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Geodata Formatter

skill-danielrosehill-claude-data-wrangler-plugin-geodata-formatter · by danielrosehill

Convert tabular geodata (CSV / Excel / Parquet) into GeoJSON (or GeoJSON Seq / newline-delimited GeoJSON) — inferring geometry from lat/lon columns, WKT/WKB columns, or address columns via geocoding. Handles CRS reprojection (default WGS84 / EPSG:4326), feature property selection, and large-file streaming. Use when the user has location data in flat form and needs it as GeoJSON for mapping, GIS,…

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Install

$ agentstack add skill-danielrosehill-claude-data-wrangler-plugin-geodata-formatter

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

Geodata Formatter

Convert CSV / tabular geodata to GeoJSON.

When to invoke

  • User has a flat file with location columns and needs GeoJSON for Leaflet / Mapbox / QGIS / ArcGIS / Kepler.gl / geopandas / PostGIS.
  • User asks "convert to GeoJSON", "make this mappable", "export as geojson".

Supported inputs

  1. Lat/lon columns — pair of numeric columns (lat/lng, latitude/longitude, y/x). Most common.
  2. WKT / WKB geometry column — a string column containing POINT(...), LINESTRING(...), POLYGON(...) etc. (or hex-encoded WKB).
  3. H3 / S2 / geohash — spatial index strings that decode to polygons/cells.
  4. Address / place name columns — requires geocoding; ask user explicitly before using a third-party service and respect rate limits.

Output formats

  • GeoJSON FeatureCollection (.geojson) — single document, good for small/medium datasets.
  • GeoJSON Sequence / NDGeoJSON (.geojsonl or .geojsons) — newline-delimited, good for streaming and >100k features.
  • Optional: TopoJSON via topojson / pytopojson for compactness.
  • Optional: GeoParquet (recommended for big data; a different skill, but link).

Procedure

  1. Identify geometry source columns — lat/lon pair, WKT, geohash, H3, or address. Ask if ambiguous.
  2. Confirm CRS:
  • Default: EPSG:4326 (WGS84) — required by the GeoJSON spec (RFC 7946).
  • If the source is in a different CRS (e.g. British National Grid EPSG:27700, Web Mercator EPSG:3857, Israeli ITM EPSG:2039), reproject via pyproj before writing GeoJSON.
  1. Validate coordinate sanity:
  • Lat in [-90, 90], lon in [-180, 180].
  • Flag rows outside these bounds — often indicates swapped lat/lon. Ask user.
  • NaN / null geometries → emit as {"geometry": null, ...} Features (valid per spec) or drop; ask user.
  1. Pick feature properties — which columns become properties in each Feature. Default all non-geometry columns. Exclude PII-flagged columns by default.
  2. Build Features:
  • Point: {"type":"Point","coordinates":[lon,lat]} — note the lon-first ordering (GeoJSON spec), not lat-first.
  • From WKT: parse with shapely.wkt.loadsshapely.geometry.mapping(...).
  • From H3: decode to cell polygon via h3.cells_to_geo([cell]).
  • From address: geocode via geopy with a user-selected provider (Nominatim, Google, Mapbox, LocationIQ). Confirm ToS/rate limits; cache results; never batch against Nominatim above 1 req/sec.
  1. Write output:
  • Small files: json.dump({"type":"FeatureCollection","features":[...]}).
  • Large files: stream per-feature to .geojsonl or chunk writes to avoid memory bloat.
  • Add a top-level bbox for the FeatureCollection if helpful.
  1. Validate — optional lint via geojson / geojson-pydantic; report any invalid features.
  2. Report — feature count, geometry-type breakdown, rows skipped/flagged, output path, CRS written (always EPSG:4326 for GeoJSON).
  3. Update the data dictionary — record geometry source columns, CRS, geocoding provider if used.

Dependencies

pip install pandas shapely pyproj
# optional
pip install geopandas fiona     # richer read/write of geo formats
pip install h3                  # H3 cells
pip install geopy               # geocoding
pip install geojson-pydantic    # validation

Edge cases

  • Lat/lon swap — the most common bug. Detect when "lat" values exceed ±90 or "lon" names correspond to y-axis. Ask, don't silently swap.
  • Antimeridian crossing — polygons crossing the 180° line need splitting for most renderers. Flag and offer to split via shapely.
  • Very large polygons — simplify via shapely.simplify(tolerance) if downstream tools choke; record the tolerance.
  • Mixed geometry types in one collection — valid GeoJSON but some tools reject. Offer to split per-type.
  • Addresses — refuse to geocode at scale without explicit user consent and a chosen provider; geocoding is often billable and rate-limited.

Safety

Follow the backup policy in CONVENTIONS.md before any in-place rewrite. This skill by default writes a new .geojson or .geojsonl file alongside the source.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.