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版本：v2.1.0
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⭐ 1
安装量（当前） 4
🛡 VirusTotal ：良性 · OpenClaw ：良性
Package：datadrivenconstruction/data-evolution-analysis
安全扫描（ClawHub）
- VirusTotal ：良性
- OpenClaw ：良性
OpenClaw 评估
The skill's requirements and instructions are coherent with its stated purpose (analyzing construction data using Python) and there are no obvious attempts to access unrelated credentials or external endpoints, but a few minor inconsistencies and permissions to watch for are noted.
目的
Name/description (data evolution analysis for construction) align with the content: SKILL.md provides Python classes and methods for assessing digital maturity and the skill declares python3 as a required binary. Accepting file inputs (CSV/Excel/JSON) is consistent with the stated purpose.
说明范围
Runtime instructions and instructions.md focus on processing user-provided data and follow-the-doc constraints. The skill accepts file paths and expects filesystem access to read those files — this is appropriate for the use case but means the agent can read files the user points it to. There are no instructions (in the provided excerpts) telling the agent to scan unrelated system paths, environment variables, or to exfiltrate data to third-pa…
安装机制
No install spec and no code files to execute are included; the skill is instruction-only and relies on python3 being present. This is low-risk compared with arbitrary downloads or package installs.
证书
The skill does not request environment variables, credentials, or external service keys. That is proportional to its stated functionality. The only declared capability in claw.json is 'filesystem', which matches the need to read user-supplied files.
持久
The skill is not always-enabled and does not request elevated platform privileges. It can be invoked by the agent (default), which is normal. There is no evidence it modifies other skills or global agent settings.
综合结论
This skill appears to do what it says: analyze construction data using Python and files you provide. Before installing or running it: 1) Verify the skill source/homepage and owner if you need provenance (registry owner is not a known organization here). 2) Be cautious about which file paths you provide — the skill has filesystem permission and will read any files you point it at; do not give it sensitive OS or credential files. 3) Note the ski…
安装（复制给龙虾 AI）
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请把本段交给龙虾中文库（龙虾 AI）执行：为本机安装 OpenClaw 技能「Data Evolution Analysis」。简介：Analyze data evolution patterns in construction organizations. Assess digital m…。
请 fetch 以下地址读取 SKILL.md 并按文档完成安装：https://raw.githubusercontent.com/openclaw/skills/refs/heads/main/skills/datadrivenconstruction/data-evolution-analysis/SKILL.md
（来源：yingzhi8.cn 技能库）SKILL.md
---
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"]}}}
---
# 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"""
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"]
}
}
def assess_organization(
self,
organization_name: str,
survey_responses: Dict[str, any],
system_inventory: List[Dict],
process_documentation: Optional[Dict] = None
) -> MaturityAssessment:
"""
Perform comprehensive digital maturity assessment.
Args:
organization_name: Name of the organization
survey_responses: Responses from maturity survey
system_inventory: List of systems/tools in use
process_documentation: Optional process documentation
Returns:
Complete maturity assessment
"""
# Analyze data flows
data_flows = self._analyze_data_flows(system_inventory, survey_responses)
# Calculate category scores
category_scores = self._calculate_category_scores(
data_flows, survey_responses
)
# Determine overall maturity level
overall_score = sum(
score * self.assessment_criteria[cat]["weight"]
for cat, score in category_scores.items()
)
overall_level = self._score_to_level(overall_score)
# Identify strengths and weaknesses
strengths, weaknesses = self._identify_gaps(category_scores)
# Generate recommendations
recommendations = self._generate_recommendations(
overall_level, weaknesses, data_flows
)
# Create roadmap
roadmap = self._create_roadmap(overall_level, recommendations)
return MaturityAssessment(
organization_name=organization_name,
assessment_date=datetime.now(),
overall_level=overall_level,
category_scores=category_scores,
data_flows=data_flows,
strengths=strengths,
weaknesses=weaknesses,
recommendations=recommendations,
roadmap=roadmap
)
def _analyze_data_flows(
self,
system_inventory: List[Dict],
survey_responses: Dict
) -> List[DataFlowAssessment]:
"""Analyze data flows between systems"""
flows = []
for category in DataCategory:
# Find systems for this category
category_systems = [
s for s in system_inventory
if s.get("category") == category.value
]
if not category_systems:
flows.append(DataFlowAssessment(
category=category,
source_systems=[],
storage_format="none",
integration_level=0.0,
automation_level=0.0,
data_quality_score=0.0,
issues=["No systems identified for this category"]
))
continue
# Analyze integration and automation
integration = self._calculate_integration_score(category_systems)
automation = self._calculate_automation_score(
category_systems, survey_responses
)
quality = survey_responses.get(
f"{category.value}_data_quality", 0.5
)
# Identify issues
issues = self._identify_flow_issues(
category_systems, integration, automation
)
flows.append(DataFlowAssessment(
category=category,
source_systems=[s["name"] for s in category_systems],
storage_format=category_systems[0].get("format", "unknown"),
integration_level=integration,
automation_level=automation,
data_quality_score=quality,
issues=issues
))
return flows
def _calculate_integration_score(
self, systems: List[Dict]
) -> float:
"""Calculate integration score for systems"""
if not systems:
return 0.0
total_integrations = sum(
len(s.get("integrations", [])) for s in systems
)
max_integrations = len(systems) * 3 # Assume max 3 integrations per system
return min(1.0, total_integrations / max_integrations)
def _calculate_automation_score(
self,
systems: List[Dict],
survey: Dict
) -> float:
"""Calculate automation score"""
scores = []
for system in systems:
system_score = 0.0
if system.get("has_api"):
system_score += 0.3
if system.get("automated_imports"):
system_score += 0.3
if system.get("automated_exports"):
system_score += 0.2
if system.get("workflow_automation"):
system_score += 0.2
scores.append(system_score)
return sum(scores) / len(scores) if scores else 0.0
def _calculate_category_scores(
self,
data_flows: List[DataFlowAssessment],
survey: Dict
) -> Dict[DataCategory, float]:
"""Calculate maturity score for each category"""
scores = {}
for flow in data_flows:
# Combine different aspects
tool_score = survey.get(f"{flow.category.value}_tool_maturity", 0.5)
process_score = survey.get(f"{flow.category.value}_process_maturity", 0.5)
category_score = (
tool_score * 0.3 +
process_score * 0.2 +
flow.integration_level * 0.2 +
flow.automation_level * 0.2 +
flow.data_quality_score * 0.1
)
scores[flow.category] = category_score
return scores
def _score_to_level(self, score: float) -> MaturityLevel:
"""Convert numeric score to maturity level"""
if score < 0.1:
return MaturityLevel.LEVEL_0_PAPER
elif score < 0.25:
return MaturityLevel.LEVEL_1_BASIC
elif score < 0.45:
return MaturityLevel.LEVEL_2_STRUCTURED
elif score < 0.65:
return MaturityLevel.LEVEL_3_INTEGRATED
elif score < 0.85:
return MaturityLevel.LEVEL_4_AUTOMATED
else:
return MaturityLevel.LEVEL_5_PREDICTIVE
def _identify_gaps(
self,
scores: Dict[DataCategory, float]
) -> tuple[List[str], List[str]]:
"""Identify strengths and weaknesses"""
avg_score = sum(scores.values()) / len(scores)
strengths = [
f"{cat.value}: {score:.0%}"
for cat, score in scores.items()
if score > avg_score + 0.1
]
weaknesses = [
f"{cat.value}: {score:.0%}"
for cat, score in scores.items()
if score < avg_score - 0.1
]
return strengths, weaknesses
def _identify_flow_issues(
self,
systems: List[Dict],
integration: float,
automation: float
) -> List[str]:
"""Identify issues in data flow"""
issues = []
if integration < 0.3:
issues.append("Low system integration - data silos likely")
if automation < 0.3:
issues.append("Manual data transfer required")
if len(systems) > 3:
issues.append("Multiple overlapping systems")
return issues
def _generate_recommendations(
self,
level: MaturityLevel,
weaknesses: List[str],
flows: List[DataFlowAssessment]
) -> List[str]:
"""Generate improvement recommendations"""
recommendations = []
# Level-specific recommendations
level_recs = {
MaturityLevel.LEVEL_0_PAPER: [
"Implement basic digital tools (spreadsheets, file sharing)",
"Digitize critical paper-based processes",
"Train staff on basic digital skills"
],
MaturityLevel.LEVEL_1_BASIC: [
"Adopt specialized construction software",
"Implement structured data storage",
"Standardize data formats and naming conventions"
],
MaturityLevel.LEVEL_2_STRUCTURED: [
"Integrate key systems (ERP, PM, BIM)",
"Implement Common Data Environment (CDE)",
"Develop automated workflows"
],
MaturityLevel.LEVEL_3_INTEGRATED: [
"Implement advanced analytics and dashboards",
"Explore IoT for automated data collection",
"Develop machine learning models for prediction"
],
MaturityLevel.LEVEL_4_AUTOMATED: [
"Implement digital twin technology",
"Deploy AI-driven decision support",
"Enable predictive maintenance and operations"
],
MaturityLevel.LEVEL_5_PREDICTIVE: [
"Continuous optimization of AI models",
"Expand autonomous decision-making",
"Industry leadership and knowledge sharing"
]
}
recommendations.extend(level_recs.get(level, []))
# Address specific weaknesses
for flow in flows:
if flow.integration_level < 0.3:
recommendations.append(
f"Improve {flow.category.value} system integrations"
)
if flow.data_quality_score < 0.5:
recommendations.append(
f"Implement data quality controls for {flow.category.value}"
)
return recommendations[:10] # Top 10 recommendations
def _create_roadmap(
self,
current_level: MaturityLevel,
recommendations: List[str]
) -> Dict[str, List[str]]:
"""Create phased improvement roadmap"""
return {
"Phase 1 (0-6 months)": recommendations[:3],
"Phase 2 (6-12 months)": recommendations[3:6],
"Phase 3 (12-24 months)": recommendations[6:],
"Target Level": [
f"Move from {current_level.name} to "
f"{MaturityLevel(min(current_level.value + 1, 5)).name}"
]
}
def compare_assessments(
self,
assessments: List[MaturityAssessment]
) -> Dict:
"""Compare multiple assessments over time or across organizations"""
comparison = {
"assessments": len(assessments),
"levels": [a.overall_level.name for a in assessments],
"trends": {},
"best_practices": []
}
# Track category trends
for category in DataCategory:
scores = [a.category_scores[category] for a in assessments]
comparison["trends"][category.value] = {
"scores": scores,
"improvement": scores[-1] - scores[0] if len(scores) > 1 else 0
}
return comparison
def generate_report(
self,
assessment: MaturityAssessment
) -> str:
"""Generate executive summary report"""
stage_info = self.evolution_stages[assessment.overall_level]
report = f"""
# Digital Maturity Assessment Report
## {assessment.organization_name}
**Assessment Date:** {assessment.assessment_date.strftime('%Y-%m-%d')}
**Overall Maturity Level:** {assessment.overall_level.name} - {stage_info['name']}
### Executive Summary
{stage_info['description']}
### Category Scores
"""
for cat, score in assessment.category_scores.items():
bar = "█" * int(score * 10) + "░" * (10 - int(score * 10))
report += f"- {cat.value.title()}: {bar} {score:.0%}n"
report += "n### Strengthsn"
for strength in assessment.strengths:
report += f"- {strength}n"
report += "n### Areas for Improvementn"
for weakness in assessment.weaknesses:
report += f"- {weakness}n"
report += "n### Recommendationsn"
for i, rec in enumerate(assessment.recommendations, 1):
report += f"{i}. {rec}n"
report += "n### Roadmapn"
for phase, items in assessment.roadmap.items():
report += f"n**{phase}**n"
for item in items:
report += f"- {item}n"
return report
class DataEvolutionTracker:
"""Track data evolution over time"""
def __init__(self, organization_name: str):
self.organization = organization_name
self.history: List[MaturityAssessment] = []
self.milestones: List[Dict] = []
def add_assessment(self, assessment: MaturityAssessment):
"""Add new assessment to history"""
self.history.append(assessment)
self._check_milestones(assessment)
def _check_milestones(self, assessment: MaturityAssessment):
"""Check if any milestones were reached"""
if len(self.history) > 1:
prev = self.history[-2]
# Level improvement
if assessment.overall_level.value > prev.overall_level.value:
self.milestones.append({
"date": assessment.assessment_date,
"type": "level_up",
"description": f"Advanced from {prev.overall_level.name} "
f"to {assessment.overall_level.name}"
})
# Category improvements
for cat in DataCategory:
if assessment.category_scores[cat] - prev.category_scores[cat] > 0.2:
self.milestones.append({
"date": assessment.assessment_date,
"type": "category_improvement",
"description": f"Significant improvement in {cat.value}"
})
def get_evolution_summary(self) -> Dict:
"""Get summary of evolution over time"""
if not self.history:
return {"error": "No assessments recorded"}
return {
"organization": self.organization,
"first_assessment": self.history[0].assessment_date,
"latest_assessment": self.history[-1].assessment_date,
"starting_level": self.history[0].overall_level.name,
"current_level": self.history[-1].overall_level.name,
"total_assessments": len(self.history),
"milestones": self.milestones,
"level_progression": [a.overall_level.value for a in self.history]
}
```
## Common Use Cases
### Assess Current Digital Maturity
```python
analyzer = DataEvolutionAnalyzer()
# Define systems in use
systems = [
{"name": "AutoCAD", "category": "design", "has_api": False},
{"name": "Revit", "category": "design", "has_api": True, "integrations": ["Navisworks"]},
{"name": "Excel", "category": "cost", "has_api": False},
{"name": "MS Project", "category": "schedule", "has_api": False},
{"name": "Email", "category": "communication", "has_api": False}
]
# Survey responses (from questionnaire)
survey = {
"design_tool_maturity": 0.6,
"design_process_maturity": 0.5,
"design_data_quality": 0.7,
"cost_tool_maturity": 0.3,
"cost_process_maturity": 0.4,
"cost_data_quality": 0.5,
"schedule_tool_maturity": 0.4,
"schedule_process_maturity": 0.3,
"schedule_data_quality": 0.4
}
assessment = analyzer.assess_organization(
organization_name="Construction Co",
survey_responses=survey,
system_inventory=systems
)
print(f"Maturity Level: {assessment.overall_level.name}")
print(f"Recommendations: {assessment.recommendations[:3]}")
```
### Track Evolution Over Time
```python
tracker = DataEvolutionTracker("Construction Co")
# Add quarterly assessments
tracker.add_assessment(q1_assessment)
tracker.add_assessment(q2_assessment)
tracker.add_assessment(q3_assessment)
summary = tracker.get_evolution_summary()
print(f"Progress: {summary['starting_level']} → {summary['current_level']}")
print(f"Milestones: {len(summary['milestones'])}")
```
### Generate Executive Report
```python
report = analyzer.generate_report(assessment)
print(report)
# Save to file
with open("maturity_report.md", "w") as f:
f.write(report)
```
## Quick Reference
| Component | Purpose |
|-----------|---------|
| `DataEvolutionAnalyzer` | Main assessment engine |
| `MaturityLevel` | 6 levels from paper to predictive |
| `DataCategory` | 8 categories (design, cost, schedule, etc.) |
| `DataFlowAssessment` | Analyze data flows per category |
| `MaturityAssessment` | Complete assessment results |
| `DataEvolutionTracker` | Track progress over time |
## Resources
- **Book**: "Data-Driven Construction" by Artem Boiko, Chapter 1.1
- **Website**: https://datadrivenconstruction.io
## Next Steps
- Use [data-silo-detection](../../Chapter-1.2/data-silo-detection/SKILL.md) to identify integration gaps
- Use [erp-integration-analysis](../../Chapter-1.2/erp-integration-analysis/SKILL.md) for system integration
- Use [digital-maturity-assessment](../../Chapter-5.1/digital-maturity-assessment/SKILL.md) for detailed assessments