Comprehensive audit of all construction data sources and systems. Map data flows, identify silos, assess quality, and create integration roadmap.

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Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

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Package format: ZIP package
Source platform: Tencent SkillHub
What's included: claw.json, instructions.md, SKILL.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 2.1.0

Provenance

Publisher: datadrivenconstruction
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Documentation

ClawHub primary doc Primary doc: SKILL.md 6 sections Open source page

Overview

Perform comprehensive audits of construction data sources to identify silos, map data flows, assess quality, and plan integration strategies. Essential for digital transformation and data-driven construction initiatives.

Business Case

Construction organizations typically have 10-50+ data sources: Project management systems Estimating software Scheduling tools Accounting/ERP systems BIM platforms Document management systems Field apps Spreadsheets Note: This skill is vendor-agnostic and works with any data source. Product names mentioned elsewhere in examples are trademarks of their respective owners. This skill helps: Discover all data sources Map data flows and dependencies Identify integration opportunities Prioritize data improvement efforts

Technical Implementation

from dataclasses import dataclass, field from typing import List, Dict, Any, Optional, Set from enum import Enum from datetime import datetime import pandas as pd import json class DataSourceType(Enum): DATABASE = "database" API = "api" FILE_SHARE = "file_share" CLOUD_APP = "cloud_app" SPREADSHEET = "spreadsheet" LEGACY_SYSTEM = "legacy_system" IOT_SENSOR = "iot_sensor" MANUAL_ENTRY = "manual_entry" class DataDomain(Enum): COST = "cost" SCHEDULE = "schedule" BIM = "bim" DOCUMENT = "document" FIELD = "field" SAFETY = "safety" QUALITY = "quality" HR = "hr" ACCOUNTING = "accounting" PROCUREMENT = "procurement" @dataclass class DataSource: name: str source_type: DataSourceType domains: List[DataDomain] owner: str department: str description: str # Technical details technology: str location: str # cloud, on-prem, hybrid access_method: str # API, ODBC, file export, manual # Data characteristics update_frequency: str # real-time, daily, weekly, monthly, ad-hoc data_volume: str # small, medium, large retention_period: str # Quality metrics completeness_score: float = 0.0 accuracy_score: float = 0.0 timeliness_score: float = 0.0 # Integration status integrations: List[str] = field(default_factory=list) is_master: bool = False # Is this the master source for any entity? master_for: List[str] = field(default_factory=list) # Issues known_issues: List[str] = field(default_factory=list) # Metadata last_audit_date: Optional[datetime] = None audit_notes: str = "" @dataclass class DataFlow: source: str target: str flow_type: str # push, pull, bidirectional, manual frequency: str entities: List[str] # What data entities flow transformation: str # none, simple, complex status: str # active, planned, deprecated @dataclass class DataSilo: name: str sources: List[str] impact: str # high, medium, low description: str resolution_options: List[str] class DataSourceAuditor: """Audit and analyze construction data sources.""" def __init__(self): self.sources: Dict[str, DataSource] = {} self.flows: List[DataFlow] = [] self.silos: List[DataSilo] = [] def add_source(self, source: DataSource): """Register a data source.""" self.sources[source.name] = source def add_flow(self, flow: DataFlow): """Register a data flow between sources.""" self.flows.append(flow) def discover_sources_from_survey(self, survey_responses: List[Dict]) -> List[DataSource]: """Create data sources from survey responses.""" sources = [] for response in survey_responses: source = DataSource( name=response['system_name'], source_type=DataSourceType(response['type']), domains=[DataDomain(d) for d in response['domains']], owner=response['owner'], department=response['department'], description=response['description'], technology=response['technology'], location=response['location'], access_method=response['access_method'], update_frequency=response['update_frequency'], data_volume=response['data_volume'], retention_period=response['retention_period'], ) sources.append(source) self.add_source(source) return sources def identify_silos(self) -> List[DataSilo]: """Identify data silos based on integration analysis.""" silos = [] # Find sources with no integrations isolated_sources = [ name for name, source in self.sources.items() if not source.integrations and source.source_type != DataSourceType.MANUAL_ENTRY ] if isolated_sources: silos.append(DataSilo( name="Isolated Systems", sources=isolated_sources, impact="high", description="Systems with no integrations, requiring manual data transfer", resolution_options=[ "Implement API integration", "Set up automated file exports", "Migrate to integrated platform" ] )) # Find duplicate data domains without master domain_sources: Dict[DataDomain, List[str]] = {} for name, source in self.sources.items(): for domain in source.domains: if domain not in domain_sources: domain_sources[domain] = [] domain_sources[domain].append(name) for domain, sources in domain_sources.items(): if len(sources) > 1: # Check if any is designated master masters = [s for s in sources if self.sources[s].is_master] if not masters: silos.append(DataSilo( name=f"No Master for {domain.value}", sources=sources, impact="medium", description=f"Multiple sources for {domain.value} data without designated master", resolution_options=[ "Designate master data source", "Implement MDM solution", "Create data reconciliation process" ] )) # Find one-way flows that should be bidirectional flow_pairs = {} for flow in self.flows: key = tuple(sorted([flow.source, flow.target])) if key not in flow_pairs: flow_pairs[key] = [] flow_pairs[key].append(flow) for (s1, s2), flows in flow_pairs.items(): if len(flows) == 1 and flows[0].flow_type != 'bidirectional': # Check if bidirectional would make sense s1_domains = set(self.sources[s1].domains) s2_domains = set(self.sources[s2].domains) if s1_domains & s2_domains: # Overlapping domains silos.append(DataSilo( name=f"One-way flow: {s1} -> {s2}", sources=[s1, s2], impact="low", description="Data flows one direction only between systems with overlapping domains", resolution_options=[ "Evaluate need for bidirectional sync", "Implement change data capture" ] )) self.silos = silos return silos def assess_source_quality(self, source_name: str, sample_data: pd.DataFrame) -> Dict[str, float]: """Assess data quality for a source based on sample data.""" if source_name not in self.sources: raise ValueError(f"Unknown source: {source_name}") scores = {} # Completeness: % of non-null values completeness = 1 - (sample_data.isnull().sum().sum() / sample_data.size) scores['completeness'] = completeness # Uniqueness: % of unique rows (for key columns) if len(sample_data) > 0: uniqueness = len(sample_data.drop_duplicates()) / len(sample_data) else: uniqueness = 1.0 scores['uniqueness'] = uniqueness # Validity: Basic format checks (simplified) validity_checks = 0 total_checks = 0 for col in sample_data.columns: if 'date' in col.lower(): total_checks += 1 try: pd.to_datetime(sample_data[col], errors='raise') validity_checks += 1 except: pass if 'email' in col.lower(): total_checks += 1 valid_emails = sample_data[col].str.contains(r'@.*\.', na=False).sum() if valid_emails / len(sample_data) > 0.9: validity_checks += 1 scores['validity'] = validity_checks / total_checks if total_checks > 0 else 1.0 # Update source with scores self.sources[source_name].completeness_score = scores['completeness'] self.sources[source_name].accuracy_score = scores['validity'] return scores def create_data_catalog(self) -> pd.DataFrame: """Create a data catalog from all sources.""" catalog_entries = [] for name, source in self.sources.items(): entry = { 'Source Name': name, 'Type': source.source_type.value, 'Domains': ', '.join(d.value for d in source.domains), 'Owner': source.owner, 'Department': source.department, 'Technology': source.technology, 'Location': source.location, 'Access Method': source.access_method, 'Update Frequency': source.update_frequency, 'Data Volume': source.data_volume, 'Integrations': len(source.integrations), 'Is Master': 'Yes' if source.is_master else 'No', 'Quality Score': (source.completeness_score + source.accuracy_score) / 2, 'Known Issues': len(source.known_issues), } catalog_entries.append(entry) return pd.DataFrame(catalog_entries) def generate_integration_matrix(self) -> pd.DataFrame: """Generate integration matrix showing connections between sources.""" source_names = list(self.sources.keys()) matrix = pd.DataFrame( index=source_names, columns=source_names, data='' ) for flow in self.flows: if flow.source in source_names and flow.target in source_names: current = matrix.loc[flow.source, flow.target] symbol = '→' if flow.flow_type == 'push' else '←' if flow.flow_type == 'pull' else '↔' matrix.loc[flow.source, flow.target] = f"{current}{symbol}" if current else symbol return matrix def calculate_integration_score(self) -> Dict[str, float]: """Calculate overall integration score and breakdown.""" if not self.sources: return {'overall': 0.0} scores = {} # Coverage: % of sources with at least one integration integrated = sum(1 for s in self.sources.values() if s.integrations) scores['coverage'] = integrated / len(self.sources) # Master data: % of domains with designated master domains_with_master = set() for source in self.sources.values(): if source.is_master: domains_with_master.update(source.master_for) all_domains = set() for source in self.sources.values(): all_domains.update(d.value for d in source.domains) scores['master_data'] = len(domains_with_master) / len(all_domains) if all_domains else 1.0 # Data quality average quality_scores = [ (s.completeness_score + s.accuracy_score) / 2 for s in self.sources.values() if s.completeness_score > 0 or s.accuracy_score > 0 ] scores['quality'] = sum(quality_scores) / len(quality_scores) if quality_scores else 0.0 # Silo impact high_impact_silos = sum(1 for s in self.silos if s.impact == 'high') scores['silo_risk'] = 1 - (high_impact_silos * 0.2) # Each high-impact silo reduces score # Overall scores['overall'] = ( scores['coverage'] * 0.3 + scores['master_data'] * 0.25 + scores['quality'] * 0.25 + scores['silo_risk'] * 0.2 ) return scores def generate_audit_report(self) -> str: """Generate comprehensive audit report.""" report = ["# Data Source Audit Report", ""] report.append(f"**Audit Date:** {datetime.now().strftime('%Y-%m-%d')}") report.append(f"**Total Sources:** {len(self.sources)}") report.append(f"**Total Data Flows:** {len(self.flows)}") report.append("") # Integration Score scores = self.calculate_integration_score() report.append("## Integration Maturity Score") report.append(f"**Overall Score:** {scores['overall']:.1%}") report.append(f"- Coverage: {scores['coverage']:.1%}") report.append(f"- Master Data: {scores['master_data']:.1%}") report.append(f"- Data Quality: {scores['quality']:.1%}") report.append(f"- Silo Risk: {scores['silo_risk']:.1%}") report.append("") # Sources by Type report.append("## Sources by Type") by_type = {} for source in self.sources.values(): t = source.source_type.value by_type[t] = by_type.get(t, 0) + 1 for t, count in sorted(by_type.items(), key=lambda x: -x[1]): report.append(f"- {t}: {count}") report.append("") # Data Silos report.append("## Identified Data Silos") if self.silos: for silo in self.silos: report.append(f"\n### {silo.name}") report.append(f"**Impact:** {silo.impact}") report.append(f"**Sources:** {', '.join(silo.sources)}") report.append(f"**Description:** {silo.description}") report.append("**Resolution Options:**") for opt in silo.resolution_options: report.append(f"- {opt}") else: report.append("No significant data silos identified.") report.append("") # Recommendations report.append("## Recommendations") recommendations = self._generate_recommendations() for i, rec in enumerate(recommendations, 1): report.append(f"{i}. {rec}") return "\n".join(report) def _generate_recommendations(self) -> List[str]: """Generate recommendations based on audit findings.""" recommendations = [] scores = self.calculate_integration_score() if scores['coverage'] < 0.7: recommendations.append( "Increase integration coverage - over 30% of systems are isolated. " "Prioritize connecting high-value data sources." ) if scores['master_data'] < 0.5: recommendations.append( "Implement Master Data Management - designate authoritative sources " "for key entities (projects, vendors, employees, cost codes)." ) if scores['quality'] < 0.7: recommendations.append( "Improve data quality - implement validation rules at data entry points " "and automated quality monitoring." ) # Check for spreadsheet dependency spreadsheets = [s for s in self.sources.values() if s.source_type == DataSourceType.SPREADSHEET] if len(spreadsheets) > 3: recommendations.append( f"Reduce spreadsheet dependency - {len(spreadsheets)} spreadsheet-based " "data sources identified. Migrate critical data to proper databases." ) # Check for legacy systems legacy = [s for s in self.sources.values() if s.source_type == DataSourceType.LEGACY_SYSTEM] if legacy: recommendations.append( f"Plan legacy system migration - {len(legacy)} legacy systems identified. " "Create modernization roadmap." ) return recommendations

Quick Start

# Initialize auditor auditor = DataSourceAuditor() # Add known sources auditor.add_source(DataSource( name="Procore", source_type=DataSourceType.CLOUD_APP, domains=[DataDomain.DOCUMENT, DataDomain.FIELD, DataDomain.SCHEDULE], owner="Project Controls", department="Operations", description="Primary project management platform", technology="SaaS", location="cloud", access_method="API", update_frequency="real-time", data_volume="large", retention_period="7 years", integrations=["Sage 300", "Primavera P6"], is_master=True, master_for=["projects", "documents"] )) auditor.add_source(DataSource( name="Sage 300", source_type=DataSourceType.DATABASE, domains=[DataDomain.COST, DataDomain.ACCOUNTING], owner="Finance", department="Accounting", description="ERP and job costing system", technology="SQL Server", location="on-prem", access_method="ODBC", update_frequency="daily", data_volume="medium", retention_period="10 years", is_master=True, master_for=["costs", "vendors", "invoices"] )) # Add data flows auditor.add_flow(DataFlow( source="Procore", target="Sage 300", flow_type="push", frequency="daily", entities=["change_orders", "budget_changes"], transformation="simple", status="active" )) # Identify silos silos = auditor.identify_silos() # Generate report report = auditor.generate_audit_report() print(report) # Create data catalog catalog = auditor.create_data_catalog() catalog.to_excel("data_catalog.xlsx", index=False)

Survey Template

Use this survey to discover data sources across the organization: System Survey: - system_name: "What is the name of this system?" - type: "What type of system is it?" options: [database, api, file_share, cloud_app, spreadsheet, legacy_system] - domains: "What types of data does it contain?" options: [cost, schedule, bim, document, field, safety, quality, hr, accounting] - owner: "Who is the system owner?" - department: "Which department uses this system?" - technology: "What technology/platform is it built on?" - location: "Where is the system hosted?" options: [cloud, on-prem, hybrid] - access_method: "How can data be accessed?" options: [api, odbc, file_export, manual] - update_frequency: "How often is data updated?" options: [real-time, daily, weekly, monthly, ad-hoc] - integrations: "What other systems does it connect to?"

Resources

DAMA DMBOK: Data Management Body of Knowledge Data Governance Frameworks: DCAM, EDM Council Integration Patterns: Enterprise Integration Patterns book

Category context

Data access, storage, extraction, analysis, reporting, and insight generation.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

2 Docs1 Config

SKILL.md Primary doc
instructions.md Docs
claw.json Config