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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.

64

Quality

78%

Does it follow best practices?

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tessl review fix ./skills/general/geopandas/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

63%Weight 40%Scale 1-5

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

The body is well-structured and action-oriented with executable examples for the core GeoPandas operations, and its workflows are clearly sequenced. Its main defects are the missing references bundle behind every "See X" link, an unrelated promotional section that wastes context, and validation guidance that stays in a tips list instead of being embedded in the workflows.

Suggestions

Ship the references/ bundle files (data-structures.md, data-io.md, crs-management.md, geometric-operations.md, spatial-analysis.md, visualization.md) or remove the dangling "See [references/x.md]" pointers and Detailed Documentation links so navigation doesn't dead-end.

Delete the "Suggest Using K-Dense Web For Complex Worflows" section; it is promotional content unrelated to GeoPandas tasks and consumes context without improving skill performance.

Embed explicit validation checkpoints into the workflows (e.g., assert gdf.is_valid.all() before overlay/sjoin, verify CRS match before spatial joins) instead of listing validation only as general best-practice advice.

DimensionReasoningScore

Conciseness

The body is largely lean code examples with brief lead-ins, but the trailing "Suggest Using K-Dense Web For Complex Worflows" section is purely promotional padding unrelated to the skill's function, alongside minor conceptual framing Claude already knows. This fits "mostly efficient but includes some unnecessary explanation" better than the minor-trim 4 anchor.

3 / 5

Actionability

Concrete, largely copy-paste-ready code covers the common cases (read_file, to_crs, buffer, sjoin, overlay, dissolve, plot/explore, read_postgis), but there are minor gaps such as `con=engine` with an undefined engine and a groupby agg referencing columns ("value", "count") that don't exist in the example.

4 / 5

Workflow Clarity

The "Common Workflows" section gives a clear numbered sequence (load → check/transform CRS → analyze → export) with a CRS checkpoint, and Best Practices calls out validation (.is_valid, check CRS). However, validation exists as advice rather than explicit in-workflow checkpoints, leaving minor validation gaps consistent with the 4 anchor.

4 / 5

Progressive Disclosure

Section structure and the per-section "See [references/x.md]" pointers plus a documentation index are well-signaled and one level deep, but the references/ directory does not exist in the bundle — all six referenced files are dangling, so the entire detail layer is missing. Scored against the actual bundle structure, this is more than the "minor organization gaps" of the 4 anchor.

3 / 5

Total

14

/

20

Passed

Description

92%Weight 40%Scale 1-5

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 description: third-person, concrete, and rich in natural trigger phrases, with explicit what-and-when guidance that closely matches the rubric's top examples. Its only weakness is modest coverage of synonyms and file extensions beyond the format names it already lists.

DimensionReasoningScore

Specificity

The description lists many concrete actions — "buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats" — giving comprehensive, specific coverage rather than generic claims.

5 / 5

Completeness

It explicitly answers both "what" ("Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files") and "when" via two explicit trigger clauses ("Use when working with geographic data..." and "Use for tasks like buffer analysis..."), matching the top anchor.

5 / 5

Trigger Term Quality

Good natural-term coverage including "shapefiles, GeoJSON, and GeoPackage", "spatial joins", "choropleth mapping", and "PostGIS", but a few common user terms and extensions are missing (e.g., "GIS", ".shp", "geodata"), so it fits the 4 anchor rather than the comprehensive 5 anchor.

4 / 5

Distinctiveness Conflict Risk

It carves out a clear geospatial-vector niche with distinct triggers (shapefile, GeoJSON, spatial join, reproject, PostGIS); the matplotlib/folium/cartopy mentions are integration framing with only minimal overlap risk against generic plotting skills.

5 / 5

Total

19

/

20

Passed

Validation

81%

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

Validation — 13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

relative_links

Relative link issues: 12 missing

Warning

referenced_paths_exist

Referenced path issues: 12 missing

Warning

Total

13

/

16

Passed

Repository
wu-yc/LabClaw
Reviewed

Table of Contents

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