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 Lord1Egypt/scientific-agent-toolkit --skill geopandasgit clone --depth 1 https://github.com/Lord1Egypt/scientific-agent-toolkitWrote 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/lord1egypt/scientific-agent-toolkit/geopandas)<a href="https://agentmods.dev/skills/lord1egypt/scientific-agent-toolkit/geopandas"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/geopandas/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/lord1egypt/scientific-agent-toolkit/geopandas"><img src="https://agentmods.dev/badge/skills/lord1egypt/scientific-agent-toolkit/geopandas.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 | $0.00125 | $0.01768 |
| Opus 5 | $0.00063 | $0.00884 |
| Sonnet 5 | $0.00025 | $0.00354 |
| Haiku 4.5 | $0.00013 | $0.00177 |
Grade A, and why
geopandas 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 11d 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.
This is a copy
94% identical to geopandas — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GeoPandas
GeoPandas extends pandas to enable spatial operations on geometric types. It combines the capabilities of pandas and shapely for geospatial data analysis.
Installation
uv pip install geopandas
Optional Dependencies
# For interactive maps
uv pip install folium
# For classification schemes in mapping
uv pip install mapclassify
# For faster I/O operations (2-4x speedup)
uv pip install pyarrow
# For PostGIS database support
uv pip install psycopg2
uv pip install geoalchemy2
# For basemaps
uv pip install contextily
# For cartographic projections
uv pip install cartopy
Quick Start
import geopandas as gpd
# Read spatial data
gdf = gpd.read_file("data.geojson")
# Basic exploration
print(gdf.head())
print(gdf.crs)
print(gdf.geometry.geom_type)
# Simple plot
gdf.plot()
# Reproject to different CRS
gdf_projected = gdf.to_crs("EPSG:3857")
# Calculate area (use projected CRS for accuracy)
gdf_projected['area'] = gdf_projected.geometry.area
# Save to file
gdf.to_file("output.gpkg")
Core Concepts
Data Structures
- GeoSeries: Vector of geometries with spatial operations
- GeoDataFrame: Tabular data structure with geometry column
See data-structures.md for details.
Reading and Writing Data
GeoPandas reads/writes multiple formats: Shapefile, GeoJSON, GeoPackage, PostGIS, Parquet.
# Read with filtering
gdf = gpd.read_file("data.gpkg", bbox=(xmin, ymin, xmax, ymax))
# Write with Arrow acceleration
gdf.to_file("output.gpkg", use_arrow=True)
See data-io.md for comprehensive I/O operations.
Coordinate Reference Systems
Always check and manage CRS for accurate spatial operations:
# Check CRS
print(gdf.crs)
# Reproject (transforms coordinates)
gdf_projected = gdf.to_crs("EPSG:3857")
# Set CRS (only when metadata missing)
gdf = gdf.set_crs("EPSG:4326")
See crs-management.md for CRS operations.
What ships with it
6 files 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.
- 11d ago First seen · 250 lines · 125 tokens per session scan A 2b9c5128b7bf
geopandas is a skill published in the GitHub repository Lord1Egypt/scientific-agent-toolkit (3 stars, last pushed 3mo ago), licensed MIT. It adds 125 tokens to every session and 1,768 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to geopandas, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
alterlab-anndata
Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations, concatenating batches, or handling layers/obsm/backed-mode; for the…
alterlab-rdkit
Provides the RDKit cheminformatics toolkit for low-level, fine-grained molecular primitives — SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure/SMARTS search, 2D/3D coordinate generation, similarity, and reaction handling. Use when custom sanitization, specialized fingerprint or descriptor…
alterlab-sympy
Symbolic mathematics in Python with SymPy — solve equations algebraically, perform calculus (derivatives, integrals, limits), manipulate algebraic expressions, work with symbolic matrices, and generate executable code from formulas. Use when exact symbolic results are needed rather than numerical approximations, or…
alterlab-cirq
Builds, simulates, and runs quantum circuits with Cirq, Google Quantum AI's framework for NISQ hardware, noise-aware low-level circuit design, and noise characterization. Use when targeting Google Quantum AI processors (Sycamore/Weber), designing noise-aware NISQ circuits, or running characterization experiments…
alterlab-pydeseq2
Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially expressed genes between conditions from raw…
alterlab-pymoo
Multi-objective optimization with pymoo — NSGA-II, NSGA-III, MOEA/D, Pareto-front computation, constraint handling, and standard benchmarks (ZDT, DTLZ). Use when solving multi-objective or constrained optimization problems, computing Pareto-optimal trade-offs, or tackling engineering design problems with competing…