A powerful Python-based tool for automating the extraction of bioclimatic data from global WorldClim rasters based on geographic coordinates.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install worldclim-extract
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install worldclim-extract using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is WorldClim Climate Data Extractor?
The WorldClim Climate Data Extractor is a specialized tool within the Openclaw Skills ecosystem designed to bridge the gap between geographic sample points and environmental context. It enables researchers and developers to automatically process large-scale GeoTIFF files provided by WorldClim 2.1, mapping longitude and latitude coordinates to 19 different bioclimatic variables including temperature and precipitation metrics. By integrating this into your Openclaw Skills workflow, you can handle massive datasets without manual GIS software intervention.
This skill simplifies complex geospatial operations by providing a ready-to-use CLI and Python API. Whether you are conducting ecological studies, population genetics, or environmental modeling, this tool ensures high-precision data retrieval at various resolutions, from 10-minute to 2.5-minute grids. It handles data cleaning, unit conversion, and file formatting, making it an essential component of any modern environmental data pipeline.
WorldClim Climate Data Extractor Use Cases
- Matching ecological sample points to local annual temperature or precipitation averages.
- Batch processing climate metadata for thousands of geographic coordinates in CSV or Excel format.
- Automating the collection of bioclimatic variables for population genetics and environmental modeling.
- Validating field research data against standardized global climate records.
How WorldClim Climate Data Extractor Works
- The user provides an input file in Excel or CSV format containing decimal coordinates for longitude and latitude.
- The system checks for the presence of WorldClim 2.1 GeoTIFF files in the local cache or automatically downloads them if missing.
- Using the rasterio library, the skill samples the specific grid cell corresponding to each coordinate pair.
- Extracted climate values such as Annual Mean Temperature or Precipitation are appended as new columns to the original dataset.
- The final enriched dataset is exported to a new Excel or CSV file for downstream analysis.
WorldClim Climate Data Extractor Setup
To begin using this tool with Openclaw Skills, ensure you have Python 3.10+ installed. Install the required dependencies using the following command:
pip install rasterio pandas openpyxl
You can then run the extraction script directly from your terminal:
python3 extract_worldclim.py -i your_samples.xlsx -o results.xlsx --bios 1-19
WorldClim Climate Data Extractor Data Schema & Taxonomy
The skill processes coordinate data and outputs structured climate variables. The following schema defines the output structure when using Openclaw Skills:
| Field | Type | Description |
|---|---|---|
| Longitude | Decimal | Coordinates between -180 and 180 |
| Latitude | Decimal | Coordinates between -90 and 90 |
| BIO1 - BIO11 | Float | Temperature-based variables (typically in °C) |
| BIO12 - BIO19 | Float | Precipitation-based variables (typically in mm) |
Supported input formats include .xlsx and .csv. The tool automatically handles common formatting issues like hidden spaces or text-based numeric values.
WorldClim Climate Data Extractor Advanced Features
- Automatic multi-resolution support allowing users to choose between 10m, 5m, or 2.5m grid sizes.
- Built-in caching mechanism to manage large GeoTIFF files efficiently without repeated downloads.
- Programmatic Python API for seamless integration into custom data science and machine learning pipelines.
- Smart coordinate cleaning that uses pandas to fix common data entry errors automatically.
SKILL.md
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