Analyze data evolution patterns in construction organizations. Assess digital maturity and data strategy for construction companies
数据来源:ClawHub。 在 ClawSkills 查看
选择你使用的 Agent
方法一:命令行安装(推荐)
推荐(无需提前安装 clawhub)
npx clawhub@latest --dir ~/.claude/skills install data-evolution-analysis或使用 clawhub CLI(需提前安装)
clawhub --dir ~/.claude/skills install data-evolution-analysis⚠️ 需要 Node.js 18+,没有 Node?请使用下方方法二直接下载 ZIP。 安装 Node.js →
方法二:手动下载安装(无需 Node)
下载 ZIP,解压后将文件夹放到以下路径,重启 Agent 即可:
安装路径
~/.claude/skills/data-evolution-analysis/💡解压后将文件夹放到上方路径,重启 Agent 即可生效
--- name: "data-evolution-analysis" description: "Analyze data evolution patterns in construction organizations. Assess digital maturity and data strategy for construction companies" homepage: "https://datadrivenconstruction.io" metadata: {"openclaw": {"emoji": "📚", "os": ["win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}} ---
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"
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"""
LEVEL_0_PAPER = 0 # Paper-based, no digital tools
LEVEL_1_BASIC = 1 # Basic digital (spreadsheets, email)
LEVEL_2_STRUCTURED = 2 # Structured databases, some integration
LEVEL_3_INTEGRATED = 3 # ERP/BIM integration, workflows
LEVEL_4_AUTOMATED = 4 # Automated processes, ML/AI
LEVEL_5_PREDICTIVE = 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
source_systems: List[str]
storage_format: str
integration_level: float # 0-1
automation_level: float # 0-1
data_quality_score: float # 0-1
issues: List[str] = field(default_factory=list)
@dataclass
class MaturityAssessment:
"""Complete digital maturity assessment"""
organization_name: str
assessment_date: datetime
overall_level: MaturityLevel
category_scores: Dict[DataCategory, float]
data_flows: 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.
"""
def __init__(self):
self.assessment_criteria = self._load_criteria()
self.evolution_stages = self._define_evolution_stages()
def _load_criteria(self) -> Dict[DataCategory, Dict]:
"""Load assessment criteria for each category"""
return {
DataCategory.DESIGN: {
"tools": ["CAD", "BIM", "Collaboration Platform"],
"metrics": ["model_usage", "clash_detection", "design_reviews"],
"weight": 0.20
},
DataCategory.COST: {
"tools": ["Spreadsheets", "Estimating Software", "ERP"],
"metrics": ["automation_level", "historical_data", "benchmarking"],
"weight": 0.15
},
DataCategory.SCHEDULE: {
"tools": ["Gantt Charts", "CPM Software", "4D BIM"],
"metrics": ["resource_loading", "progress_tracking", "forecasting"],
"weight": 0.15
},
DataCategory.QUALITY: {
"tools": ["Checklists", "QC Software", "Defect Tracking"],
"metrics": ["inspection_digitization", "defect_analytics", "compliance"],
"weight": 0.12
},
DataCategory.SAFETY: {
"tools": ["Incident Reports", "Safety Software", "IoT Sensors"],
"metrics": ["incident_tracking", "predictive_safety", "training"],
"weight": 0.12
},
DataCategory.PROCUREMENT: {
"tools": ["RFQ Manual", "e-Procurement", "Supply Chain"],
"metrics": ["vendor_management", "material_tracking", "integration"],
"weight": 0.10
},
DataCategory.DOCUMENT: {
"tools": ["File Shares", "DMS", "CDE"],
"metrics": ["version_control", "access_control", "searchability"],
"weight": 0.08
},
DataCategory.COMMUNICATION: {
"tools": ["Email", "Collaboration", "Unified Platform"],
"metrics": ["response_time", "transparency", "audit_trail"],
"weight": 0.08
}
}
def _define_evolution_stages(self) -> Dict[MaturityLevel, Dict]:
"""Define characteristics of each evolution stage"""
return {
MaturityLevel.LEVEL_0_PAPER: {
"name": "Paper-Based",
"description": "Manual, paper-based processes",
"characteristics": [
"Physical document storage",
"Manual data entry",
"Limited data sharing",
"No real-time visibility"
],
"typical_tools": ["Paper forms", "Physical filing"]
},
MaturityLevel.LEVEL_1_BASIC: {
"name": "Basic Digital",
"description": "Basic digitization with standalone tools",
"characteristics": [
"Spreadsheets for calculations",
"Email for communication",
"File shares for storage",
"Manual data transfer between systems"
],
"typical_tools": ["Excel", "Word", "Email", "File shares"]
},
MaturityLevel.LEVEL_2_STRUCTURED: {
"name": "Structured Data",
"description": "Structured databases and specialized software",
"characteristics": [
"Department-specific software",
"Structured databases",
"Basic reporting",
"Some standardization"
],
"typical_tools": ["CAD", "Estimating software", "Project software"]
},
MaturityLevel.LEVEL_3_INTEGRATED: {
"name": "Integrated Systems",
"description": "Connected systems with data flow",
"characteristics": [
"ERP integration",
"BIM adoption",
"Automated workflows",
"Cross-department data sharing"
],
"typical_tools": ["BIM", "ERP", "CDE", "BI dashboards"]
},
MaturityLevel.LEVEL_4_AUTOMATED: {
"name": "Automated & Analytics",
"description": "Automation and advanced analytics",
"characteristics": [
"Automated data collection",
"Machine learning models",
"Predictive analytics",
"Real-time dashboards"
],
"typical_tools": ["ML platforms", "IoT", "Advanced analytics"]
},
MaturityLevel.LEVEL_5_PREDICTIVE: {
"name": "Predictive & Autonomous",
"description": "AI-driven, predictive operations",
"characteristics": [
"Digital twins",
"Autonomous decision support",
"Continuous optimization",
"Predictive maintenance"
],
"typical_tools": ["Digital twins", "AI/ML", "Autonomous systems"]
...安装 Data Evolution Analysis 后,可以对 AI 说这些话来触发它
Help me get started with Data Evolution Analysis
Explains what Data Evolution Analysis does, walks through the setup, and runs a quick demo based on your current project
Use Data Evolution Analysis to analyze data evolution patterns in construction organizations
Invokes Data Evolution Analysis with the right parameters and returns the result directly in the conversation
What can I do with Data Evolution Analysis in my data & analytics workflow?
Lists the top use cases for Data Evolution Analysis, with example commands for each scenario
将技能文件夹放到 ~/.claude/skills/data-evolution-analysis/ 目录(个人级,所有项目可用),或 .claude/skills/data-evolution-analysis/(项目级)。重启 AI 客户端后,用 /data-evolution-analysis 主动调用,或让 AI 根据上下文自动发现并使用。
Data Evolution Analysis 支持 Claude、Cursor、OpenClaw,可与这些 AI 平台无缝集成,扩展其能力。
Data Evolution Analysis 可免费安装使用。请查阅仓库了解许可证信息。
Analyze data evolution patterns in construction organizations. Assess digital maturity and data strategy for construction companies
Data Evolution Analysis 属于「Data & Analytics」分类,该分类的技能帮助 AI 智能体在此领域执行专业任务。
Automate my data & analytics tasks using Data Evolution Analysis
Identifies repetitive steps in your workflow and sets up Data Evolution Analysis to handle them automatically