geo-infer-data
Data connectors, ETL pipelines, and data management for geospatial datasets. Use when loading spatial data from databases, APIs, files (GeoJSON, Shapefile, GeoParquet), or building data transformation pipelines.
Best use case
geo-infer-data is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Data connectors, ETL pipelines, and data management for geospatial datasets. Use when loading spatial data from databases, APIs, files (GeoJSON, Shapefile, GeoParquet), or building data transformation pipelines.
Teams using geo-infer-data should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/GEO-INFER-DATA/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How geo-infer-data Compares
| Feature / Agent | geo-infer-data | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Data connectors, ETL pipelines, and data management for geospatial datasets. Use when loading spatial data from databases, APIs, files (GeoJSON, Shapefile, GeoParquet), or building data transformation pipelines.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# GEO-INFER-DATA
## Instructions
### Core Capabilities
- **Connectors**: PostgreSQL/PostGIS, SQLite/SpatiaLite, REST APIs, file I/O
- **Formats**: GeoJSON, Shapefile, GeoParquet, GeoTIFF, CSV with coordinates
- **ETL pipelines**: Extract → Transform → Load with spatial awareness
- **Caching**: Spatial tile caching, query result caching
- **Validation**: Schema validation, coordinate bounds checking
### Key Imports
```python
from geo_infer_data.connectors.database import DatabaseConnector
from geo_infer_data.core.pipeline import ETLPipeline
from geo_infer_data.formats.geojson import GeoJSONLoader
```
## Examples
```python
from geo_infer_data.formats.geojson import GeoJSONLoader
from geo_infer_data.core.validation import CoordinateValidator
loader = GeoJSONLoader()
features = loader.load("buildings.geojson")
print(f"Loaded {len(features)} features")
# Validate coordinates against WGS84 bounds
validator = CoordinateValidator()
valid, invalid = validator.validate(features)
print(f"Valid: {len(valid)}, Out-of-bounds: {len(invalid)}")
```
```python
from geo_infer_data.core.pipeline import ETLPipeline
pipeline = ETLPipeline(name="census_ingest")
pipeline.extract(source="postgresql://db/census", query="SELECT * FROM tracts")
pipeline.transform(operations=["reproject_to_4326", "validate_bounds"])
pipeline.load(target="geoparquet", path="output/census.parquet")
pipeline.run()
```
## Guidelines
- SQL uses parameterized queries (`:param` placeholders) — never string interpolation
- All coordinate data validated against WGS84 bounds
- Test: `uv run python -m pytest GEO-INFER-DATA/tests/ -v`
### Integrations
- **SPACE** → Spatial indexing of loaded datasets
- **GIT** → Version control for spatial data
- **API** → Data source for spatial query endpoints
- **IOT** → Sensor data ingestion pipelines
- **EXAMPLES** → Example ETL workflows