Development
Skill
/domain-3d
Use for 3D and point cloud tasks: point cloud classification, 3D semantic or instance segmentation, 3D object detection from LiDAR, and depth or mesh analysis, including autonomous-driving and indoor-scan settings. Picks the right 3D backbone, representation, and scene-level
Claude Code
$ npx -y skills add mxslr/mlcraft --skill domain-3d --agent claude-code
Codex
$ npx skills add mxslr/mlcraft --skill domain-3d -a codex -y
Cursor
$ npx skills add mxslr/mlcraft --skill domain-3d -a cursor -y
Gemini CLI
$ npx skills add mxslr/mlcraft --skill domain-3d -a gemini-cli -y
GitHub Copilot
$ npx skills add mxslr/mlcraft --skill domain-3d -a github-copilot -y
OpenCode
$ npx skills add mxslr/mlcraft --skill domain-3d -a opencode -y
Windsurf
$ npx skills add mxslr/mlcraft --skill domain-3d -a windsurf -y
SKILL.md
domain-3d.SKILL.mdname: domain-3d
description: "Use for 3D and point cloud tasks: point cloud classification, 3D semantic or instance segmentation, 3D object detection from LiDAR, and depth or mesh analysis, including autonomous-driving and indoor-scan settings. Picks the right 3D backbone, representation, and scene-level split and metric. Triggers on 'point cloud', '3D', 'LiDAR', 'mesh', 'depth', '3D detection', '3D segmentation', 'ScanNet', 'KITTI', 'ModelNet', 'autonomous driving perception'."
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 with `rigorous-evaluation`.
Read more
name: domain-3d description: "Use for 3D and point cloud tasks: point cloud classification, 3D semantic or instance segmentation, 3D object detection from LiDAR, and depth or mesh analysis, including autonomous-driving and indoor-scan settings. Picks the right 3D backbone, representation, and scene-level split and metric. Triggers on 'point cloud', '3D', 'LiDAR', 'mesh', 'depth', '3D detection', '3D segmentation', 'ScanNet', 'KITTI', 'ModelNet', 'autonomous driving perception'."
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 with `rigorous-evaluation`.
823 skills1 agent
Stars on GitHub
Momentum
- stars in total
- 8
Other skills on mlcraft.
More skills from other plugins.
Auto-invoked
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or…
@obraInstall Skillask-matt
Ask which skill or flow fits your situation. A router over the skills in this repo.
@mattpocockInstall SkillAuto-invoked
Questions about domain-3d.
- Which coding agents can use domain-3d?
- domain-3d installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.