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.

Quality

86%

Does it follow best practices?

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Securityby

Passed

No findings from the security scan

The canonical home for this skill is geopandas in K-Dense-AI/scientific-agent-skills

Quality

Content

72%

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

A well-structured, highly actionable skill body with excellent progressive disclosure and executable examples; its weaknesses are mild verbosity/redundancy and workflows that omit explicit validation checkpoints before destructive writes.

Suggestions

Trim redundant prose and de-duplicate the reference links: keep either the inline 'See ...' pointers or the 'Detailed Documentation' list, not both, and remove the pandas/shapely explanation Claude already knows.

Add an explicit validation checkpoint to the Common Workflows, e.g. assert gdf.is_valid.all() and confirm CRS matching before calling to_file() on multi-layer outputs.

Treat the 'Performance Tips' and 'Best Practices' lists as a single concise checklist to reduce overlap and token cost.

Dimension	Reasoning	Score
Conciseness	Mostly efficient code-driven content, but it includes some explanatory padding Claude already knows ('GeoPandas extends pandas...combines the capabilities of pandas and shapely') and redundant link lists (the inline 'See ...' references are repeated in 'Detailed Documentation'), so it could be tightened.	2 / 3
Actionability	Provides fully executable, copy-paste-ready code throughout (read_file, to_file, sjoin, sjoin_nearest, overlay, dissolve, plot, explore) with concrete parameters, matching the executable-examples anchor.	3 / 3
Workflow Clarity	The 'Common Workflows' show a numbered Load/Transform/Analyze/Export sequence but lack explicit validation checkpoints; since the workflows write output files and the rubric caps clarity at 2 when validation is missing for batch/destructive operations, this is not a level-3 sequenced-with-feedback-loop workflow.	2 / 3
Progressive Disclosure	SKILL.md is a concise overview with well-signaled one-level-deep references to six real files in references/ (data-structures, data-io, crs-management, geometric-operations, spatial-analysis, visualization), each linked inline and summarized in a navigation section.	3 / 3
	Total	10 / 12 Passed

Description

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.

A strong, well-targeted description: it names concrete capabilities, supplies explicit use-when triggers with natural vocabulary, and occupies a clear niche unlikely to conflict with other skills.

Dimension	Reasoning	Score
Specificity	Lists many concrete actions including 'buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats', matching the multiple-specific-actions anchor.	3 / 3
Completeness	Explicitly answers both what (a Python library for geospatial vector data supporting named formats, PostGIS, and matplotlib/folium/cartopy) and when via two explicit 'Use when/Use for' trigger clauses.	3 / 3
Trigger Term Quality	Covers natural terms users would say such as 'shapefiles, GeoJSON, and GeoPackage', 'spatial joins', 'choropleth mapping', 'PostGIS', and 'spatial file formats', giving broad coverage rather than technical jargon alone.	3 / 3
Distinctiveness Conflict Risk	The geospatial vector-data niche with distinctive triggers (shapefiles, GeoJSON, GeoPackage, PostGIS, choropleth, spatial joins) makes it unlikely to fire for unrelated skills.	3 / 3
	Total	12 / 12 Passed

Validation

87%

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 — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
metadata_version	'metadata.version' is missing	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/data-engineering/geopandas/SKILL.md
Commit: 52845c3

Reviewed: about 10 hours ago

Table of Contents

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