Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill cwicr-cost-calculatorgit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-cost-calculator)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-cost-calculator"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-cost-calculator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-cost-calculator"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-cost-calculator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.03589 |
| Opus 5 | $0.00016 | $0.01795 |
| Sonnet 5 | $0.00007 | $0.00718 |
| Haiku 4.5 | $0.00003 | $0.00359 |
Grade A, and why
cwicr-cost-calculator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- cwicr-cost-calculator — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 461 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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(),
'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)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 461 lines · 33 tokens per session scan A 25042a589cd5
cwicr-cost-calculator is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (310 stars, last pushed 21d ago), licensed MIT. It adds 33 tokens to every session and 3,589 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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