geopandas

Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

1.35x

Quality

86%

Does it follow best practices?

Impact

87%

1.35x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Quality

Discovery

100%

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

This is a strong skill description that thoroughly covers specific capabilities, includes abundant natural trigger terms, explicitly states both what the skill does and when to use it, and occupies a clearly distinct niche. The description is comprehensive without being padded with fluff, listing concrete actions and file formats that users would naturally reference. Minor improvement could be made by slightly tightening the length, but the content is well-structured and highly functional for skill selection.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: buffer analysis, spatial joins, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, converting between spatial file formats, overlay operations, choropleth mapping.	3 / 3
Completeness	Clearly answers both 'what' (Python library for geospatial vector data with specific operations listed) and 'when' (explicit 'Use when' clause covering spatial analysis, geometric operations, coordinate transformations, etc., plus a second 'Use for' clause with concrete task examples).	3 / 3
Trigger Term Quality	Excellent coverage of natural terms users would say: shapefiles, GeoJSON, GeoPackage, geographic data, spatial analysis, spatial joins, choropleth, coordinate transformations, PostGIS, matplotlib, folium, cartopy, buffer analysis, reprojecting, and specific file format mentions.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive niche focused on geospatial vector data with specific triggers like shapefiles, GeoJSON, GeoPackage, PostGIS, spatial joins, and choropleth mapping that are unlikely to conflict with non-geospatial skills.	3 / 3
	Total	12 / 12 Passed

Implementation

72%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

This is a well-structured GeoPandas skill with strong actionability and excellent progressive disclosure. The main weaknesses are some verbosity in sections like optional dependencies and best practices that Claude likely already knows, and the absence of validation checkpoints in the multi-step workflows despite mentioning geometry validation in best practices.

Suggestions

Integrate validation steps into the Common Workflows sections (e.g., check gdf.geometry.is_valid after loading, verify CRS after reprojection, validate output file exists after export).

Trim the optional dependencies section and best practices/performance tips — much of this is general knowledge for Claude or could be moved to a reference file to improve conciseness.

Dimension	Reasoning	Score
Conciseness	Generally efficient but includes some unnecessary content like the optional dependencies section (Claude knows how to install packages), the 'Core Concepts' data structures bullet definitions, and some tips/best practices that are general knowledge for Claude. The quick start section has comments like 'Basic exploration' that add little value.	2 / 3
Actionability	Provides fully executable, copy-paste ready code examples throughout — reading files, reprojecting, buffering, spatial joins, overlays, visualization, and multi-source integration. All code snippets are concrete and use real library APIs with realistic parameters.	3 / 3
Workflow Clarity	The 'Common Workflows' section provides clear multi-step sequences (Load, Transform, Analyze, Export), but lacks validation checkpoints. For geospatial operations, there's no explicit validation step (e.g., checking .is_valid after operations, verifying CRS transformation succeeded, or confirming output file integrity). The best practices mention .is_valid but it's not integrated into the workflows.	2 / 3
Progressive Disclosure	Excellent structure with a concise overview and quick start at the top, followed by well-signaled one-level-deep references to six detailed documentation files. The main SKILL.md provides enough context to get started while clearly pointing to specialized reference files for deeper topics.	3 / 3
	Total	10 / 12 Passed

Validation

90%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 10 / 11 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning

	Total	10 / 11 Passed

Repository: K-Dense-AI/claude-scientific-skills
Commit: b58ad7e

Reviewed: 2 days ago

Table of Contents

Discovery Implementation Validation

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