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-articulated-reasonergit 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-articulated-reasoner)<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/3dgs-articulated-reasoner"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/3dgs-articulated-reasoner/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-articulated-reasoner"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/3dgs-articulated-reasoner.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 | $0.00105 | $0.02540 |
| Opus 5 | $0.00053 | $0.01270 |
| Sonnet 5 | $0.00021 | $0.00508 |
| Haiku 4.5 | $0.00011 | $0.00254 |
Grade A, and why
3dgs-articulated-reasoner 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 12d 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 — 198 lines — stays where its author put it; the contents beside it link to each section on GitHub.
3DGS Articulated Reasoner
Reason about articulated objects within 3DGS scenes: extract part structure, infer kinematics, build interactive digital twins.
Capabilities
- Part Structure Extraction: Given a 3DGS scene containing articulated objects (furniture, vehicles, tools), identify part boundaries and segment Gaussians into movable/ fixed groups.
- Kinematic Inference: Estimate joint types (revolute, prismatic, fixed) and axes from multi-view observation or user specification. Output URDF-compatible kinematic tree.
- Part-Aware Compositing: Apply part-aware alpha-compositing that penalizes inter-part penetration at boundaries (inspired by Innovation I-01 in the knowledge base).
- Digital Twin Generation: Produce an interactable digital twin where Agent commands (open, rotate, slide) drive real-time Gaussian deformation.
- Simulation-Ready Export: Export articulated 3DGS scenes to simulation frameworks (MuJoCo, Isaac Sim) with preserved visual fidelity.
Instructions
Step 1: Scene Analysis
When the user provides a 3DGS scene or asks about articulated objects:
- Check
references/3dgs-methods-overview.mdfor relevant methods (ArtiTwinSplat, ArtiSplat, ULF-Loc). - Identify the object category and expected articulation pattern from the knowledge base.
- Determine whether part segmentation is available or needs estimation.
Key references:
- ArtiTwinSplat (arXiv 2026): Articulated digital twin from 3DGS
- ULF-Loc (CVPR 2026): Exposed feature bias at part boundaries
- ArtiSplat: Differentiable physics + rendering for articulated objects
Step 2: Part Segmentation
If the user needs part segmentation:
Input: 3DGS point cloud / rendered views
Method options:
a) Semantic segmentation (LangSplat/Feature 3DGS) → part labels
b) Motion-based segmentation (multi-frame observation) → movable vs fixed
c) User-specified masks (manual annotation on rendered views)
Output: Per-Gaussian part label {0, 1, ..., K}
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.
- 12d ago First seen · 198 lines · 105 tokens per session scan A 67a6b36f08dd
3dgs-articulated-reasoner is a skill published in the GitHub repository jaccen/Awesome-Gaussian-Skills (151 stars, last pushed 7d ago), licensed Apache-2.0. It adds 105 tokens to every session and 2,540 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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