productivity-analyzer
Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.
npx skills add majiayu000/claude-skill-registry --skill productivity-analyzer-datadrivenconstructi-ddc-skills-for-ai-ag --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.
# Productivity Analyzer ## Business Case ### Problem Statement Understanding productivity requires: - Tracking actual output rates - Comparing to planned rates - Identifying problem areas - Forecasting project completion ### Solution Analyze labor productivity data to identify trends, compare to benchmarks, and provide actionable insights. ## Technical Implementation ```python import pandas as pd import numpy as np from typing import Dict, Any, List, Optional from dataclasses import dataclass from datetime import date, timedelta from enum import Enum class ProductivityStatus(Enum): EXCELLENT = "excellent" # >110% of planned ON_TARGET = "on_target" # 90-110% BELOW = "below" # 70-90% CRITICAL = "critical" # <70% @dataclass class ProductivityRecord: date: date activity_code: str description: str planned_output: float actual_output: float unit: str manhours: float crew_size: int conditions: str # weather, access issues @dataclass class ProductivityAnalysis: activity_code: str description: str total_planned: float total_actual: float total_manhours: float planned_rate: float # unit per manhour actual_rate: float efficiency: float # percentage status: ProductivityStatus trend: str # impr
- Business Case
- Problem Statement
- Solution
- Technical Implementation
- Quick Start
- Common Use Cases
- 1. Identify Problems
- 2. Forecast Completion
- 3. Compare to Benchmarks
- Resources
What does the productivity-analyzer skill do?
Analyze labor productivity from site data. Compare planned vs actual, identify trends, benchmark against industry standards.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill productivity-analyzer-datadrivenconstructi-ddc-skills-for-ai-ag --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.