A specialized toolkit for inspecting, validating, and extracting structural data from KML and KMZ geospatial files.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install kml
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 kml 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 KML Skill for AI Agents?
The KML skill is a robust diagnostic and extraction tool designed for AI agents to handle Google Earth and GIS data formats. It provides deep visibility into the internal structure of both flat KML files and compressed KMZ archives, allowing for immediate analysis of placemarks, geometry types, and folder hierarchies.
By utilizing Openclaw Skills, developers can automate the verification of spatial data integrity. This skill excels at identifying common pitfalls such as malformed XML, incorrect coordinate ordering (lon/lat/alt), and empty placemarks, ensuring that geospatial datasets are clean and ready for advanced GIS workflows.
KML Skill for AI Agents Use Cases
- Validating coordinate ranges to prevent longitude and latitude swap errors.
- Summarizing large KMZ archives to identify the primary KML payload and document structure.
- Extracting placemark-level summaries for quick data auditing without opening heavy GIS software.
- Debugging Google Earth packaging issues such as missing doc.kml or broken directory structures.
- Pre-processing geospatial files before hand-off to complex tools like QGIS.
How KML Skill for AI Agents Works
- Format Detection: The skill identifies whether the input is a standard XML-based .kml or a zipped .kmz archive.
- Payload Extraction: For KMZ files, it locates and parses the primary KML document within the archive.
- Structural Analysis: The engine traverses the XML tree to count placemarks, folders, overlays, and specific geometry types.
- Spatial Validation: It calculates a bounding box (bbox) and checks coordinate tuples against WGS84 standards.
- Issue Reporting: Generates a detailed report flagging malformed XML, empty geometries, or suspicious coordinate patterns.
KML Skill for AI Agents Setup
To use this skill, ensure Python 3 is installed on your system. You can install the necessary environment using the following commands:
# Install Python 3 via Homebrew if not present
brew install python
# The skill utilizes a specialized script for processing
# Ensure baseDir points to your Openclaw Skills installation directory
python3 {baseDir}/scripts/kml_tool.py --help
KML Skill for AI Agents Data Schema & Taxonomy
The skill organizes extracted geospatial metadata into a structured summary format:
| Attribute | Description |
|---|---|
| Source Type | Identifies file as 'kml' or 'kmz' |
| Document Name | The internal <name> tag of the KML document |
| Geometry Counts | Breakdown of Points, LineStrings, and Polygons |
| BBox | Calculated bounding box [minLon, minLat, maxLon, maxLat] |
| Validation Errors | List of structural or coordinate-specific warnings |
KML Skill for AI Agents Advanced Features
- KMZ Deep Inspection: Automatically navigates internal archive directories to find valid payloads.
- Coordinate Tuple Validation: Flags altitude errors and out-of-bounds geographic coordinates.
- Intelligent Hand-off: Integrated logic to suggest escalation to QGIS for reprojection or raster processing.
- Placemark Summarization: Generates granular lists of individual markers for targeted data cleanup.
- Openclaw Skills Integration: Optimized for seamless multi-agent workflows within the Openclaw ecosystem.
SKILL.md
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