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 ontologygit 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/ontology)<a href="https://agentmods.dev/skills/jaccen/awesome-gaussian-skills/ontology"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/ontology/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/ontology"><img src="https://agentmods.dev/badge/skills/jaccen/awesome-gaussian-skills/ontology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00121 | $0.01252 |
| Opus 5.5 | $0.00048 | $0.00501 |
| Sonnet 5.5 | $0.00024 | $0.00250 |
| Haiku 4.5 | $0.00012 | $0.00125 |
Grade A, and why
ontology 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 2d 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ontology & Knowledge Representation for 3D Domains
Scope: ontology engineering and structured knowledge representations applied to 3D scenes, robots, and spatial reasoning. Knowledge base: 12 arXiv-verified methods in the Ontology & Knowledge Representation category (
data/methods.json, single source of truth — always re-query, never answer from memory).
1. Representation Selection
| Need | Representation | Library exemplar |
|---|---|---|
| Structured scene memory that updates with change | Dynamic 3D Gaussian scene graph | DynamicGSG [arXiv:2502.15309] |
| Cross-scene unification of 3DGS maps | 3D scene graph registration | Graph-GSReg [arXiv:2606.29782] |
| Grounded, scalable long-horizon planning | Ontology-grounded scene representation | OntoPlan [arXiv:2610.07649] |
| End-to-end scene graph without pipelines | Direct 3D scene graph writing | GraphWrit3R [arXiv:2609.31595] |
| Open-plan environments (no wall heuristics) | Task/prior-driven probabilistic scene graphs | Prior-SG [arXiv:2608.06170] |
2. Reasoning over Structured Spatial Knowledge
| Pattern | Method | Mechanism |
|---|---|---|
| Neuro-symbolic spatial reasoning | SATURN [arXiv:2606.22694] | Approximate 3D scene reconstruction + soft perspective-aware predicates + training-free Pythonic symbolic executor |
| KG-augmented driving QA | KLDrive [arXiv:2603.21029] | Knowledge graph of fine-grained 3D scene facts reduces LLM hallucination |
| Scene-graph-guided CoT | EmbodiedVSR [arXiv:2503.11089] | Dynamic scene graphs steer chain-of-thought for zero-shot spatial tasks |
| Zero-shot 3D path planning | HiWE [arXiv:2609.39323] | Point-based interface: PointVLM grounding + depth-lifted semantic 3D representation + language planner |
| Symbolic safety assessment | NEUROSYMLAND [arXiv:2607.02277] | Probabilistic semantic scene graph + symbolic constraints (flatness, clearance), edge-deployable |
| Temporal-logic trajectory generation | Vision-TL-Action [arXiv:2607.26770] | Coordinate-free TL syntax graphs fused with visual tokens via cross-attention |
| Open-vocabulary manipulation via symbols | GRASP [arXiv:2606.12910] | VLM translates natural language → PDDL goals → real-time task-and-motion planning |
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.
- 2d ago First seen · 61 lines · 121 tokens per session scan A a485afab171e
ontology is a skill published in the GitHub repository jaccen/Awesome-Gaussian-Skills (162 stars, last pushed today), licensed Apache-2.0. It adds 121 tokens to every session and 1,252 once invoked, about $0.0005 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 2026-10-14.
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