What does the Cwicr Work Breakdown AI skill do?

Break down CWICR work items into component resources. Decompose aggregate items, analyze resource composition, and generate detailed bills of resources.

Why use Cwicr Work Breakdown on TypingMind?

Because you install it once and use it with any model. Cwicr Work Breakdown is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cwicr Work Breakdown in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction/tree/main/1_DDC_Toolkit/CWICR-Database/cwicr-work-breakdown. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cwicr Work Breakdown?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cwicr Work Breakdown?

As many as you like. As long as a model supports skills, you can use Cwicr Work Breakdown with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cwicr Work Breakdown AI skill free?

Yes. It is published on GitHub by datadrivenconstruction under the MIT license. You only pay your own AI provider for the tokens you use.

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

Cwicr Work Breakdown by datadrivenconstruction — AI Skill for Any LLM

CWICR Work Breakdown

Business Case

Problem Statement

Work items in CWICR contain aggregated resources:

What materials make up a concrete work item?
What labor categories are needed?
What equipment is involved?
How to generate detailed resource bills?

Solution

Decompose CWICR work items into their constituent resources (labor, materials, equipment) with quantities and costs.

Business Value

Transparency - See inside aggregated items
Procurement - Generate material lists
Scheduling - Identify resource needs
Cost control - Track resource consumption

Technical Implementation

python
import pandas as pd
import numpy as np
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from collections import defaultdict


class ResourceType(Enum):
    """Types of resources in work items."""
    LABOR = "labor"
    MATERIAL = "material"
    EQUIPMENT = "equipment"
    OVERHEAD = "overhead"


@dataclass
class ResourceComponent:
    """Single resource component of a work item."""
    resource_code: str
    resource_type: ResourceType
    description: str
    unit: str
    quantity_per_unit: float  # Per unit of work item
    unit_rate: float
    cost_per_unit: float  # Per unit of work item


@dataclass
class WorkItemBreakdown:
    """Complete breakdown of a work item."""
    work_item_code: str
    work_item_description: str
    work_item_unit: str
    components: List[ResourceComponent]
    labor_cost_per_unit: float
    material_cost_per_unit: float
    equipment_cost_per_unit: float
    total_cost_per_unit: float


@dataclass
class BillOfResources:
    """Bill of resources for multiple work items."""
    project_name: str
    total_labor_cost: float
    total_material_cost: float
    total_equipment_cost: float
    total_cost: float
    labor_resources: List[Dict[str, Any]]
    material_resources: List[Dict[str, Any]]
    equipment_resources: List[Dict[str, Any]]


class CWICRWorkBreakdown:
    """Break down work items into resources."""

    def __init__(self, cwicr_data: pd.DataFrame,
                 resources_data: pd.DataFrame = None):
        self.work_items = cwicr_data
        self.resources = resources_data
        self._index_data()

    def _index_data(self):
        """Index data for fast lookup."""
        if 'work_item_code' in self.work_items.columns:
            self._work_index = self.work_items.set_index('work_item_code')
        else:
            self._work_index = None

        if self.resources is not None and 'resource_code' in self.resources.columns:
            self._resource_index = self.resources.set_index('resource_code')
        else:
            self._resource_index = None

    def breakdown_work_item(self, work_item_code: str) -> Optional[WorkItemBreakdown]:
        """Break down single work item into components."""

        if self._work_index is None or work_item_code not in self._work_index.index:
            return None

        item = self._work_index.loc[work_item_code]
        components = []

        # Extract labor component
        labor_norm = float(item.get('labor_norm', 0) or 0)
        labor_rate = float(item.get('labor_rate', 35) or 35)
        labor_cost = float(item.get('labor_cost', labor_norm * labor_rate) or labor_norm * labor_rate)

        if labor_norm > 0:
            components.append(ResourceComponent(
                resource_code=f"{work_item_code}-LABOR",
                resource_type=ResourceType.LABOR,
                description=f"Labor for {item.get('description', '')}",
                unit="hr",
                quantity_per_unit=labor_norm,
                unit_rate=labor_rate,
                cost_per_unit=labor_cost
            ))

        # Extract material component
        material_norm = float(item.get('material_norm', 1) or 1)
        material_cost = float(item.get('material_cost', 0) or 0)

        if material_cost > 0:
            components.append(ResourceComponent(
                resource_code=f"{work_item_code}-MAT",
                resource_type=ResourceType.MATERIAL,
                description=str(item.get('material_description', 'Materials')),
                unit=str(item.get('material_unit', item.get('unit', 'ea'))),
                quantity_per_unit=material_norm,
                unit_rate=material_cost / material_norm if material_norm > 0 else material_cost,
                cost_per_unit=material_cost
            ))

        # Extract equipment component
        equipment_norm = float(item.get('equipment_norm', 0) or 0)
        equipment_rate = float(item.get('equipment_rate', 0) or 0)
        equipment_cost = float(item.get('equipment_cost', equipment_norm * equipment_rate) or 0)

        if equipment_norm > 0 or equipment_cost > 0:
            components.append(ResourceComponent(
                resource_code=f"{work_item_code}-EQUIP",
                resource_type=ResourceType.EQUIPMENT,
                description=str(item.get('equipment_description', 'Equipment')),
                unit="hr",
                quantity_per_unit=equipment_norm,
                unit_rate=equipment_rate,
                cost_per_unit=equipment_cost
            ))

        return WorkItemBreakdown(
            work_item_code=work_item_code,
            work_item_description=str(item.get('description', '')),
            work_item_unit=str(item.get('unit', '')),
            components=components,
            labor_cost_per_unit=labor_cost,
            material_cost_per_unit=material_cost,
            equipment_cost_per_unit=equipment_cost,
            total_cost_per_unit=labor_cost + material_cost + equipment_cost
        )

    def generate_bill_of_resources(self,
                                    items: List[Dict[str, Any]],
                                    project_name: str = "Project") -> BillOfResources:
        """Generate bill of resources from work items."""

        labor_agg = defaultdict(lambda: {'hours': 0, 'cost': 0, 'work_items': []})
        material_agg = defaultdict(lambda: {'quantity': 0, 'cost': 0, 'unit': '', 'work_items': []})
        equipment_agg = defaultdict(lambda: {'hours': 0, 'cost': 0, 'work_items': []})

        for item in items:
            code = item.get('work_item_code', item.get('code'))
            qty = item.get('quantity', 0)

            breakdown = self.breakdown_work_item(code)
            if not breakdown:
                continue

            for component in breakdown.components:
                scaled_qty = component.quantity_per_unit * qty
                scaled_cost = component.cost_per_unit * qty

                if component.resource_type == ResourceType.LABOR:
                    key = 'General Labor'  # Could be more specific with skill data
                    labor_agg[key]['hours'] += scaled_qty
                    labor_agg[key]['cost'] += scaled_cost
                    labor_agg[key]['work_items'].append(code)

                elif component.resource_type == ResourceType.MATERIAL:
                    key = component.description
                    material_agg[key]['quantity'] += scaled_qty
                    material_agg[key]['cost'] += scaled_cost
                    material_agg[key]['unit'] = component.unit
                    material_agg[key]['work_items'].append(code)

                elif component.resource_type == ResourceType.EQUIPMENT:
                    key = component.description
                    equipment_agg[key]['hours'] += scaled_qty
                    equipment_agg[key]['cost'] += scaled_cost
                    equipment_agg[key]['work_items'].append(code)

        # Convert to lists
        labor_resources = [
            {
                'resource': name,
                'hours': round(data['hours'], 1),
                'cost': round(data['cost'], 2),
                'work_items': len(set(data['work_items']))
            }
            for name, data in labor_agg.items()
        ]

        material_resources = [
            {
                'resource': name,
                'quantity': round(data['quantity'], 2),
                'unit': data['unit'],
                'cost': round(data['cost'], 2),
                'work_items': len(set(data['work_items']))
            }
            for name, data in material_agg.items()
        ]

        equipment_resources = [
            {
                'resource': name,
                'hours': round(data['hours'], 1),
                'cost': round(data['cost'], 2),
                'work_items': len(set(data['work_items']))
            }
            for name, data in equipment_agg.items()
        ]

        total_labor = sum(r['cost'] for r in labor_resources)
        total_material = sum(r['cost'] for r in material_resources)
        total_equipment = sum(r['cost'] for r in equipment_resources)

        return BillOfResources(
            project_name=project_name,
            total_labor_cost=round(total_labor, 2),
            total_material_cost=round(total_material, 2),
            total_equipment_cost=round(total_equipment, 2),
            total_cost=round(total_labor + total_material + total_equipment, 2),
            labor_resources=labor_resources,
            material_resources=material_resources,
            equipment_resources=equipment_resources
        )

    def get_resource_composition(self, work_item_code: str) -> Dict[str, float]:
        """Get percentage composition of work item by resource type."""

        breakdown = self.breakdown_work_item(work_item_code)
        if not breakdown or breakdown.total_cost_per_unit == 0:
            return {'labor': 0, 'material': 0, 'equipment': 0}

        total = breakdown.total_cost_per_unit
        return {
            'labor': round(breakdown.labor_cost_per_unit / total * 100, 1),
            'material': round(breakdown.material_cost_per_unit / total * 100, 1),
            'equipment': round(breakdown.equipment_cost_per_unit / total * 100, 1)
        }

    def analyze_labor_intensity(self,
                                 work_items: List[str]) -> pd.DataFrame:
        """Analyze labor intensity of work items."""

        data = []
        for code in work_items:
            breakdown = self.breakdown_work_item(code)
            if breakdown:
                composition = self.get_resource_composition(code)
                labor_components = [c for c in breakdown.components if c.resource_type == ResourceType.LABOR]
                labor_hours = sum(c.quantity_per_unit for c in labor_components)

                data.append({
                    'work_item_code': code,
                    'description': breakdown.work_item_description,
                    'labor_hours_per_unit': labor_hours,
                    'labor_cost_pct': composition['labor'],
                    'material_cost_pct': composition['material'],
                    'equipment_cost_pct': composition['equipment'],
                    'labor_intensive': composition['labor'] > 50
                })

        return pd.DataFrame(data).sort_values('labor_cost_pct', ascending=False)

    def export_breakdown(self,
                         breakdown: WorkItemBreakdown,
                         output_path: str) -> str:
        """Export single work item breakdown."""

        df = pd.DataFrame([
            {
                'Resource Code': c.resource_code,
                'Type': c.resource_type.value,
                'Description': c.description,
                'Unit': c.unit,
                'Quantity/Unit': c.quantity_per_unit,
                'Rate': c.unit_rate,
                'Cost/Unit': c.cost_per_unit
            }
            for c in breakdown.components
        ])

        df.to_excel(output_path, index=False)
        return output_path

    def export_bill_of_resources(self,
                                  bill: BillOfResources,
                                  output_path: str) -> str:
        """Export bill of resources to Excel."""

        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Summary
            summary_df = pd.DataFrame([{
                'Project': bill.project_name,
                'Total Labor Cost': bill.total_labor_cost,
                'Total Material Cost': bill.total_material_cost,
                'Total Equipment Cost': bill.total_equipment_cost,
                'Grand Total': bill.total_cost
            }])
            summary_df.to_excel(writer, sheet_name='Summary', index=False)

            # Labor
            labor_df = pd.DataFrame(bill.labor_resources)
            labor_df.to_excel(writer, sheet_name='Labor', index=False)

            # Materials
            material_df = pd.DataFrame(bill.material_resources)
            material_df.to_excel(writer, sheet_name='Materials', index=False)

            # Equipment
            equipment_df = pd.DataFrame(bill.equipment_resources)
            equipment_df.to_excel(writer, sheet_name='Equipment', index=False)

        return output_path


class ResourceAggregator:
    """Aggregate resources across work items."""

    def __init__(self, breakdown_tool: CWICRWorkBreakdown):
        self.breakdown = breakdown_tool

    def aggregate_by_trade(self,
                           items: List[Dict[str, Any]]) -> Dict[str, Dict[str, float]]:
        """Aggregate resources by trade/category."""

        by_trade = defaultdict(lambda: {'labor_hours': 0, 'labor_cost': 0, 'items': 0})

        for item in items:
            code = item.get('work_item_code', item.get('code'))
            qty = item.get('quantity', 0)

            # Extract trade from code prefix
            trade = code.split('-')[0] if '-' in code else 'General'

            breakdown = self.breakdown.breakdown_work_item(code)
            if breakdown:
                by_trade[trade]['labor_hours'] += breakdown.labor_cost_per_unit / 35 * qty  # Estimate hours
                by_trade[trade]['labor_cost'] += breakdown.labor_cost_per_unit * qty
                by_trade[trade]['items'] += 1

        return dict(by_trade)

    def identify_critical_resources(self,
                                     bill: BillOfResources,
                                     threshold_pct: float = 10) -> Dict[str, List[Dict]]:
        """Identify resources that contribute significantly to cost."""

        critical = {
            'labor': [],
            'material': [],
            'equipment': []
        }

        # Labor
        for r in bill.labor_resources:
            if bill.total_labor_cost > 0:
                pct = r['cost'] / bill.total_labor_cost * 100
                if pct >= threshold_pct:
                    critical['labor'].append({**r, 'percentage': round(pct, 1)})

        # Materials
        for r in bill.material_resources:
            if bill.total_material_cost > 0:
                pct = r['cost'] / bill.total_material_cost * 100
                if pct >= threshold_pct:
                    critical['material'].append({**r, 'percentage': round(pct, 1)})

        # Equipment
        for r in bill.equipment_resources:
            if bill.total_equipment_cost > 0:
                pct = r['cost'] / bill.total_equipment_cost * 100
                if pct >= threshold_pct:
                    critical['equipment'].append({**r, 'percentage': round(pct, 1)})

        return critical

Quick Start

python
# Load CWICR data
cwicr = pd.read_parquet("TR_workitems_costs_resources_DDC_CWICR.parquet")

# Initialize breakdown tool
breakdown = CWICRWorkBreakdown(cwicr)

# Break down single item
item_breakdown = breakdown.breakdown_work_item("CONC-001")

print(f"Work Item: {item_breakdown.work_item_description}")
print(f"Labor: ${item_breakdown.labor_cost_per_unit}")
print(f"Material: ${item_breakdown.material_cost_per_unit}")
print(f"Equipment: ${item_breakdown.equipment_cost_per_unit}")

for comp in item_breakdown.components:
    print(f"  - {comp.resource_type.value}: {comp.description}")

Common Use Cases

1. Bill of Resources

python
items = [
    {'work_item_code': 'CONC-001', 'quantity': 150},
    {'work_item_code': 'REBAR-002', 'quantity': 5000},
    {'work_item_code': 'FORM-003', 'quantity': 300}
]

bill = breakdown.generate_bill_of_resources(items, "Building A")
print(f"Total Labor: ${bill.total_labor_cost:,.2f}")
print(f"Total Material: ${bill.total_material_cost:,.2f}")

2. Resource Composition

python
composition = breakdown.get_resource_composition("CONC-001")
print(f"Labor: {composition['labor']}%")
print(f"Material: {composition['material']}%")

3. Labor Intensity Analysis

python
analysis = breakdown.analyze_labor_intensity(['CONC-001', 'EXCV-002', 'REBAR-003'])
print(analysis)

4. Export Report

python
breakdown.export_bill_of_resources(bill, "bill_of_resources.xlsx")

Resources

GitHub: OpenConstructionEstimate-DDC-CWICR
DDC Book: Chapter 3.1 - Resource-Based Estimating

Publisher	datadrivenconstruction
Repository	`DDC_Skills_for_AI_Agents_in_Construction`
Skill name	`cwicr-work-breakdown`
Stars	365
Forks	91
Bundled files	2
License	MIT
Links	Skill source Repository Homepage

Cwicr Work Breakdown

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

CWICR Work Breakdown

Business Case

Problem Statement

Solution

Business Value

Technical Implementation

Quick Start

Common Use Cases

1. Bill of Resources

2. Resource Composition

3. Labor Intensity Analysis

4. Export Report

Resources

Bundled files

Frequently asked questions

Set up your own AI workspace now

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

CWICR Work Breakdown

Business Case

Problem Statement

Solution

Business Value

Technical Implementation

Quick Start

Common Use Cases

1. Bill of Resources

2. Resource Composition

3. Labor Intensity Analysis

4. Export Report

Resources

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now