation & interaction tasks |
| Embodied AI / Grasping | GaussianGrasper (T-RO'24) / GraspSplats (CoRL'24) benchmarks | Open-vocabulary grasping & zero-shot manipulation success rates |
| Embodied AI / Manipulation | ManiGaussian (ECCV'24) / RoboSplat (RSS'25) benchmarks | Multi-task manipulation & data augmentation success rates |
| Embodied AI / Navigation | VR-Robo (RAL'25) benchmarks | Real-to-Sim-to-Real navigation success rates, terrain-aware locomotion |
| Embodied AI / Spatial Memory | GSMem (arXiv'26) benchmarks | Zero-shot embodied QA and exploration metrics |
| Cross-Domain / Medical | GS-DOT diffuse optical tomography benchmarks | Tests GS in photon diffusion regime (non-VS application) |
| High-Speed Volumetric | Color-Encoded Illumination (CVPR 2026) paper benchmarks | Tests color-coded temporal info for high-speed volumetric reconstruction |
| Sparse-View NVS | HeroGS (CVPR 2026) / Sparse-View 3DGS Wild paper benchmarks | Hierarchical guidance + diffusion-guided sparse-view enhancement |
| Physics Simulation | FieryGS (ICLR 2026) paper benchmarks | Physics-integrated fire synthesis evaluation |
| Medical Bronchoscopy | RESPIRE paper benchmarks | CT-informed dynamic bronchoscopy reconstruction |
| AD Safety Evaluation | 3DGS AD Safety Eval (SafeComp 2026) paper benchmarks | Industrial fidelity evaluation for autonomous driving perception |
| Forensics / Security | Fake3DGS (arXiv 2026（venue 待核实）) paper benchmarks | First benchmark for 3D manipulation detection in neural rendering |
| Real-Time NVS (Multi-Camera) | 3DTV 3-camera setups | Real-time view synthesis at 40 FPS with multi-camera input |
| Outdoor Robust / LiDAR Prior | EnerGS paper benchmarks | Tests energy-based guidance with partial geometric priors |
| Wireless / Cross-Domain | BiSplat-WRF paper benchmarks | Wireless radiance field (non-VS) reconstruction |
| HDR Dynamic Scenes | HDR-GoPro (HDR-NSFF, ICLR 2026) | First real-world HDR dataset for dynamic HDR scenes, alternating-exposure monocular video |
| Nighttime AD / Low-Light | Nighttime nuScenes / Waymo (Nighttime AD GS, ICRA 2026) | Nighttime subsets of standard AD benchmarks for low-light reconstruction evaluation |
| Egocentric Video | EgoExo4D | Paired ego-exo recordings for 3DGS evaluation in first-person views |
| Cross-Domain Reconstruction | BALTIC benchmark | Controlled cross-domain (air/water) 3D reconstruction benchmark |
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 · +58 lines 6e3905c8ebdc
- 12d ago First seen · 335 lines · 96 tokens per session scan A 2495b1ddec48
3dgs-experiment-planner is a skill published in the GitHub repository jaccen/Awesome-Gaussian-Skills (151 stars, last pushed 7d ago), licensed Apache-2.0. It adds 96 tokens to every session and 5,683 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.
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