Agent skill · data analytics · datadrivenconstruction
5000-projects-analysis
Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.
Why this skill is useful
Provides domain-specific analysis scripts for benchmarking and pattern detection in construction projects that the AI wouldn't generate on its own.
What it needs
Requires python3 installed locally. About 3k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
Large-Scale BIM Project Analysis Business Case Problem Statement Construction companies lack industry benchmarks because: Individual project data is insufficient for statistical analysis Comparable project data is not available Manual analysis doesn't scale to thousands of projects Solution Analyze 5000+ IFC and Revit projects to extract patterns, create benchmarks, and train ML models for prediction. Business Value Industry benchmarks - Compare your project to 5000+ others Pattern detection - Identify common designs and issues ML training data - Build predictive models with real data Research foundation - Academic and industry research dataset Technical Implementation Dataset Overview Metric Value -------- ------- Total Projects 5000+ File Formats IFC, RVT Elements Millions Categories 200+ Analysis Pipeline Analysis Examples Insights You Can Extract Structural Patterns Average wall-to-floor ratio Typical door/window counts per area MEP element density benchmarks Quality Indicators Category completeness Parameter fill rates Geometric consistency Complexity Metrics Elements per m² of floor area Category diversity index Level count vs building height Integration with ML Resources Kaggle Notebook: 5000 Projects Analysis Dataset: Available via DataDrivenConstruction.io
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/5000-projects-analysis