A specialized tool for extracting, cleaning, and normalizing legacy construction data from archived formats like old spreadsheets and database exports.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install historical-data-manager
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 historical-data-manager 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 Historical Data Manager?
The Historical Data Manager is an essential tool for construction firms looking to unlock the latent value trapped in decades of archived project data. This component of the Openclaw Skills library enables teams to move beyond static PDFs and obsolete file formats by programmatically extracting cost, schedule, and labor data into clean, actionable datasets. By bridging the gap between legacy systems and modern analytics, it transforms fragmented historical records into a strategic asset for future bidding and project planning.
Historical Data Manager Use Cases
- Cost Benchmarking to compare historical project expenses against current market rates using cost indices.
- Productivity Analysis to track labor efficiency trends across multiple decades of project performance.
- Estimating Calibration by using real-world historical results to refine future bid accuracy.
- Risk Pattern Identification to uncover recurring issues in project closeout documentation.
How Historical Data Manager Works
- Scan the archive directory to identify file types including legacy Excel, CSV, and database exports.
- Detect data categories such as cost, labor, or schedule using pattern-matching on column headers.
- Normalize column names and data structures to ensure consistency across disparate data sources.
- Apply cost escalation factors using indices like RSMeans to adjust historical dollars to current value.
- Generate a comprehensive migration report assessing the quality and completeness of the extracted data.
Historical Data Manager Setup
Install the necessary Python dependencies for handling legacy formats as part of your Openclaw Skills deployment:
pip install pandas openpyxl xlrd pyodbc
Ensure you have Python 3.x installed and the Microsoft Access Driver available if processing legacy MDB files.
Historical Data Manager Data Schema & Taxonomy
The skill organizes data into a structured HistoricalRecord schema for consistent analysis:
| Field | Type | Description |
|---|---|---|
| project_id | String | Unique identifier derived from data or file path |
| year | Integer | The original year of the project data |
| data_type | String | Category: cost, schedule, labor, or material |
| quality_score | Float | A 0.0-1.0 assessment of data completeness |
| normalized_cost | Float | Cost adjusted for inflation/escalation |
Historical Data Manager Advanced Features
- Multi-engine Excel extraction supporting both modern .xlsx and legacy .xls formats via openpyxl and xlrd.
- Automated cost normalization using integrated historical cost indices for accurate dollar value adjustment.
- Native support for Primavera P6 XER file parsing to recover legacy schedule logic and activities.
- Heuristic-based data quality scoring to flag low-confidence records for manual review during migration.
SKILL.md
Loading
Related Openclaw Skills
Historical Cost Analyzer
A data-driven analytics tool for benchmarking construction costs and tracking historical escalation patterns.
Estimate Builder
A Python-powered construction estimate engine for generating detailed cost breakdowns with labor, materials, and automated markups.
ERP Integration Analysis
A technical framework for analyzing, mapping, and optimizing data integrations between ERP modules and external construction management systems.
DWG to Excel Data Converter
A professional utility for extracting structured CAD data, including layers, blocks, and geometry, from DWG files into Excel spreadsheets without requiring AutoCAD.
IFC to Excel Converter
Convert Industry Foundation Classes (IFC) files into structured Excel databases and 3D Collada geometry for accessible BIM data analysis.
Image To Data
A sophisticated AI vision skill that extracts structured data, safety metrics, and progress measurements from construction photos and drawings.
METADATA
Featured*
