cwicr-cost-calculator

cwicr-cost-calculator is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 33 tokens per session (3,589 once invoked), scanned A, original, MIT.

A construction-cost calculator based on the DDC CWICR resource method. It separates the amount of labor, materials, and equipment needed from their prices, producing a visible cost breakdown.

In plain words
What is it for?
Use it to estimate construction work, break totals into labor, materials, equipment, overhead, and profit, and review the calculation logic with Python data tools.
Why use it?
It replaces unexplained total estimates with calculations that can be traced and audited. Prices can be updated without changing the underlying resource quantities and work norms.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to estimate construction work, break totals into labor, materials, equipment, overhead, and profit, and review the calculation logic with Python data tools.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-cost-calculator
Install

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.

Any agent
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill cwicr-cost-calculator
Clone the repo
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for cwicr-cost-calculator

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-cost-calculator/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-cost-calculator)
Your own site
<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.

agentmods 80×15 button for cwicr-cost-calculator

Your own site · 80×15
<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>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,589 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00033 $0.03589
Opus 5.5 $0.00013 $0.01436
Sonnet 5.5 $0.00007 $0.00718
Haiku 4.5 $0.00003 $0.00359

Measured 1mo ago against content hash 25042a589cd5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

1_DDC_Toolkit/CWICR-Database/cwicr-cost-calculator/SKILL.md · 461 lines

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)

Read the full file on GitHub · 461 lines

Files

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.

Changes

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.

  1. 1mo ago First seen · 461 lines · 33 tokens per session scan A 25042a589cd5

Subscribe to this mod's changes

cwicr-cost-calculator is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (373 stars, last pushed 8d ago), licensed MIT. It adds 33 tokens to every session and 3,589 once invoked, about $0.0001 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

agent-era-pricing

Redesign seat-based pricing for the agent era — when one human runs ten agents, per-seat models collapse. Use when agents are eroding seat counts, when asked to migrate to usage- or outcome-based pricing, to price an agent/API tier, or to defend revenue as customers automate their own usage. Produces a pricing…

mohitagw15856/pm-claude-skills · 108 tokens

invest-backup

Back up / restore openInvest's local state — memory/ (holdings, strategy, user profile, committee records, dream logs) + db/ (trade ledger, job run history, market-data cache) + .env (SMTP/API credentials) + userprofile.json. All of this data is .gitignore'd with no historical versions in git, so a single accidental…

longsizhuo/openInvest · 202 tokens

job-profitability-analyzer

Find out which clients and jobs actually make money -- allocate real costs (labor hours, materials, subs, overhead) against what each job billed, and rank every client by true margin. Most service businesses discover their biggest client is their least profitable.

OneWave-AI/claude-skills · 55 tokens

peer-bench

Peer benchmarking, multi-ticker financial comparison, growth value matrix, composite z-score ranking, industry peer comparison, competitive benchmarking, sector relative performance, peer group analysis, industry leader comparison, financial ratio benchmarking.

agentii-ai/agentii-investment-intelligence · 44 tokens

fda-catalyst-vaccines

Vaccine catalyst analysis across the two-gate path: CBER BLA review and the ACIP recommendation gate that turns FDA approval into commercial availability. Lot release, procurement milestones, pediatric bridging, and healthy-population efficacy context — dated, verified, and sized per event.

agentii-ai/agentii-investment-intelligence · 64 tokens

trading-as-business

Trading as a business, performance review process, trade journaling, capital allocation discipline, business infrastructure for traders, KPI tracking for trading operations.

agentii-ai/agentii-investment-intelligence · 32 tokens