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 znlgis/opengis-skills --skill open3dgit clone --depth 1 https://github.com/znlgis/opengis-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/znlgis/opengis-skills/open3d)<a href="https://agentmods.dev/skills/znlgis/opengis-skills/open3d"><img src="https://agentmods.dev/badge/skills/znlgis/opengis-skills/open3d/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/znlgis/opengis-skills/open3d"><img src="https://agentmods.dev/badge/skills/znlgis/opengis-skills/open3d.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 | $0.00083 | $0.04948 |
| Opus 5.5 | $0.00033 | $0.01979 |
| Sonnet 5.5 | $0.00017 | $0.00990 |
| Haiku 4.5 | $0.00008 | $0.00495 |
Grade A, and why
open3d 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 5d 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 — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
项目地址： https://github.com/isl-org/Open3D
官方文档： https://www.open3d.org/docs/ ｜ Python API： https://www.open3d.org/docs/release/python_api/
许可证： MIT ｜ 最新版本：
v0.20.0（PyPIopen3d与 GitHub Releases 双核实）｜ 默认分支：main
概述
Open3D 是由 Intel 开源（组织 isl-org）的现代 3D 数据处理库，采用 C++ 内核并提供等价的 Python 与 C++ 接口，覆盖点云、网格、体素与 RGB-D 数据的完整处理链路。它以「一套数据结构 + 一套算法」把 IO、滤波、配准、重建、可视化串联起来，是学术与工程界处理三维数据的事实标准之一。
- 点云：读写、体素/均匀下采样、法线估计、统计与半径离群点移除、平面分割、DBSCAN 聚类、裁剪。
- 配准：粗配准（RANSAC / Fast Global Registration + FPFH 特征）与精配准（ICP，点到点 / 点到面）。
- 网格：表面重建（泊松、Ball Pivoting、Alpha Shape）、二次误差简化、法线计算、平滑与拓扑修复。
- 可视化：
draw_geometries快速查看、O3DVisualizer可编程 GUI、OffscreenRenderer无头渲染与截图。 - 生态：与 NumPy 零拷贝互操作；张量后端与几何深度学习算子；覆盖桌面、服务器与传感器（RealSense）场景。
环境准备
安装
# 需要 Python >= 3.10（PyPI 元数据 requires_python）
pip install open3d
# 升级到最新版本
pip install --upgrade open3d
官方为 Linux / macOS / Windows 提供预编译 wheel，通常无需自行编译 C++ 内核。
验证安装
python -c "import open3d as o3d; print(o3d.__version__)"
# 预期输出：0.20.0
运行环境说明
- 桌面可视化（
draw_geometries）需要 OpenGL / GLFW 图形环境，在本地桌面可直接使用。 - 无头服务器（CI、容器）请改用离屏渲染接口
o3d.visualization.rendering.OffscreenRenderer，不要调用需要窗口的 GUI 接口。 - 官方文档的稳定版位于
https://www.open3d.org/docs/release/，版本号可能滞后于 PyPI 发布。
核心 API 速览
以下函数名与签名均以官方 Python API 文档为准。
点云 IO 与格式支持
| 函数 | 说明 |
|---|---|
o3d.io.read_point_cloud(path) |
按扩展名解码读取点云 |
o3d.io.write_point_cloud(path, pcd) |
按扩展名写出点云 |
o3d.io.read_triangle_mesh(path) / write_triangle_mesh |
读取/写出三角网格 |
点云支持格式（官方 File IO）：xyz、xyzn、xyzrgb、pts、ply、pcd。
网格支持格式：ply、stl、obj、off、gltf / glb。
LAS / LAZ 不在 Open3D 原生支持范围内：需先用 PDAL、laspy 或 CloudCompare 转为
ply/pcd再读入（见 FAQ）。
点云基础操作
| 方法 | 签名要点 | 用途 |
|---|---|---|
voxel_down_sample(voxel_size) |
体素边长 | 均匀降采样，保持几何分布 |
uniform_down_sample(every_k_points) |
每 k 个点取一个 | 快速抽稀 |
estimate_normals(search_param, fast_normal_computation=True) |
KDTreeSearchParamHybrid(radius, max_nn) |
估计法线 |
remove_statistical_outlier(nb_neighbors, std_ratio) |
返回 (pcd, indices) |
统计离群点移除 |
remove_radius_outlier(nb_points, radius) |
返回 (pcd, indices) |
半径离群点移除 |
segment_plane(distance_threshold, ransac_n, num_iterations) |
RANSAC | 平面分割（地面/桌面） |
cluster_dbscan(eps, min_points) |
返回标签数组 | 欧氏聚类分簇 |
compute_point_cloud_distance(target) |
返回距离向量 | 点到点云的距离/误差 |
paint_uniform_color(color) / transform(T) |
颜色、4×4 变换 | 着色、位姿变换 |
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.
- 5d ago First seen · 324 lines · 83 tokens per session scan A 4b8bff991c2c
open3d is a skill published in the GitHub repository znlgis/opengis-skills (68 stars, last pushed today), licensed MIT. It adds 83 tokens to every session and 4,948 once invoked, about $0.0003 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 2026-10-16.
Other skills, from other repositories
cuopt-numerical-optimization-api
LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
cuopt-developer
Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.
Spatial Omics Skills Index
Skills for spatial transcriptomics analysis including single-cell to spatial mapping (MOSCOT), 3D visualization (PyVista), and related spatial workflows.
torchtalk-analyzer
Analyze PyTorch internals across Python, C++, and CUDA layers using the TorchTalk MCP server. Use when asked about how PyTorch operators work internally, where functions are implemented, what would break if code is modified, or finding tests for PyTorch operators.
torchtalk-trace
Trace a PyTorch function's cross-language binding chain (Python -> C++ -> CUDA).
data-analysis
Analyze datasets and create visualizations.