An industrial-grade vision skill leveraging YOLOv11 and MobileSAM to provide pixel-perfect coral coverage analysis for marine researchers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install coral-precise-coverage-ai
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 coral-precise-coverage-ai 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 Precision Coral Metrics API (YOLO+SAM)?
The Precision Coral Metrics AI (CM-AI) is a sophisticated computer vision tool designed to transform how marine ecologists and environmental agencies perform reef assessments. By utilizing a hybrid architecture of YOLOv11 for detection and MobileSAM for segmentation, this skill effectively replaces manual, error-prone workflows with rapid, automated data extraction. As a key component of the Openclaw Skills ecosystem, it provides the precision needed for high-stakes environmental monitoring.
Built for high-noise underwater environments, CM-AI excels where traditional vision models fail. It offers researchers a scalable way to process vast amounts of visual data, ensuring that coral colony identification and surface area calculations are both consistent and scientifically rigorous.
Precision Coral Metrics API (YOLO+SAM) Use Cases
- Automated reef health monitoring for environmental conservation agencies.
- Rapid processing of underwater transect imagery for marine biology research.
- Biodiversity indexing and colony population counting in complex reef ecosystems.
- Large-scale environmental impact assessments following climate events or coastal development.
How Precision Coral Metrics API (YOLO+SAM) Works
- The user uploads high-resolution underwater imagery to the endpoint secured by the Openclaw Skills gateway.
- The YOLOv11 backend performs high-fidelity detection to locate individual coral colonies amidst complex backgrounds.
- MobileSAM is triggered for refined mask extraction, generating pixel-perfect segmentation of the identified coral organisms.
- The system calculates the total Coral Coverage Ratio (%) and provides individual colony counts.
- Structured results are returned to the user, with royalties processed via the integrated monetization protocol.
Precision Coral Metrics API (YOLO+SAM) Setup
To integrate this skill, you must route requests through the Openclaw Skills API Gateway. Ensure you have a valid developer token from the dashboard.
# Example request using cURL
curl -X POST https://api.openclaw.io/v1/skills/coral-ai/predict \
-H "X-OpenClaw-Token: <YOUR_TOKEN>" \
-F "file=@/path/to/reef_image.jpg"
Precision Coral Metrics API (YOLO+SAM) Data Schema & Taxonomy
The skill processes image files and returns detailed metrics in a structured format. Data organization follows this taxonomy:
| Attribute | Type | Description |
|---|---|---|
coverage_ratio |
Float | The percentage of the image area covered by coral. |
colony_count |
Integer | The total number of unique coral colonies detected. |
segmentation_masks |
Array | Pixel-level mask data for each detected organism. |
detection_confidence |
Float | Probability score for the YOLOv11 detection phase. |
Precision Coral Metrics API (YOLO+SAM) Advanced Features
- Hybrid YOLOv11 + MobileSAM architecture for superior accuracy in high-noise environments.
- Native integration with the Openclaw Skills monetization protocol for automated royalty settlement.
- GPU-powered backend processing for near real-time analysis of high-resolution files.
- Roadmap features include genus-level identification (Acropora, Brain Coral, etc.) and automated biodiversity index analysis.
SKILL.md
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