An advanced Python-based utility for parsing Binary STL files to extract geometric properties and material metadata.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install 3d-scan-calc-mesh-analysis
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 3d-scan-calc-mesh-analysis 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 Mesh Analysis?
The Mesh Analysis skill provides a robust MeshAnalyzer tool designed to handle the complexities of 3D geometry processing within the Openclaw Skills ecosystem. It specializes in Binary STL parsing, allowing AI agents and developers to programmatically calculate volumes and analyze connected components in 3D models. This skill is particularly valuable for processing raw or noisy 3D scan data where debris needs to be filtered to identify the primary object.
Beyond basic geometry, this tool focuses on the technical nuances of the STL format, such as extracting the 2-byte attribute data often used for material identification or color coding. By utilizing this skill in Openclaw Skills, users can bridge the gap between raw mesh data and actionable physical properties like mass and material composition.
Mesh Analysis Use Cases
- Geometric analysis for calculating the volume of complex or noisy 3D meshes.
- Noise filtering to isolate the largest connected component from dirty scan data.
- Attribute extraction for identifying material IDs stored in STL binary headers.
- Automated mass calculation workflows by combining volume data with density tables.
How Mesh Analysis Works
- The AI agent imports the MeshAnalyzer module from the designated Openclaw Skills script directory.
- A Binary STL file path is provided to the analyzer for initialization.
- The tool parses the mesh connectivity to identify distinct geometric components.
- It performs a connected component analysis to isolate the largest part from any background noise.
- The tool calculates the volume based on the coordinate system of the input file.
- It extracts the binary attribute bytes to identify material or metadata properties.
- A final report is generated containing the volume, material ID, and component data.
Mesh Analysis Setup
To use this tool within your environment, ensure the script path is accessible. You can add the skill path to your Python environment as follows:
# Add the specific skill path to your PYTHONPATH
export PYTHONPATH=$PYTHONPATH:/root/.claude/skills/mesh-analysis/scripts
Inside your script, initialize the MeshAnalyzer with the path to your target STL file to begin processing with Openclaw Skills.
Mesh Analysis Data Schema & Taxonomy
The skill returns structured data regarding the mesh geometry and metadata. The output is organized as follows:
| Property | Description |
|---|---|
| main_part_volume | The volume of the largest identified component in the mesh. |
| main_part_material_id | The 2-byte attribute value extracted from the Binary STL. |
| units | Units are inherited directly from the STL coordinate system. |
Mesh Analysis Advanced Features
- Robust Binary STL support for industry-standard 3D file formats.
- Automated noise reduction through intelligent connected component identification.
- Direct attribute byte extraction for proprietary material tagging.
- Flexible unit handling that supports any coordinate system provided in the mesh data for Openclaw Skills.
SKILL.md
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