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 AndyZhuang/Opentest --skill gaussian_splatting_scene_descriptiongit clone --depth 1 https://github.com/AndyZhuang/OpentestWrote 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/andyzhuang/opentest/gaussian_splatting_scene_description)<a href="https://agentmods.dev/skills/andyzhuang/opentest/gaussian_splatting_scene_description"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/gaussian_splatting_scene_description/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/andyzhuang/opentest/gaussian_splatting_scene_description"><img src="https://agentmods.dev/badge/skills/andyzhuang/opentest/gaussian_splatting_scene_description.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 | $0.00072 | $0.03472 |
| Opus 5 | $0.00036 | $0.01736 |
| Sonnet 5 | $0.00014 | $0.00694 |
| Haiku 4.5 | $0.00007 | $0.00347 |
Grade A, and why
gaussian_splatting_scene_description 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 8d 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gaussian Splatting Scene Description
Overview
gaussian_splatting_scene_description bridges 3D lab reconstruction and natural language understanding. Given a small set of lab photos or short video clips, it builds a 3D Gaussian Splatting (3DGS) scene representation and then generates a structured natural language description of the spatial layout — instrument positions, sample locations, bench topology, and relational predicates (e.g., "pipette is left of tube rack", "centrifuge is behind the operator"). The output is designed for downstream consumption by VLMs, spatial reasoning models, or LabOS skills (protocol_video_matching, detect_common_wetlab_errors, realtime_protocol_guidance_prompts) that need a persistent, queryable representation of the lab environment for context-aware guidance, error detection, or AR overlay anchoring.
When to Use This Skill
Use this skill when any of the following conditions are present:
- Spatial context for protocol guidance: A protocol step references "the tube on your left" or "the centrifuge behind you"; the agent needs a 3D-aware scene description to resolve spatial references and generate accurate
realtime_protocol_guidance_prompts. - Lab layout documentation: A new lab setup or bench configuration must be documented in natural language for onboarding, SOP writing, or remote collaboration — "The pipette is on the right side of the bench; the tube rack is centered; the centrifuge is 2 m behind."
- VLM / spatial model pre-training or fine-tuning: A VLM or spatial intelligence model requires structured scene descriptions as training data; the skill produces consistent, schema-aligned text from real lab imagery.
- AR overlay anchoring: AR overlays (e.g., step indicators, deviation highlights) need to be anchored to 3D positions; the scene description provides object labels and approximate coordinates for overlay placement.
- Multi-session consistency: The same lab is imaged at different times; 3DGS + description enables comparison ("centrifuge was moved from left to right since last scan").
- Error detection context:
detect_common_wetlab_errorsorprotocol_video_matchingbenefits from knowing the canonical layout — e.g., "tube A1 is in position (x, y) of the rack" — to disambiguate observations. - Robotic or automation planning: A lab robot (Opentrons, Hamilton) or future automation system needs a natural language map of the workspace for path planning or object localization.
- Virtual lab tours or training: Generate descriptive text for a 3D lab model used in VR training, virtual tours, or remote supervision.
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.
- 8d ago First seen · 222 lines · 72 tokens per session scan A e9bfd13fc148
gaussian_splatting_scene_description is a skill published in the GitHub repository AndyZhuang/Opentest (22 stars, last pushed 6mo ago), licensed MIT. It adds 72 tokens to every session and 3,472 once invoked, about $0.0004 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 2026-09-03.
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