GeoPandas Spatial Analysis
davila7/claude-code-templates
Handles vector geospatial data in Python with GeoPandas: reading shapefiles, GeoJSON and GeoPackage, reprojecting, spatial joins, overlays, clipping and maps.
A skill your agent uses when working with GeoPandas GeoSeries and GeoDataFrame objects, geometry columns, CRS metadata, Shapely-backed geometry arrays, and spatial-index basics.
Install Core Data Model
$ npx skills add VectorSpaceLab/AREX-Skill --skill core-data-model -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill core-data-model --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model .claude/skills/core-data-model && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "core-data-model" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model into .claude/skills/core-data-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "core-data-model", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/geopandas/sub-skills/core-data-modelType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add VectorSpaceLab/AREX-Skill --skill core-data-model -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill core-data-model --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model .agents/skills/core-data-model && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "core-data-model" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model into .agents/skills/core-data-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "core-data-model", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill core-data-model -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill core-data-model --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model .cursor/skills/core-data-model && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "core-data-model" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model into .cursor/skills/core-data-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "core-data-model", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/VectorSpaceLab/AREX-Skill.git --path skills/repositories/repo-skills/geopandas/sub-skills/core-data-model--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add VectorSpaceLab/AREX-Skill --skill core-data-model -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill core-data-model --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model .gemini/skills/core-data-model && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "core-data-model" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model into .gemini/skills/core-data-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "core-data-model", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install VectorSpaceLab/AREX-Skill core-data-modelInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add VectorSpaceLab/AREX-Skill --skill core-data-model -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model .github/skills/core-data-model && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "core-data-model" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model into .github/skills/core-data-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "core-data-model", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add VectorSpaceLab/AREX-Skill --skill core-data-model -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install VectorSpaceLab/AREX-Skill core-data-model --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model .opencode/skills/core-data-model && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "core-data-model" agent skill from https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/geopandas/sub-skills/core-data-model into .opencode/skills/core-data-model/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "core-data-model", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
core-data-model- GitHub stars
- 328
- Token cost
- ~1k tokens
- SKILL.md length
- 435 words
- Files
- 5 (incl. scripts, references)
- Skills in repo
- 159
- Repo updated
- First seen
- Licence
- BSD-3-Clause
At a glance
A skill your agent uses when working with GeoPandas GeoSeries and GeoDataFrame objects, geometry columns, CRS metadata, Shapely-backed geometry arrays, and spatial-index basics.
- Works in 6 steps: Confirm the object type and active… → Inspect .crs on all layers. Use… → For distance, area, buffer, nearest, or… → …
- Working with GeoPandas GeoSeries and GeoDataFrame objects
- SKILL.md covers Read First, Route Here When, Route Elsewhere and Default Operating Rules, plus 2 more sections
- Runs Python scripts from its folder; calls python
What it does
Core Data Model is an agent skill from VectorSpaceLab/AREX-Skill. Use when working with GeoPandas GeoSeries and GeoDataFrame objects, geometry columns, CRS metadata, Shapely-backed geometry arrays, and spatial-index basics.
Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/api-reference.md`, `references/troubleshooting.md` and `references/workflows.md`).
It sits in Data & Analytics, covering Geospatial analysis. It works with pandas. The repository describes itself as: A Skill Library for Automated Machine Learning. The licence is BSD-3-Clause.
When your agent uses it
- Working with GeoPandas GeoSeries and GeoDataFrame objects
- Geometry columns
- Shapely-backed geometry arrays
- Spatial-index basics
Example prompts
- “/core-data-model”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Confirm the object type and active geometry column before applying GeoPandas methods.
- Inspect .crs on all layers. Use set_crs() only to attach known metadata; use to_crs() to reproject coordinates.
- For distance, area, buffer, nearest, or metric thresholds, prefer a projected CRS with suitable linear units.
- Treat None/missing geometries separately from empty Shapely geometries.
- When pandas operations strip GeoPandas metadata, rebuild with GeoDataFrame(df, geometry=..., crs=...) or call set_geometry() deliberately.
- Use spatial index as an optimization or query surface, not as a replacement for exact Shapely predicates.
What it can do on your machine
Read from SKILL.md and the folder at commit ac3fe1a. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Core Data Model loads about 1k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 43 tokens; SKILL.md has 435 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from VectorSpaceLab/AREX-Skill at commit ac3fe1a, republished under its BSD-3-Clause licence (© VectorSpaceLab). 435 words, ~1,001 tokens.
Download SKILL.mdSave it as
.claude/skills/core-data-model/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- core-data-model
- description
- Use when working with GeoPandas GeoSeries and GeoDataFrame objects, geometry columns, CRS metadata, Shapely-backed geometry arrays, and spatial-index basics.
- disable-model-invocation
- true
- metadata.disco-role
- operating
- license
- BSD 3-Clause
Core Data Model
Use this sub-skill when the task is about creating, inspecting, repairing, or transforming GeoPandas objects before any file I/O, spatial analysis, map rendering, or testing-specific assertion work.
Read First
- API reference: verified signatures and object rules for
GeoSeries,GeoDataFrame, geometry columns, CRS, coordinate access, and spatial index basics. - Workflows: practical recipes for construction, active geometry repair, CRS assignment/reprojection, missing/empty geometry checks, and pandas interoperability.
- Troubleshooting: symptoms and fixes for missing active geometry, wrong CRS, metric operation misuse, invalid or missing geometries, and pandas conversions.
- validate_geodataframe.py: safe helper that builds or reads a tiny GeoDataFrame and reports geometry column, CRS, bounds, empty/missing geometry, and optional spatial-index status.
Route Here When
- The user asks how to build a
GeoDataFrameorGeoSeriesfrom Shapely objects, WKT/WKB, x/y coordinates, features, or tabular data. - The task mentions
geometry, active geometry column,set_geometry,rename_geometry,active_geometry_name, or pandas operations that lost geometry semantics. - The task involves
.crs,set_crs,to_crs,estimate_utm_crs, EPSG codes, or deciding whether coordinates need assignment versus transformation. - The question is about missing/empty geometries,
isna(),is_empty, bounds, coordinate extraction, geometry dtype, or when operations return pandas versus GeoPandas objects. - The task asks for spatial index basics such as
.sindex,has_sindex, valid predicates, or why a spatial operation is slow.
Route Elsewhere
- Use
../io-formats/SKILL.mdforread_file,to_file, GeoJSON, WKB/WKT persistence, Arrow/Parquet/Feather, or PostGIS. - Use
../spatial-operations/SKILL.mdfor joins, overlays, clips, dissolves, unary/binary geometry analysis, nearest searches, and predicate selection. - Use
../mapping-geocoding/SKILL.mdfor.plot(),.explore(), folium, matplotlib, geocoding, or map classification. - Use
../validation-testing/SKILL.mdforgeopandas.testingassertions or focused native test commands.
Show full SKILL.md (179 more words)Show less
Default Operating Rules
- Confirm the object type and active geometry column before applying GeoPandas methods.
- Inspect
.crson all layers. Useset_crs()only to attach known metadata; useto_crs()to reproject coordinates. - For distance, area, buffer, nearest, or metric thresholds, prefer a projected CRS with suitable linear units.
- Treat
None/missing geometries separately from empty Shapely geometries. - When pandas operations strip GeoPandas metadata, rebuild with
GeoDataFrame(df, geometry=..., crs=...)or callset_geometry()deliberately. - Use spatial index as an optimization or query surface, not as a replacement for exact Shapely predicates.
Minimal Checks
Run from this sub-skill directory or any other current working directory:
python scripts/validate_geodataframe.py --default-fixture --build-sindexExpected high-level signal: the report names the active geometry column, CRS, row count, total bounds, missing and empty geometry counts, and whether a spatial index could be built.
Handoff to Other Workflows
- After constructing or repairing objects, move to
spatial-operationsfor analysis orio-formatsfor persistence. - Before maps or real geocoding, move to
mapping-geocodingand check optional dependencies. - When the result must be asserted in code review or tests, move to
validation-testing.
© VectorSpaceLab, BSD-3-Clause. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (scripts, references) in skills/repositories/repo-skills/geopandas/sub-skills/core-data-model of VectorSpaceLab/AREX-Skill.
- SKILL.md
- references/api-reference.md
- references/troubleshooting.md
- references/workflows.md
- scripts/validate_geodataframe.py
Open the folder on GitHubat commit ac3fe1a
Compare with similar skills
Core Data Model next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Core Data Model this skillVectorSpaceLab/AREX-Skill | 328 | — | ~1k | Automated safety check: Pass | BSD-3-Clause | |
| GeoPandas Spatial Analysisdavila7/claude-code-templates | 32k | 10 repos | ~1.8k | Automated safety check: Pass | MIT | |
| Geopandas Geospatialjaechang-hits/SciAgent-Skills | 370 | 1 repos | ~3.8k | Automated safety check: Pass | BSD-3-Clause | |
| Geopandasaipoch/medical-research-skills | 2k | — | ~2k | Automated safety check: Pass | MIT | |
| SeabornzLanqing/codex-claude-academic-skills | 4.6k | 16 repos | ~4.9k | Automated safety check: Pass | BSD-3-Clause | |
| Fix Module Not Found ErrorNuitka/Nuitka | 15k | — | ~519 | Automated safety check: Pass | AGPL-3.0 |
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A Python library for reading, writing, and analyzing geospatial vector data; use it when you need spatial operations (buffer/overlay/join), CRS reprojection, or map visualization on formats like…
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Diagnose and fix ModuleNotFoundError in Nuitka standalone binaries caused by missing implicit imports.
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Questions about Core Data Model
What does Core Data Model do?
A skill your agent uses when working with GeoPandas GeoSeries and GeoDataFrame objects, geometry columns, CRS metadata, Shapely-backed geometry arrays, and spatial-index basics. Core Data Model is an agent skill from VectorSpaceLab/AREX-Skill. Use when working with GeoPandas GeoSeries and GeoDataFrame objects, geometry columns, CRS metadata, Shapely-backed geometry arrays, and spatial-index basics.
When should I use Core Data Model?
Core Data Model fits situations like: working with GeoPandas GeoSeries and GeoDataFrame objects; geometry columns; shapely-backed geometry arrays; spatial-index basics.
How do I install Core Data Model in Claude Code?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill core-data-model -a claude-code`. Or copy the skill folder (skills/repositories/repo-skills/geopandas/sub-skills/core-data-model in VectorSpaceLab/AREX-Skill) into .claude/skills/core-data-model in your project. Claude Code loads it when a task matches its description.
How do I install Core Data Model in Codex?
Run `npx skills add VectorSpaceLab/AREX-Skill --skill core-data-model -a codex`. Or copy the skill folder (skills/repositories/repo-skills/geopandas/sub-skills/core-data-model in VectorSpaceLab/AREX-Skill) into .agents/skills/core-data-model in your project. Codex loads it when a task matches its description.
Can I use Core Data Model in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add VectorSpaceLab/AREX-Skill --skill core-data-model -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/core-data-model, .gemini/skills/core-data-model, .github/skills/core-data-model and .opencode/skills/core-data-model in your project.
What does Core Data Model need to run?
Going by SKILL.md and its folder, Core Data Model needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Core Data Model access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Core Data Model safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Core Data Model use?
Core Data Model is published under the BSD-3-Clause licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Core Data Model use?
About 1k tokens (SKILL.md is roughly 4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.9k tokens, read only when the agent opens those files.
What are the alternatives to Core Data Model?
Skills that share tags, products or a category with Core Data Model: GeoPandas Spatial Analysis (davila7/claude-code-templates, 32k stars), Geopandas Geospatial (jaechang-hits/SciAgent-Skills, 370 stars), Geopandas (aipoch/medical-research-skills, 2k stars) and Seaborn (zLanqing/codex-claude-academic-skills, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Core Data Model?
VectorSpaceLab (a GitHub organization) maintains it in VectorSpaceLab/AREX-Skill, which has 328 GitHub stars. The repository holds 159 skills in this directory. The repository was last updated on September 3, 2026.
Source: VectorSpaceLab/AREX-Skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.