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 agentmods add skills/mxslr/mlcraft/domain-3dnpx skills add mxslr/mlcraft --skill domain-3dgit clone --depth 1 https://github.com/mxslr/mlcraftWrote 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/mxslr/mlcraft/domain-3d)<a href="https://agentmods.dev/skills/mxslr/mlcraft/domain-3d"><img src="https://agentmods.dev/badge/skills/mxslr/mlcraft/domain-3d.svg" alt="Measured on agentmods" 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 | $0.00117 | $0.00450 |
| Opus 5 | $0.00059 | $0.00225 |
| Sonnet 5 | $0.00023 | $0.00090 |
| Haiku 4.5 | $0.00012 | $0.00045 |
Grade A, and why
domain-3d 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 4d 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.
What it actually says
3D and Point Cloud - Method Selection
Choose a representation first: raw points, or sparse voxels for large scenes. Normalize and center the cloud.
Decision table
| Task | Recommended (recent, 2022-2025) | Notes |
|---|---|---|
| Point cloud classification | PointNeXt or Point Transformer v3; PointNet++ as a baseline | ModelNet-style. Metric is overall and mean-class accuracy. |
| Semantic segmentation (indoor or outdoor) | Point Transformer v3 (SOTA 2024) or sparse convolution (MinkowskiNet) | ScanNet, S3DIS. Metric is mIoU. |
| 3D object detection (driving) | sparse-voxel detectors (CenterPoint, VoxelNeXt) or transformer detectors | KITTI, nuScenes. Metric is mAP and NDS. |
| Efficiency-critical or very large clouds | Point Mamba (linear-time, 2024-2025) or sparse convolution | trade a little accuracy for speed and memory. |
Cross-cutting practice
- Augment with rotation, jitter, scaling, and random point dropout. Voxelize for large outdoor scenes.
- Leakage: split by scene or scan, never by cropped block within the same scene.
- Metrics: overall accuracy for classification, mIoU for segmentation, mAP and NDS for detection.
- Explainability: per-point saliency; highlight the points that drove the prediction.
- Improve accuracy: use
accuracy-improvement-loop; evaluate withrigorous-evaluation.
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.
- 4d ago First seen · 24 lines · 117 tokens per session scan A 3a24f6abd353
domain-3d is a skill published in the GitHub repository mxslr/mlcraft (8 stars, last pushed 1mo ago), licensed MIT. It adds 117 tokens to every session and 450 once invoked, about $0.0006 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 2026-08-31.
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