Agent skill · data analytics · datadrivenconstruction
cwicr-productivity-tracker
Track actual vs planned productivity using CWICR norms. Calculate productivity rates, identify variances, and generate performance reports.
Why this skill is useful
Provides specific productivity tracking methods and variance analysis that the AI wouldn't reliably generate on its own.
What it needs
Requires python3 installed locally. About 5k tokens when loaded. Last updated 2026-02-14. 265 stars on the source repository.
What this skill does
CWICR Productivity Tracker Business Case Problem Statement Project performance tracking requires: Comparing actual vs planned productivity Identifying underperforming activities Forecasting completion dates Learning from historical data Solution Track productivity by comparing actual hours/quantities against CWICR norms, generating variance analysis and forecasts. Business Value Performance visibility - Real-time productivity metrics Early warning - Identify issues before escalation Continuous improvement - Learn from variances Accurate forecasting - Data-driven predictions Technical Implementation Quick Start Common Use Cases 1. Variance Analysis 2. Completion Forecast 3. Export Report Resources GitHub: OpenConstructionEstimate-DDC-CWICR DDC Book: Chapter 3.1 - Productivity Management
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills datadrivenconstruction/cwicr-productivity-tracker