labor-productivity-analytics
Analyze construction labor productivity using data analytics. Track worker performance, identify inefficiencies, predict resource needs, and optimize crew allocation for maximum efficiency.
npx skills add majiayu000/claude-skill-registry --skill labor-productivity-analytics --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Provides data-driven labor productivity analysis for construction projects, including tracking productivity metrics, time-series analysis, crew optimization, resource forecasting, benchmarking, and variance analysis.
How it works
- Defines data structures for work logs, metrics, workers, crews, and daily production records.
- Calculates productivity metrics per activity by aggregating hours and quantity, computing unit rates, hours per unit, and variance from standards.
- Includes a Comprehensive Productivity System with modules for data collection/management and a Productivity Database that stores workers, crews, records, and standards.
- Offers a Productivity Analysis Engine with methods to compute earned value metrics, analyze by activity and by crew, identify trends, assess weather impact, and surface inefficiencies.
- Implements a Resource Forecasting module that can forecast hours based on historical data or standards and can project crew needs for planned work.
- Exposes example code for logging production, setting standards, and performing analyses (e.g., calculate_productivity, get_records_df, analyze_trends).
When to use it
Use when you need to quantify labor productivity, benchmark performance against standards, detect inefficiencies, forecast resource needs, and inform crew allocations for construction projects.
What it can touch
- Tools: claude-code (as declared)
- Data entities: Worker, Crew, DailyProductionRecord, ProductivityDatabase, ProductivityAnalyzer, ResourceForecaster
- Data inputs: work logs, activity codes, quantities, hours, weather, dates, standards
Caveats
- License: MIT
- The skill relies on provided data structures and assumes non-empty records for most analyses; some methods return empty results if data is missing.
- Standard and activity mappings are user-supplied; misconfigured standards may affect metrics.
# Labor Productivity Analytics ## Overview This skill implements data-driven labor productivity analysis for construction projects. Track, measure, and optimize workforce performance to improve project efficiency and reduce costs. **Capabilities:** - Productivity metrics calculation - Time series analysis - Crew optimization - Resource forecasting - Benchmarking - Variance analysis ## Quick Start ```python from dataclasses import dataclass, field from datetime import date, datetime, timedelta from typing import List, Dict, Optional, Tuple from enum import Enum import numpy as np @dataclass class WorkLog: worker_id: str work_date: date activity_code: str hours_worked: float quantity_completed: float unit: str crew_id: str weather: str = "normal" notes: str = "" @dataclass class ProductivityMetric: activity: str unit_rate: float # units per hour hours_per_unit: float # hours per unit standard_rate: float # benchmark variance_pct: float def calculate_productivity(logs: List[WorkLog], standard_rates: Dict[str, float]) -> List[ProductivityMetric]: """Calculate productivity metrics from work logs""" # Group by activity by_activity = {} for log in logs: if log.activity_code not in by_acti
- Overview
- Quick Start
- Comprehensive Productivity System
- Data Collection and Management
- Productivity Analysis Engine
- Resource Forecasting
- Reporting and Visualization
- Quick Reference
- Resources
- Next Steps
What does the labor-productivity-analytics skill do?
Analyze construction labor productivity using data analytics. Track worker performance, identify inefficiencies, predict resource needs, and optimize crew allocation for maximum efficiency.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill labor-productivity-analytics --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.