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 shapelygit 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/shapely)<a href="https://agentmods.dev/skills/znlgis/opengis-skills/shapely"><img src="https://agentmods.dev/badge/skills/znlgis/opengis-skills/shapely/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/shapely"><img src="https://agentmods.dev/badge/skills/znlgis/opengis-skills/shapely.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00044 | $0.03704 |
| Opus 5 | $0.00022 | $0.01852 |
| Sonnet 5 | $0.00009 | $0.00741 |
| Haiku 4.5 | $0.00004 | $0.00370 |
Grade A, and why
shapely 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 today.
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 — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
项目地址： https://github.com/shapely/shapely
官方文档： https://shapely.readthedocs.io/
PyPI： https://pypi.org/project/shapely/
许可证： BSD-3-Clause
概述
Shapely 是 Python 计算几何的核心库，基于 GEOS（Geometry Engine - Open Source）的 C++ 几何引擎。它为 PostGIS、GeoPandas、PyQGIS 等众多开源 GIS 项目提供几何计算底层支持。核心能力：
- 几何对象模型：Point、LineString、Polygon、Multi*、GeometryCollection
- 空间关系判断：contains、intersects、within、touches、covers、disjoint、relate（DE-9IM）
- 集合运算：intersection、union、difference、symmetric_difference
- 构造操作：buffer、simplify、convex_hull、delaunay_triangles、voronoi_polygons、make_valid
- 几何度量：area、length、distance、hausdorff_distance、frechet_distance
- 空间索引：STRtree，加速批量空间查询
- 格式读写：WKT、WKB、GeoJSON
- 双重 API：函数式（向量化，支持 NumPy 广播）+ OOP（标量便捷）
环境要求： Python 3.9+，Shapely 2.0+
Geometry Types
| Type | Class | Description |
|---|---|---|
| Point | shapely.Point |
Single coordinate (x, y [, z [, m]]) |
| LineString | shapely.LineString |
Ordered sequence of 2+ points |
| LinearRing | shapely.LinearRing |
Closed, simple LineString |
| Polygon | shapely.Polygon |
Exterior ring + optional holes |
| MultiPoint | shapely.MultiPoint |
Collection of Points |
| MultiLineString | shapely.MultiLineString |
Collection of LineStrings |
| MultiPolygon | shapely.MultiPolygon |
Collection of Polygons |
| GeometryCollection | shapely.GeometryCollection |
Heterogeneous collection |
Z coordinates are ignored for all spatial analysis — operations are performed in the x-y plane only.
Dual API Pattern
Shapely provides two APIs. Prefer the function-based API for arrays and performance; use the OOP API for scalar convenience.
Function-based (vectorized, NumPy ufunc)
All functions release the GIL during GEOS execution, support NumPy broadcasting, and handle arrays natively.
import shapely
import numpy as np
geoms = np.array([shapely.Point(0, 0), shapely.Point(1, 1)])
shapely.area(geoms) # array of floats
shapely.contains(polygon, geoms) # array of bools
shapely.buffer(geoms, 1.0) # array of polygons
shapely.distance(geoms, other) # array of distances
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today Changed cf0e01ddb9a4
- 2d ago Changed 5b70ce58a11f
- 9d ago First seen · 339 lines · 44 tokens per session scan A 05d4d8405d23
shapely is a skill published in the GitHub repository znlgis/opengis-skills (61 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 3,704 once invoked, about $0.0002 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.
Other skills, from other repositories
spatial-regulation
Python API skill for spatial transcription-factor activity and regulatory network inference.
mcp-numpy
MCP server exposing NumPy functionality.
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with…
dd-code-generation
Use pup CLI for immediate Datadog operations or generate code for integration into applications.
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…