Agent skill · data analytics · datadrivenconstruction
data-source-audit
Comprehensive audit of all construction data sources and systems. Map data flows, identify silos, assess quality, and create integration roadmap.
Why this skill is useful
Provides a structured framework for auditing construction data sources, including specific data flow mappings and quality assessment metrics that the AI wouldn't generate on its own.
What it needs
Requires python3 installed locally. About 7k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
Data Source Audit for Construction 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 Quick Start Survey Template Use this survey to discover data sources across the organization: Resources DAMA DMBOK: Data Management Body of Knowledge Data Governance Frameworks: DCAM, EDM Council Integration Patterns: Enterprise Integration Patterns book
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/data-source-audit