by yihong1120 · MCP Server · ★ 347
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🇬🇧 English | 🇹🇼 繁體中文 Construction Hazard Detection Server API | MCP Server | FCM Notification Server | Violation Records Server | Web BFF | Flutter App | Data Management Server | Streaming Web | Data Augmentation | Evaluation | Train <a href="https://fastapi.
| Stars | 347 |
| Forks | 44 |
| Language | Python |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 56.5648121674601/100 |
| Last Updated | 2026-08-27 |
| Created | 2024-01-02 |
| Platforms | mcp, python |
| Est. Tokens | ~13k |
These tools work well together with Construction-Hazard-Detection for enhanced workflows:
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Construction-Hazard-Detection is Enhances construction site safety using YOLO for object detection, identifying hazards like workers without helmets or safety vests, and proximity to machinery or vehicles. HDBSCAN clusters safety con. It is categorized as a MCP Server with 347 GitHub stars.
Construction-Hazard-Detection is primarily written in Python. It covers topics such as alert-system, apis, artificial-intelligence.
You can find installation instructions and usage details in the Construction-Hazard-Detection GitHub repository at github.com/yihong1120/Construction-Hazard-Detection. The project has 347 stars and 44 forks, indicating an active community.
Construction-Hazard-Detection is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to Construction-Hazard-Detection on Agent Skills Hub include free-ai-resources-x, AITreasureBox, fastapi-mcp-langgraph-template. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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