Agent skill · data analytics · datadrivenconstruction
historical-data-manager
Extract, clean, and organize legacy construction data from archives. Migrate historical project data, cost records, and schedules into modern formats.
Why this skill is useful
Provides specific data extraction and normalization methods for legacy construction data that the AI wouldn't reliably generate on its own.
What it needs
Requires python3 installed locally. About 6k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
Historical Data Manager for Construction Overview Manage legacy construction data from archives, old systems, and historical records. Extract, clean, normalize, and migrate data into modern formats for analysis and benchmarking. Business Case Construction companies accumulate decades of project data in various formats: Paper records scanned to PDF Legacy database exports (Access, dBase, FoxPro) Old spreadsheet formats (Lotus 1-2-3, early Excel) Proprietary software exports Project closeout documentation This skill helps extract value from historical data for: Cost benchmarking and trending Productivity analysis over time Risk pattern identification Estimating improvement Technical Implementation Historical Data Extractor Legacy System Connectors Quick Start Common Use Cases 1. Cost Benchmarking: Normalize historical costs for comparison 2. Productivity Analysis: Track labor productivity over time 3. Risk Identification: Find patterns in historical project issues 4. Estimating Calibration: Improve estimates with historical data Dependencies Resources RSMeans Historical Cost Index: For cost escalation ENR Construction Cost Index: Alternative escalation source Legacy Format Documentation: Vendor-specific export formats
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/historical-data-manager