labor-productivity-analyzer について
労働生産性を分析するスキルで、Pythonの実装とクイックスタート例が示されています。プロジェクトの生産目標設定と日付付きエントリーの記録が可能です。
主な機能
- 01生産性目標の設定
- 02日付付きエントリーの記録
- 03労働生産性の分析
活用シーン
- 01作業項目のプロジェクト目標設定
- 02当日の労働エントリーの記録
インストール
コマンドをコピーしてターミナルで実行します。
$
npx skills add https:/ / github.com/ datadrivenconstruction/ ddc_skills_for_ai_agents_in_construction --skill labor-productivity-analyzerインストール後、Claude Code で/labor-productivity-analyzerを入力して読み込まれたか確認します。
同梱ファイルclaw.jsoninstructions.md
README(原文)English
プロジェクトの README よりこの README の日本語訳はまだありません。以下は英語の原文です。日本語の紹介はページ上部をご覧ください。
Labor Productivity Analyzer
Technical Implementation
import pandas as pd
from datetime import date
from typing import Dict, Any, List
from dataclasses import dataclass, field
from enum import Enum
class ProductivityStatus(Enum):
EXCEEDING = "exceeding"
ON_TARGET = "on_target"
BELOW_TARGET = "below_target"
CRITICAL = "critical"
@dataclass
class ProductivityEntry:
entry_id: str
date: date
trade: str
activity_code: str
activity_description: str
location: str
crew_size: int
hours_worked: float
quantity_installed: float
unit: str
target_productivity: float # units per hour
@property
def actual_productivity(self) -> float:
if self.hours_worked == 0:
return 0
return self.quantity_installed / self.hours_worked
@property
def productivity_factor(self) -> float:
if self.target_productivity == 0:
return 0
return self.actual_productivity / self.target_productivity
@property
def status(self) -> ProductivityStatus:
pf = self.productivity_factor
if pf >= 1.1:
return ProductivityStatus.EXCEEDING
elif pf >= 0.9:
return ProductivityStatus.ON_TARGET
elif pf >= 0.7:
return ProductivityStatus.BELOW_TARGET
return ProductivityStatus.CRITICAL
class LaborProductivityAnalyzer:
def __init__(self, project_name: str):
self.project_name = project_name
self.entries: List[ProductivityEntry] = []
self.targets: Dict[str, float] = {} # activity_code: target_productivity
self._counter = 0
def set_target(self, activity_code: str, target_productivity: float):
self.targets[activity_code] = target_productivity
def add_entry(self, entry_date: date, trade: str, activity_code: str,
activity_description: str, location: str, crew_size: int,
hours_worked: float, quantity_installed: float,
unit: str) -> ProductivityEntry:
self._counter += 1
entry_id = f"PROD-{self._counter:05d}"
target = self.targets.get(activity_code, 1.0)
entry = ProductivityEntry(
entry_id=entry_id,
date=entry_date,
trade=trade,
activity_code=activity_code,
activity_description=activity_description,
location=location,
crew_size=crew_size,
hours_worked=hours_worked,
quantity_installed=quantity_installed,
unit=unit,
target_productivity=target
)
self.entries.append(entry)
return entry
def get_productivity_by_trade(self) -> Dict[str, Dict[str, Any]]:
by_trade = {}
for entry in self.entries:
if entry.trade not in by_trade:
by_trade[entry.trade] = {'hours': 0, 'quantity': 0, 'entries': 0}
by_trade[entry.trade]['hours'] += entry.hours_worked
by_trade[entry.trade]['quantity'] += entry.quantity_installed
by_trade[entry.trade]['entries'] += 1
for trade in by_trade:
hours = by_trade[trade]['hours']
qty = by_trade[trade]['quantity']
by_trade[trade]['avg_productivity'] = qty / hours if hours > 0 else 0
return by_trade
def get_productivity_by_activity(self) -> Dict[str, Dict[str, Any]]:
by_activity = {}
for entry in self.entries:
code = entry.activity_code
if code not in by_activity:
by_activity[code] = {
'description': entry.activity_description,
'hours': 0, 'quantity': 0, 'target': entry.target_productivity
}
by_activity[code]['hours'] += entry.hours_worked
by_activity[code]['quantity'] += entry.quantity_installed
for code in by_activity:
hours = by_activity[code]['hours']
qty = by_activity[code]['quantity']
by_activity[code]['actual'] = qty / hours if hours > 0 else 0
by_activity[code]['factor'] = (
by_activity[code]['actual'] / by_activity[code]['target']
if by_activity[code]['target'] > 0 else 0
)
return by_activity
def get_low_performers(self) -> List[ProductivityEntry]:
return [e for e in self.entries
if e.status in [ProductivityStatus.BELOW_TARGET, ProductivityStatus.CRITICAL]]
def get_summary(self) -> Dict[str, Any]:
if not self.entries:
return {'total_entries': 0}
total_hours = sum(e.hours_worked for e in self.entries)
factors = [e.productivity_factor for e in self.entries]
avg_factor = sum(factors) / len(factors)
return {
'total_entries': len(self.entries),
'total_hours': total_hours,
'average_productivity_factor': round(avg_factor, 2),
'exceeding': sum(1 for e in self.entries if e.status == ProductivityStatus.EXCEEDING),
'on_target': sum(1 for e in self.entries if e.status == ProductivityStatus.ON_TARGET),
'below_target': sum(1 for e in self.entries if e.status == ProductivityStatus.BELOW_TARGET),
'critical': sum(1 for e in self.entries if e.status == ProductivityStatus.CRITICAL)
}
def export_report(self, output_path: str):
data = [{
'Date': e.date,
'Trade': e.trade,
'Activity': e.activity_code,
'Location': e.location,
'Crew': e.crew_size,
'Hours': e.hours_worked,
'Quantity': e.quantity_installed,
'Unit': e.unit,
'Target': e.target_productivity,
'Actual': round(e.actual_productivity, 2),
'Factor': round(e.productivity_factor, 2),
'Status': e.status.value
} for e in self.entries]
pd.DataFrame(data).to_excel(output_path, index=False)
Quick Start
analyzer = LaborProductivityAnalyzer("Office Tower")
# Set targets
analyzer.set_target("CONC-001", 2.5) # m3 per hour
# Add entry
entry = analyzer.add_entry(
entry_date=date.today(),
trade="Concrete",
activity_code="CONC-001",
activity_description="Pour concrete slab",
location="Level 3",
crew_size=8,
hours_worked=80,
quantity_installed=180,
unit="m3"
)
print(f"Productivity factor: {entry.productivity_factor:.2f}")
print(f"Status: {entry.status.value}")
Resources
- DDC Book: Chapter 3.2 - Resource Management
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