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 tondevrel/scientific-agent-skills --skill shapelygit clone --depth 1 https://github.com/tondevrel/scientific-agent-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/tondevrel/scientific-agent-skills/shapely)<a href="https://agentmods.dev/skills/tondevrel/scientific-agent-skills/shapely"><img src="https://agentmods.dev/badge/skills/tondevrel/scientific-agent-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/tondevrel/scientific-agent-skills/shapely"><img src="https://agentmods.dev/badge/skills/tondevrel/scientific-agent-skills/shapely.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 | $0.00105 | $0.02767 |
| Opus 5 | $0.00053 | $0.01384 |
| Sonnet 5 | $0.00021 | $0.00553 |
| Haiku 4.5 | $0.00011 | $0.00277 |
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 9d 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 — 347 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shapely - Planar Geometry
Shapely is the engine behind GeoPandas and many other GIS tools. It focuses on the geometry itself: calculating intersections, unions, distances, and checking spatial relationships (like "is this point inside this polygon?").
When to Use
- Precise manipulation of 2D geometric shapes.
- Performing set-theoretic operations (Intersection, Union, Difference).
- Checking spatial predicates (Contains, Within, Intersects, Touches).
- Cleaning and validating "dirty" geometry (fixing self-intersections).
- Calculating geometric properties (Area, Length, Centroid, Bounds).
- Generating buffers or simplifying complex lines.
- Linear referencing (finding points along a line).
Reference Documentation
Official docs: https://shapely.readthedocs.io/
GEOS (Engine): https://libgeos.org/
Search patterns: shapely.geometry, shapely.ops.unary_union, shapely.validation.make_valid
Core Principles
Geometric Objects
Objects are immutable. Once created, you don't change them; you perform an operation that returns a new object.
- Points: 0-dimensional.
- LineStrings: 1-dimensional curves.
- Polygons: 2-dimensional surfaces with optional holes.
Cartesian Geometry
Shapely operates in a Cartesian plane. It does not know about Earth's curvature, latitudes, or longitudes. Distance is sqrt(dx² + dy²).
Vectorization (Shapely 2.0+)
Modern Shapely supports vectorized operations on NumPy arrays of geometry objects, making it significantly faster than older versions.
Quick Reference
Installation
pip install shapely numpy
Standard Imports
import numpy as np
from shapely import Point, LineString, Polygon, MultiPoint, MultiPolygon
from shapely import ops, wkt, wkb
import shapely
Basic Pattern - Creation and Analysis
from shapely.geometry import Point, Polygon
# 1. Create objects
p = Point(0, 0)
poly = Polygon([(0, 0), (2, 0), (2, 2), (0, 2)])
# 2. Check relationships
is_inside = p.within(poly) # True
is_on_border = p.touches(poly) # False (interior counts as within)
# 3. Calculate
print(f"Area: {poly.area}")
print(f"Distance: {p.distance(Point(10, 10))}")
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.
- 9d ago First seen · 347 lines · 105 tokens per session scan A d0316a995fa7
shapely is a skill published in the GitHub repository tondevrel/scientific-agent-skills (21 stars, last pushed 7mo ago), licensed MIT. It adds 105 tokens to every session and 2,767 once invoked, about $0.0005 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 2026-08-30.
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