cwicr-cost-calculator
Calculate construction costs using DDC CWICR resource-based methodology. Break down costs into labor, materials, equipment with transparent pricing.
Install / Use
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill cwicr-cost-calculatorInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Development & EngineeringSupported Platforms
Our assessment of cwicr-cost-calculator
cwicr-cost-calculator scores 91/100 on our quality scale, 1207th of 4,644 Development & Engineering skills we index (top 26%).
Its SKILL.md is 16 KB long, well organised into 21 sections with 6 code examples: a thorough specification that gives an agent plenty to work with.
It has 333 GitHub stars, a meaningful sign that others use it.
Maintenance, license and trust
- The repository was last updated 44 days ago, so cwicr-cost-calculator is actively maintained.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
cwicr-cost-calculator compared with similar skills
All 4 of these similar skills score higher than cwicr-cost-calculator; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| cwicr-cost-calculator (this skill)by datadrivenconstruction | 91 | 333 | 44d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 91.2k | 19d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.4k | today | CLAUDE.md |
| ai-job-searchby MadsLorentzen | 100 | 45.0k | 1d ago | CLAUDE.md |
| claude-howtoby luongnv89 | 100 | 41.7k | 5d ago | CLAUDE.md |
Frequently asked questions
- How do I install cwicr-cost-calculator?
- Run
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill cwicr-cost-calculator. The install tabs above show the steps for each supported agent. - Which AI agents does cwicr-cost-calculator work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is cwicr-cost-calculator safe to use?
- It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is cwicr-cost-calculator still maintained?
- The repository was last updated 44 days ago, so cwicr-cost-calculator is actively maintained.
Skill content
View source on GitHubname: "cwicr-cost-calculator" description: "Calculate construction costs using DDC CWICR resource-based methodology. Break down costs into labor, materials, equipment with transparent pricing." homepage: "https://datadrivenconstruction.io" metadata: {"openclaw":{"emoji":"💰","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"]}}}
CWICR Cost Calculator
Business Case
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
Technical Implementation
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(),
Truncated for display — read the full file on GitHub.
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From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
