A comprehensive auditing tool to map, analyze, and optimize data flows and systems within construction organizations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install data-source-audit
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install data-source-audit using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Data Source Audit for Construction?
This skill provides a vendor-agnostic framework for performing deep-dive audits of construction data environments. It helps organizations transition toward data-driven construction by identifying fragmented data silos and mapping complex inter-system dependencies across project management, ERP, and BIM platforms.
Using Openclaw Skills like this enables technical teams to assess data quality metrics—including completeness and accuracy—while generating a clear integration roadmap. By formalizing the relationship between project controls, accounting, and field apps, the auditor creates a technical foundation for digital transformation initiatives.
Data Source Audit for Construction Use Cases
- Mapping data flows between project management software and accounting ERPs to ensure financial alignment.
- Identifying isolated data silos that require high-effort manual entry or lack modern API access.
- Evaluating the quality, validity, and completeness of data across diverse construction platforms.
- Designing a Master Data Management (MDM) strategy by designating authoritative sources for projects and vendors.
- Auditing legacy systems to plan for modernization roadmaps or cloud migration strategies.
How Data Source Audit for Construction Works
- Register known data sources by defining their technology stack, location (cloud/on-prem), and update frequency.
- Define data flows between registered systems, specifying directionality (push/pull/bidirectional) and data entities.
- Execute silo identification logic to detect systems without active integrations or overlapping domains without a master source.
- Perform automated data quality assessments using sample datasets to calculate scores for completeness and validity.
- Generate a detailed Audit Report featuring an Integration Maturity Score and actionable recommendations.
Data Source Audit for Construction Setup
This skill requires Python 3 and the pandas library for data processing.
pip install pandas
Ensure your environment is configured to run Python-based Openclaw Skills. The tool is compatible with Darwin, Linux, and Win32 operating systems.
Data Source Audit for Construction Data Schema & Taxonomy
The skill organizes construction metadata into a structured taxonomy to facilitate clear reporting and analysis:
- Data Sources: Detailed objects capturing system types (SaaS, Database, Spreadsheet), ownership, and technical access methods (API, ODBC).
- Data Flows: A registry of system-to-system connections including frequency, transformation logic, and active status.
- Data Silos: A log of isolated data clusters categorized by business impact and resolution priority.
| Entity | Key Attributes |
|---|---|
| DataSource | Name, Type, Domains (Cost/BIM/HR), Owner, Quality Scores |
| DataFlow | Source, Target, Flow Type, Entities, Frequency |
| DataSilo | Impact Level, Member Sources, Resolution Options |
Data Source Audit for Construction Advanced Features
- Automated Integration Maturity Scoring based on coverage, master data health, and silo risk.
- Survey ingestion module to programmatically discover data sources from stakeholder feedback.
- Automated generation of visual integration matrices and Excel-ready data catalogs.
- Built-in quality monitoring for common data formats including date validation and email verification.
- Support for identifying 'no master' conflicts in overlapping data domains like Cost or Schedule.
SKILL.md
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METADATA
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