cwicr-takeoff-helper

cwicr-takeoff-helper is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 34 tokens per session (3,312 once invoked), scanned A, original, MIT.

A construction quantity-takeoff helper that calculates how much work or material a project requires from dimensions, counts, weights, or volumes.

In plain words
What is it for?
Use it for linear, area, volume, count, or weight takeoffs in metric or imperial units, with waste factors and related-item suggestions.
Why use it?
It reduces calculation and unit-conversion mistakes, includes expected waste, and helps reveal missing related work.

Skill for Claude CodeCodex

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

Good fit Use it for linear, area, volume, count, or weight takeoffs in metric or imperial units, with waste factors and related-item suggestions.

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

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README.md
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Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,312 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 $0.00034 $0.03312
Opus 5 $0.00017 $0.01656
Sonnet 5 $0.00007 $0.00662
Haiku 4.5 $0.00003 $0.00331

Measured 13d ago against content hash ced0665b9c19, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

cwicr-takeoff-helper 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 13d 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-takeoff-helper/SKILL.md · 444 lines

How it starts

The opening of the file, as written. The whole thing — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.

CWICR Takeoff Helper

Business Case

Problem Statement

Quantity takeoff requires:

  • Accurate calculations from dimensions
  • Correct unit conversions
  • Waste factor application
  • Complete scope coverage

Solution

Assist takeoff process with CWICR-based calculations, automatic waste factors, unit conversions, and related item suggestions.

Business Value

  • Accuracy - Validated calculations
  • Completeness - Related items suggested
  • Speed - Quick quantity calculations
  • Consistency - Standard approaches

Technical Implementation

import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import math


class TakeoffType(Enum):
    """Types of takeoff calculations."""
    LINEAR = "linear"        # Length
    AREA = "area"            # Square measure
    VOLUME = "volume"        # Cubic measure
    COUNT = "count"          # Each/number
    WEIGHT = "weight"        # By weight


class UnitSystem(Enum):
    """Unit systems."""
    METRIC = "metric"
    IMPERIAL = "imperial"


@dataclass
class TakeoffItem:
    """Single takeoff item."""
    work_item_code: str
    description: str
    takeoff_type: TakeoffType
    gross_quantity: float
    waste_factor: float
    net_quantity: float
    unit: str
    dimensions: Dict[str, float]
    calculation: str


@dataclass
class TakeoffResult:
    """Complete takeoff result."""
    items: List[TakeoffItem]
    total_items: int
    related_suggestions: List[str]


# Unit conversion factors
CONVERSIONS = {
    # Length
    ('m', 'ft'): 3.28084,
    ('ft', 'm'): 0.3048,
    ('m', 'in'): 39.3701,
    ('in', 'm'): 0.0254,

    # Area
    ('m2', 'sf'): 10.7639,
    ('sf', 'm2'): 0.0929,

    # Volume
    ('m3', 'cf'): 35.3147,
    ('cf', 'm3'): 0.0283,
    ('m3', 'cy'): 1.30795,
    ('cy', 'm3'): 0.7646,

    # Weight
    ('kg', 'lb'): 2.20462,
    ('lb', 'kg'): 0.453592,
    ('ton', 'kg'): 1000,
    ('kg', 'ton'): 0.001
}

# Standard waste factors
WASTE_FACTORS = {
    'concrete': 0.05,
    'rebar': 0.08,
    'formwork': 0.10,
    'brick': 0.10,
    'block': 0.08,
    'drywall': 0.12,
    'tile': 0.15,
    'lumber': 0.12,
    'roofing': 0.10,
    'paint': 0.10,
    'pipe': 0.05,
    'wire': 0.05,
    'duct': 0.08,
    'default': 0.05
}

# Related work items by category
RELATED_ITEMS = {
    'concrete': ['formwork', 'rebar', 'curing', 'finishing'],
    'masonry': ['mortar', 'reinforcement', 'ties', 'lintels'],
    'drywall': ['framing', 'insulation', 'taping', 'painting'],
    'roofing': ['underlayment', 'flashing', 'ventilation', 'insulation'],
    'flooring': ['underlayment', 'adhesive', 'trim', 'transitions']
}


class CWICRTakeoffHelper:
    """Assist with quantity takeoff using CWICR data."""

    def __init__(self, cwicr_data: pd.DataFrame = None):
        self.cwicr = cwicr_data
        if cwicr_data is not None:
            self._index_cwicr()

    def _index_cwicr(self):
        """Index CWICR data."""
        if 'work_item_code' in self.cwicr.columns:
            self._cwicr_index = self.cwicr.set_index('work_item_code')
        else:
            self._cwicr_index = None

    def convert_unit(self, value: float, from_unit: str, to_unit: str) -> float:
        """Convert between units."""
        if from_unit == to_unit:
            return value

        key = (from_unit.lower(), to_unit.lower())
        if key in CONVERSIONS:
            return value * CONVERSIONS[key]

        # Try reverse
        reverse_key = (to_unit.lower(), from_unit.lower())
        if reverse_key in CONVERSIONS:
            return value / CONVERSIONS[reverse_key]

        return value

    def get_waste_factor(self, work_item_code: str) -> float:
        """Get waste factor for work item."""
        code_lower = work_item_code.lower()

        for material, factor in WASTE_FACTORS.items():
            if material in code_lower:
                return factor

        return WASTE_FACTORS['default']

    def calculate_area(self,
                       length: float,
                       width: float,
                       deductions: List[Tuple[float, float]] = None) -> Dict[str, float]:
        """Calculate area with deductions."""

        gross_area = length * width

        deduction_area = 0
        if deductions:
            for d_length, d_width in deductions:
                deduction_area += d_length * d_width

        net_area = gross_area - deduction_area

        return {
            'gross_area': round(gross_area, 2),
            'deductions': round(deduction_area, 2),
            'net_area': round(net_area, 2),
            'calculation': f"{length} x {width} = {gross_area}, minus {deduction_area} deductions"
        }

    def calculate_volume(self,
                          length: float,
                          width: float,
                          depth: float) -> Dict[str, float]:
        """Calculate volume."""

        volume = length * width * depth

        return {
            'volume': round(volume, 3),
            'calculation': f"{length} x {width} x {depth} = {volume}"
        }

    def calculate_perimeter(self,
                            length: float,
                            width: float) -> Dict[str, float]:
        """Calculate perimeter."""

        perimeter = 2 * (length + width)

        return {
            'perimeter': round(perimeter, 2),
            'calculation': f"2 x ({length} + {width}) = {perimeter}"
        }

    def calculate_concrete(self,
                            length: float,
                            width: float,
                            thickness: float,
                            work_item_code: str = "CONC-001") -> TakeoffItem:
        """Calculate concrete quantity with related items."""

        volume = length * width * thickness
        waste = self.get_waste_factor(work_item_code)
        net_qty = volume * (1 + waste)

        return TakeoffItem(
            work_item_code=work_item_code,
            description="Concrete",
            takeoff_type=TakeoffType.VOLUME,
            gross_quantity=round(volume, 3),
            waste_factor=waste,
            net_quantity=round(net_qty, 3),
            unit="m3",
            dimensions={'length': length, 'width': width, 'thickness': thickness},
            calculation=f"{length}m x {width}m x {thickness}m = {volume:.3f} m3 + {waste:.0%} waste"
        )

    def calculate_wall_area(self,
                             perimeter: float,
                             height: float,
                             openings: List[Tuple[float, float]] = None,
                             work_item_code: str = "WALL-001") -> TakeoffItem:
        """Calculate wall area with openings deducted."""

        gross_area = perimeter * height

        opening_area = 0
        if openings:
            for w, h in openings:
                opening_area += w * h

        net_area = gross_area - opening_area
        waste = self.get_waste_factor(work_item_code)
        order_qty = net_area * (1 + waste)

        return TakeoffItem(
            work_item_code=work_item_code,
            description="Wall finish",
            takeoff_type=TakeoffType.AREA,
            gross_quantity=round(gross_area, 2),
            waste_factor=waste,
            net_quantity=round(order_qty, 2),
            unit="m2",
            dimensions={'perimeter': perimeter, 'height': height, 'openings': len(openings or [])},
            calculation=f"{perimeter}m x {height}m = {gross_area:.2f} m2 - {opening_area:.2f} openings + {waste:.0%} waste"
        )

    def calculate_flooring(self,
                            length: float,
                            width: float,
                            work_item_code: str = "FLOOR-001") -> TakeoffItem:
        """Calculate flooring quantity."""

        area = length * width
        waste = self.get_waste_factor(work_item_code)
        order_qty = area * (1 + waste)

        return TakeoffItem(
            work_item_code=work_item_code,
            description="Flooring",
            takeoff_type=TakeoffType.AREA,
            gross_quantity=round(area, 2),
            waste_factor=waste,
            net_quantity=round(order_qty, 2),
            unit="m2",
            dimensions={'length': length, 'width': width},
            calculation=f"{length}m x {width}m = {area:.2f} m2 + {waste:.0%} waste"
        )

    def calculate_rebar(self,
                         concrete_volume: float,
                         kg_per_m3: float = 100,
                         work_item_code: str = "REBAR-001") -> TakeoffItem:
        """Calculate rebar from concrete volume."""

        weight = concrete_volume * kg_per_m3
        waste = self.get_waste_factor(work_item_code)
        order_qty = weight * (1 + waste)

        return TakeoffItem(
            work_item_code=work_item_code,
            description="Reinforcement",
            takeoff_type=TakeoffType.WEIGHT,
            gross_quantity=round(weight, 1),
            waste_factor=waste,
            net_quantity=round(order_qty, 1),
            unit="kg",
            dimensions={'concrete_m3': concrete_volume, 'kg_per_m3': kg_per_m3},
            calculation=f"{concrete_volume} m3 x {kg_per_m3} kg/m3 = {weight:.1f} kg + {waste:.0%} waste"
        )

    def suggest_related_items(self, work_item_code: str) -> List[str]:
        """Suggest related work items."""

        code_lower = work_item_code.lower()

        for category, related in RELATED_ITEMS.items():
            if category in code_lower:
                return related

        return []

    def room_takeoff(self,
                      length: float,
                      width: float,
                      height: float,
                      openings: List[Tuple[float, float]] = None) -> TakeoffResult:
        """Complete room takeoff."""

        items = []

        # Floor
        floor = self.calculate_flooring(length, width, "FLOOR-001")
        items.append(floor)

        # Ceiling (same as floor)
        ceiling = TakeoffItem(
            work_item_code="CEIL-001",
            description="Ceiling",
            takeoff_type=TakeoffType.AREA,
            gross_quantity=floor.gross_quantity,
            waste_factor=floor.waste_factor,
            net_quantity=floor.net_quantity,
            unit="m2",
            dimensions=floor.dimensions,
            calculation=f"Same as floor: {floor.gross_quantity} m2"
        )
        items.append(ceiling)

        # Walls
        perimeter = 2 * (length + width)
        walls = self.calculate_wall_area(perimeter, height, openings, "WALL-001")
        items.append(walls)

        # Related suggestions
        suggestions = ['paint', 'baseboard', 'trim', 'electrical outlets']

        return TakeoffResult(
            items=items,
            total_items=len(items),
            related_suggestions=suggestions
        )

    def export_takeoff(self,
                        items: List[TakeoffItem],
                        output_path: str) -> str:
        """Export takeoff to Excel."""

        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            df = pd.DataFrame([
                {
                    'Work Item Code': item.work_item_code,
                    'Description': item.description,
                    'Type': item.takeoff_type.value,
                    'Gross Qty': item.gross_quantity,
                    'Waste %': f"{item.waste_factor:.0%}",
                    'Net Qty': item.net_quantity,
                    'Unit': item.unit,
                    'Calculation': item.calculation
                }
                for item in items
            ])
            df.to_excel(writer, sheet_name='Takeoff', index=False)

        return output_path

Read the full file on GitHub · 444 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. 13d ago First seen · 444 lines · 34 tokens per session scan A ced0665b9c19

Subscribe to this mod's changes

cwicr-takeoff-helper 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 34 tokens to every session and 3,312 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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