Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Cwicr Cost Calculator
Calculate construction costs using DDC CWICR resource-based methodology. Break down costs into labor, materials, equipment with transparent pricing.
skill openclawclawhub Free
Calculate construction costs using DDC CWICR resource-based methodology. Break down costs into labor, materials, equipment with transparent pricing.
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- Download the package from Yavira.
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- Tencent SkillHub
- What's included
- claw.json, instructions.md, SKILL.md
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Trust & source
Release facts
- Source
- Tencent SkillHub
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- Indexed source record
- Version
- 2.1.0
Provenance
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- datadrivenconstruction
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Documentation
Problem Statement
Traditional cost estimation often produces "black box" estimates with hidden markups. Stakeholders need: Transparent cost breakdowns Traceable pricing logic Auditable calculations Resource-level detail
Solution
Resource-based cost calculation using CWICR methodology that separates physical norms (labor hours, material quantities) from volatile prices, enabling transparent and auditable estimates.
Business Value
Full transparency - Every cost component visible Auditable - Traceable calculation logic Flexible - Update prices without changing norms Accurate - Based on 55,000+ validated work items
Prerequisites
pip install pandas numpy
Python Implementation
import pandas as pd import numpy as np from typing import Dict, Any, List, Optional, Tuple from dataclasses import dataclass, field from enum import Enum from datetime import datetime class CostComponent(Enum): """Cost breakdown components.""" LABOR = "labor" MATERIAL = "material" EQUIPMENT = "equipment" OVERHEAD = "overhead" PROFIT = "profit" TOTAL = "total" class CostStatus(Enum): """Cost calculation status.""" CALCULATED = "calculated" ESTIMATED = "estimated" MISSING_DATA = "missing_data" ERROR = "error" @dataclass class CostBreakdown: """Detailed cost breakdown for a work item.""" work_item_code: str description: str unit: str quantity: float labor_cost: float = 0.0 material_cost: float = 0.0 equipment_cost: float = 0.0 overhead_cost: float = 0.0 profit_cost: float = 0.0 unit_price: float = 0.0 total_cost: float = 0.0 labor_hours: float = 0.0 labor_rate: float = 0.0 resources: List[Dict[str, Any]] = field(default_factory=list) status: CostStatus = CostStatus.CALCULATED def to_dict(self) -> Dict[str, Any]: return { 'work_item_code': self.work_item_code, 'description': self.description, 'unit': self.unit, 'quantity': self.quantity, 'labor_cost': self.labor_cost, 'material_cost': self.material_cost, 'equipment_cost': self.equipment_cost, 'overhead_cost': self.overhead_cost, 'profit_cost': self.profit_cost, 'total_cost': self.total_cost, 'status': self.status.value } @dataclass class CostSummary: """Summary of cost estimate.""" total_cost: float labor_total: float material_total: float equipment_total: float overhead_total: float profit_total: float item_count: int currency: str calculated_at: datetime breakdown_by_category: Dict[str, float] = field(default_factory=dict) class CWICRCostCalculator: """Resource-based cost calculator using CWICR methodology.""" DEFAULT_OVERHEAD_RATE = 0.15 # 15% overhead DEFAULT_PROFIT_RATE = 0.10 # 10% profit def __init__(self, cwicr_data: pd.DataFrame, overhead_rate: float = None, profit_rate: float = None, currency: str = "USD"): """Initialize calculator with CWICR data.""" self.data = cwicr_data self.overhead_rate = overhead_rate or self.DEFAULT_OVERHEAD_RATE self.profit_rate = profit_rate or self.DEFAULT_PROFIT_RATE self.currency = currency # Index data for fast lookup self._index_data() def _index_data(self): """Create index for fast work item lookup.""" if 'work_item_code' in self.data.columns: self._code_index = self.data.set_index('work_item_code') else: self._code_index = None def calculate_item_cost(self, work_item_code: str, quantity: float, price_overrides: Dict[str, float] = None) -> CostBreakdown: """Calculate cost for single work item.""" # Find work item in database if self._code_index is not None and work_item_code in self._code_index.index: item = self._code_index.loc[work_item_code] else: # Try partial match matches = self.data[ self.data['work_item_code'].str.contains(work_item_code, case=False, na=False) ] if matches.empty: return CostBreakdown( work_item_code=work_item_code, description="NOT FOUND", unit="", quantity=quantity, status=CostStatus.MISSING_DATA ) item = matches.iloc[0] # Get base costs labor_unit = float(item.get('labor_cost', 0) or 0) material_unit = float(item.get('material_cost', 0) or 0) equipment_unit = float(item.get('equipment_cost', 0) or 0) # Apply price overrides if provided if price_overrides: if 'labor_rate' in price_overrides: labor_norm = float(item.get('labor_norm', 0) or 0) labor_unit = labor_norm * price_overrides['labor_rate'] if 'material_factor' in price_overrides: material_unit *= price_overrides['material_factor'] if 'equipment_factor' in price_overrides: equipment_unit *= price_overrides['equipment_factor'] # Calculate component costs labor_cost = labor_unit * quantity material_cost = material_unit * quantity equipment_cost = equipment_unit * quantity # Direct costs direct_cost = labor_cost + material_cost + equipment_cost # Overhead and profit overhead_cost = direct_cost * self.overhead_rate profit_cost = (direct_cost + overhead_cost) * self.profit_rate # Total total_cost = direct_cost + overhead_cost + profit_cost # Unit price unit_price = total_cost / quantity if quantity > 0 else 0 return CostBreakdown( work_item_code=work_item_code, description=str(item.get('description', '')), unit=str(item.get('unit', '')), quantity=quantity, labor_cost=labor_cost, material_cost=material_cost, equipment_cost=equipment_cost, overhead_cost=overhead_cost, profit_cost=profit_cost, unit_price=unit_price, total_cost=total_cost, labor_hours=float(item.get('labor_norm', 0) or 0) * quantity, labor_rate=float(item.get('labor_rate', 0) or 0), status=CostStatus.CALCULATED ) def calculate_estimate(self, items: List[Dict[str, Any]], group_by_category: bool = True) -> CostSummary: """Calculate cost estimate for multiple items.""" breakdowns = [] for item in items: code = item.get('work_item_code') or item.get('code') qty = item.get('quantity', 0) overrides = item.get('price_overrides') breakdown = self.calculate_item_cost(code, qty, overrides) breakdowns.append(breakdown) # Aggregate totals labor_total = sum(b.labor_cost for b in breakdowns) material_total = sum(b.material_cost for b in breakdowns) equipment_total = sum(b.equipment_cost for b in breakdowns) overhead_total = sum(b.overhead_cost for b in breakdowns) profit_total = sum(b.profit_cost for b in breakdowns) total_cost = sum(b.total_cost for b in breakdowns) # Group by category if requested breakdown_by_category = {} if group_by_category: for b in breakdowns: # Extract category from work item code prefix category = b.work_item_code.split('-')[0] if '-' in b.work_item_code else 'Other' if category not in breakdown_by_category: breakdown_by_category[category] = 0 breakdown_by_category[category] += b.total_cost return CostSummary( total_cost=total_cost, labor_total=labor_total, material_total=material_total, equipment_total=equipment_total, overhead_total=overhead_total, profit_total=profit_total, item_count=len(breakdowns), currency=self.currency, calculated_at=datetime.now(), breakdown_by_category=breakdown_by_category ) def calculate_from_qto(self, qto_df: pd.DataFrame, code_column: str = 'work_item_code', quantity_column: str = 'quantity') -> pd.DataFrame: """Calculate costs from Quantity Takeoff DataFrame.""" results = [] for _, row in qto_df.iterrows(): code = row[code_column] qty = row[quantity_column] breakdown = self.calculate_item_cost(code, qty) result = breakdown.to_dict() # Add original QTO columns for col in qto_df.columns: if col not in result: result[f'qto_{col}'] = row[col] results.append(result) return pd.DataFrame(results) def apply_regional_factors(self, base_costs: pd.DataFrame, region_factors: Dict[str, float]) -> pd.DataFrame: """Apply regional adjustment factors.""" adjusted = base_costs.copy() if 'labor_cost' in adjusted.columns and 'labor' in region_factors: adjusted['labor_cost'] *= region_factors['labor'] if 'material_cost' in adjusted.columns and 'material' in region_factors: adjusted['material_cost'] *= region_factors['material'] if 'equipment_cost' in adjusted.columns and 'equipment' in region_factors: adjusted['equipment_cost'] *= region_factors['equipment'] # Recalculate totals adjusted['direct_cost'] = ( adjusted.get('labor_cost', 0) + adjusted.get('material_cost', 0) + adjusted.get('equipment_cost', 0) ) adjusted['total_cost'] = adjusted['direct_cost'] * (1 + self.overhead_rate) * (1 + self.profit_rate) return adjusted def compare_estimates(self, estimate1: CostSummary, estimate2: CostSummary) -> Dict[str, Any]: """Compare two cost estimates.""" return { 'total_difference': estimate2.total_cost - estimate1.total_cost, 'total_percent_change': ( (estimate2.total_cost - estimate1.total_cost) / estimate1.total_cost * 100 if estimate1.total_cost > 0 else 0 ), 'labor_difference': estimate2.labor_total - estimate1.labor_total, 'material_difference': estimate2.material_total - estimate1.material_total, 'equipment_difference': estimate2.equipment_total - estimate1.equipment_total, 'item_count_difference': estimate2.item_count - estimate1.item_count } class CostReportGenerator: """Generate cost reports from calculations.""" def __init__(self, calculator: CWICRCostCalculator): self.calculator = calculator def generate_summary_report(self, items: List[Dict[str, Any]]) -> Dict[str, Any]: """Generate summary cost report.""" summary = self.calculator.calculate_estimate(items) return { 'report_date': datetime.now().isoformat(), 'currency': summary.currency, 'total_cost': round(summary.total_cost, 2), 'breakdown': { 'labor': round(summary.labor_total, 2), 'material': round(summary.material_total, 2), 'equipment': round(summary.equipment_total, 2), 'overhead': round(summary.overhead_total, 2), 'profit': round(summary.profit_total, 2) }, 'percentages': { 'labor': round(summary.labor_total / summary.total_cost * 100, 1) if summary.total_cost > 0 else 0, 'material': round(summary.material_total / summary.total_cost * 100, 1) if summary.total_cost > 0 else 0, 'equipment': round(summary.equipment_total / summary.total_cost * 100, 1) if summary.total_cost > 0 else 0, }, 'item_count': summary.item_count, 'by_category': summary.breakdown_by_category } def generate_detailed_report(self, items: List[Dict[str, Any]]) -> pd.DataFrame: """Generate detailed line-item report.""" results = [] for item in items: code = item.get('work_item_code') or item.get('code') qty = item.get('quantity', 0) breakdown = self.calculator.calculate_item_cost(code, qty) results.append(breakdown.to_dict()) df = pd.DataFrame(results) # Add totals row totals = df[['labor_cost', 'material_cost', 'equipment_cost', 'overhead_cost', 'profit_cost', 'total_cost']].sum() totals['description'] = 'TOTAL' totals['work_item_code'] = '' df = pd.concat([df, pd.DataFrame([totals])], ignore_index=True) return df # Convenience functions def calculate_cost(cwicr_data: pd.DataFrame, work_item_code: str, quantity: float) -> float: """Quick cost calculation.""" calc = CWICRCostCalculator(cwicr_data) breakdown = calc.calculate_item_cost(work_item_code, quantity) return breakdown.total_cost def estimate_project_cost(cwicr_data: pd.DataFrame, items: List[Dict[str, Any]]) -> Dict[str, Any]: """Quick project cost estimate.""" calc = CWICRCostCalculator(cwicr_data) report = CostReportGenerator(calc) return report.generate_summary_report(items)
Quick Start
import pandas as pd from cwicr_data_loader import CWICRDataLoader # Load CWICR data loader = CWICRDataLoader() cwicr = loader.load("ddc_cwicr_en.parquet") # Initialize calculator calc = CWICRCostCalculator(cwicr) # Calculate single item breakdown = calc.calculate_item_cost("CONC-001", quantity=150) print(f"Total: ${breakdown.total_cost:,.2f}") print(f" Labor: ${breakdown.labor_cost:,.2f}") print(f" Material: ${breakdown.material_cost:,.2f}") print(f" Equipment: ${breakdown.equipment_cost:,.2f}")
1. Project Estimate
items = [ {'work_item_code': 'CONC-001', 'quantity': 150}, {'work_item_code': 'EXCV-002', 'quantity': 200}, {'work_item_code': 'REBAR-003', 'quantity': 15000} # kg ] summary = calc.calculate_estimate(items) print(f"Project Total: ${summary.total_cost:,.2f}")
2. QTO Integration
# Load BIM quantities qto = pd.read_excel("quantities.xlsx") # Calculate costs costs = calc.calculate_from_qto(qto, code_column='work_item', quantity_column='quantity' ) print(costs[['description', 'quantity', 'total_cost']])
3. Regional Adjustment
# Apply Berlin pricing berlin_factors = { 'labor': 1.15, # 15% higher labor 'material': 0.95, # 5% lower materials 'equipment': 1.0 } adjusted = calc.apply_regional_factors(costs, berlin_factors)
Resources
GitHub: OpenConstructionEstimate-DDC-CWICR DDC Book: Chapter 3.1 - Construction Cost Estimation
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs1 Config
- SKILL.md
- instructions.md
- claw.json