No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by datadrivenconstruction · Agent Tool · ★ 220
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is OpenConstructionEstimate-DDC-CWICR safe to install? View the security audit →
DDC CWICR - Construction Work Items, Components & Resources Pipelines n8n for calculating estimates based on descriptions, photos, and CAD (BIM) 🇬🇧 English • 🇨🇳 中文 • 🇪🇸 Español • 🇧🇷 Português • 🇷🇺 Русский • 🇯🇵 日本語 • 🇩🇪 Deutsch • 🇫🇷 Français <img src="https://img.sh
| Stars | 220 |
| Forks | 55 |
| Language | HTML |
| Category | Agent Tool |
| Quality Score | 59.2688044203767/100 |
| Open Issues | 5 |
| Last Updated | 2026-08-21 |
| Created | 2025-12-09 |
| Platforms | claude-code |
| Est. Tokens | ~28k |
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OpenConstructionEstimate-DDC-CWICR is Open multilingual construction cost database for AI Agents - 55K+ work items, 27K+ resources, 30 regions. Semantic search via Qdrant vector DB. It is categorized as a Agent Tool with 220 GitHub stars.
OpenConstructionEstimate-DDC-CWICR is primarily written in HTML. It covers topics such as agentic-workflow, ai-agents, antigravity.
You can find installation instructions and usage details in the OpenConstructionEstimate-DDC-CWICR GitHub repository at github.com/datadrivenconstruction/OpenConstructionEstimate-DDC-CWICR. The project has 220 stars and 55 forks, indicating an active community.
The top alternatives to OpenConstructionEstimate-DDC-CWICR on Agent Skills Hub include agent-sessions, antigravity-skills, hcom. 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.
Sources & who's responsible: