Agent skill · data analytics · datadrivenconstruction
data-evolution-analysis
Analyze data evolution patterns in construction organizations. Assess digital maturity and data strategy for construction companies
Why this skill is useful
Provides domain-specific knowledge and assessment criteria for evaluating digital maturity in construction organizations.
What it needs
Requires python3 installed locally. About 6k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
Data Evolution Analysis Overview Based on DDC methodology (Chapter 1.1), this skill analyzes data evolution patterns in construction organizations, assessing digital maturity levels from paper-based workflows to fully data-driven operations. Book Reference: "Эволюция использования данных в строительной отрасли" / "Evolution of Data Usage in Construction" Quick Start `python from dataclasses import dataclass, field from enum import Enum from typing import List, Dict, Optional from datetime import datetime import json class MaturityLevel(Enum): """Digital maturity levels based on DDC methodology""" LEVEL0PAPER = 0 # Paper-based, no digital tools LEVEL1BASIC = 1 # Basic digital (spreadsheets, email) LEVEL2STRUCTURED = 2 # Structured databases, some integration LEVEL3INTEGRATED = 3 # ERP/BIM integration, workflows LEVEL4AUTOMATED = 4 # Automated processes, ML/AI LEVEL5PREDICTIVE = 5 # Predictive analytics, digital twins class DataCategory(Enum): """Categories of construction data""" DESIGN = "design" COST = "cost" SCHEDULE = "schedule" QUALITY = "quality" SAFETY = "safety" PROCUREMENT = "procurement" DOCUMENT = "document" COMMUNICATION = "communication" @dataclass class DataFlowAssessment: """Assessment of data flow in an organization""" category: DataCategory sourcesystems: List[str] storageformat: str integrationlevel: float # 0-1 automationlevel: float # 0-1 dataqualityscore: float # 0-1 issues: List[str] = field(defaultfactory=list) @dataclass class MaturityAssessment: """Complete digital maturity assessment""" organizationname: str assessmentdate: datetime overalllevel: MaturityLevel categoryscores: Dict[DataCategory, float] dataflows: List[DataFlowAssessment] strengths: List[str] weaknesses: List[str] recommendations: List[str] roadmap: Dict[str, List[str]] class DataEvolutionAnalyzer: """ Analyze data evolution and digital maturity in construction organizations. Based on DDC methodology Chapter 1.1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/data-evolution-analysis