What does the Cwicr Comparison Tool AI skill do?

Compare cost estimates across projects, versions, and scenarios. Identify variances, benchmark against standards, and generate comparison reports.

Why use Cwicr Comparison Tool on TypingMind?

Because you install it once and use it with any model. Cwicr Comparison Tool 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 Comparison Tool 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-comparison-tool. 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 Comparison Tool?

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 Comparison Tool?

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

Is the Cwicr Comparison Tool 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 Comparison Tool by datadrivenconstruction — AI Skill for Any LLM

CWICR Comparison Tool

Business Case

Problem Statement

Project stakeholders need to compare:

Alternative design options
Estimate versions over time
Projects against benchmarks
Actual vs estimated costs

Solution

Structured comparison of CWICR-based estimates with variance analysis, benchmarking, and visual reporting.

Business Value

Decision support - Compare alternatives objectively
Version control - Track estimate evolution
Benchmarking - Compare against standards
Audit - Document estimate changes

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


class ComparisonType(Enum):
    """Types of comparisons."""
    VERSION = "version"          # Same project, different versions
    ALTERNATIVE = "alternative"  # Same project, design alternatives
    BENCHMARK = "benchmark"      # Project vs standard/benchmark
    ACTUAL = "actual"            # Estimate vs actual costs
    PROJECT = "project"          # Different projects


class VarianceSignificance(Enum):
    """Significance level of variance."""
    CRITICAL = "critical"    # >20% variance
    HIGH = "high"            # 10-20%
    MEDIUM = "medium"        # 5-10%
    LOW = "low"              # <5%
    NONE = "none"            # No variance


@dataclass
class ComparisonItem:
    """Single item comparison."""
    work_item_code: str
    description: str
    base_quantity: float
    base_cost: float
    compare_quantity: float
    compare_cost: float
    quantity_variance: float
    quantity_variance_pct: float
    cost_variance: float
    cost_variance_pct: float
    significance: VarianceSignificance


@dataclass
class ComparisonResult:
    """Complete comparison result."""
    comparison_type: ComparisonType
    base_name: str
    compare_name: str
    base_total: float
    compare_total: float
    total_variance: float
    total_variance_pct: float
    items: List[ComparisonItem]
    summary_by_category: Dict[str, Dict[str, float]]
    created_at: datetime


class CWICRComparisonTool:
    """Compare CWICR-based estimates."""

    SIGNIFICANCE_THRESHOLDS = {
        VarianceSignificance.CRITICAL: 0.20,
        VarianceSignificance.HIGH: 0.10,
        VarianceSignificance.MEDIUM: 0.05,
        VarianceSignificance.LOW: 0.01
    }

    def __init__(self):
        pass

    def _get_significance(self, variance_pct: float) -> VarianceSignificance:
        """Determine variance significance."""
        abs_var = abs(variance_pct) / 100

        if abs_var >= self.SIGNIFICANCE_THRESHOLDS[VarianceSignificance.CRITICAL]:
            return VarianceSignificance.CRITICAL
        elif abs_var >= self.SIGNIFICANCE_THRESHOLDS[VarianceSignificance.HIGH]:
            return VarianceSignificance.HIGH
        elif abs_var >= self.SIGNIFICANCE_THRESHOLDS[VarianceSignificance.MEDIUM]:
            return VarianceSignificance.MEDIUM
        elif abs_var >= self.SIGNIFICANCE_THRESHOLDS[VarianceSignificance.LOW]:
            return VarianceSignificance.LOW
        else:
            return VarianceSignificance.NONE

    def compare_estimates(self,
                          base_df: pd.DataFrame,
                          compare_df: pd.DataFrame,
                          base_name: str = "Base",
                          compare_name: str = "Compare",
                          comparison_type: ComparisonType = ComparisonType.VERSION,
                          code_column: str = 'work_item_code',
                          quantity_column: str = 'quantity',
                          cost_column: str = 'total_cost') -> ComparisonResult:
        """Compare two estimates."""

        # Merge on code
        merged = base_df.merge(
            compare_df,
            on=code_column,
            how='outer',
            suffixes=('_base', '_compare')
        )

        items = []
        for _, row in merged.iterrows():
            base_qty = float(row.get(f'{quantity_column}_base', 0) or 0)
            base_cost = float(row.get(f'{cost_column}_base', 0) or 0)
            compare_qty = float(row.get(f'{quantity_column}_compare', 0) or 0)
            compare_cost = float(row.get(f'{cost_column}_compare', 0) or 0)

            qty_variance = compare_qty - base_qty
            qty_variance_pct = (qty_variance / base_qty * 100) if base_qty > 0 else (100 if compare_qty > 0 else 0)

            cost_variance = compare_cost - base_cost
            cost_variance_pct = (cost_variance / base_cost * 100) if base_cost > 0 else (100 if compare_cost > 0 else 0)

            items.append(ComparisonItem(
                work_item_code=str(row.get(code_column, '')),
                description=str(row.get('description_base', row.get('description_compare', ''))),
                base_quantity=base_qty,
                base_cost=base_cost,
                compare_quantity=compare_qty,
                compare_cost=compare_cost,
                quantity_variance=round(qty_variance, 2),
                quantity_variance_pct=round(qty_variance_pct, 1),
                cost_variance=round(cost_variance, 2),
                cost_variance_pct=round(cost_variance_pct, 1),
                significance=self._get_significance(cost_variance_pct)
            ))

        # Totals
        base_total = sum(i.base_cost for i in items)
        compare_total = sum(i.compare_cost for i in items)
        total_variance = compare_total - base_total
        total_variance_pct = (total_variance / base_total * 100) if base_total > 0 else 0

        # Summary by category
        summary_by_category = self._summarize_by_category(items, merged)

        return ComparisonResult(
            comparison_type=comparison_type,
            base_name=base_name,
            compare_name=compare_name,
            base_total=round(base_total, 2),
            compare_total=round(compare_total, 2),
            total_variance=round(total_variance, 2),
            total_variance_pct=round(total_variance_pct, 1),
            items=items,
            summary_by_category=summary_by_category,
            created_at=datetime.now()
        )

    def _summarize_by_category(self,
                                items: List[ComparisonItem],
                                merged_df: pd.DataFrame) -> Dict[str, Dict[str, float]]:
        """Summarize comparison by category."""

        summary = {}

        # Try to extract category from work item code prefix
        for item in items:
            code = item.work_item_code
            category = code.split('-')[0] if '-' in code else 'Other'

            if category not in summary:
                summary[category] = {
                    'base_cost': 0,
                    'compare_cost': 0,
                    'variance': 0,
                    'variance_pct': 0,
                    'item_count': 0
                }

            summary[category]['base_cost'] += item.base_cost
            summary[category]['compare_cost'] += item.compare_cost
            summary[category]['variance'] += item.cost_variance
            summary[category]['item_count'] += 1

        # Calculate percentages
        for category in summary:
            base = summary[category]['base_cost']
            if base > 0:
                summary[category]['variance_pct'] = round(
                    summary[category]['variance'] / base * 100, 1
                )

        return summary

    def get_significant_variances(self,
                                   result: ComparisonResult,
                                   min_significance: VarianceSignificance = VarianceSignificance.MEDIUM) -> List[ComparisonItem]:
        """Get items with significant variances."""

        significance_order = [
            VarianceSignificance.CRITICAL,
            VarianceSignificance.HIGH,
            VarianceSignificance.MEDIUM,
            VarianceSignificance.LOW,
            VarianceSignificance.NONE
        ]

        min_index = significance_order.index(min_significance)
        significant = [
            item for item in result.items
            if significance_order.index(item.significance) <= min_index
        ]

        return sorted(significant, key=lambda x: abs(x.cost_variance), reverse=True)

    def compare_multiple(self,
                          estimates: List[Tuple[str, pd.DataFrame]],
                          base_index: int = 0) -> Dict[str, ComparisonResult]:
        """Compare multiple estimates against base."""

        base_name, base_df = estimates[base_index]
        results = {}

        for i, (name, df) in enumerate(estimates):
            if i == base_index:
                continue

            result = self.compare_estimates(
                base_df=base_df,
                compare_df=df,
                base_name=base_name,
                compare_name=name,
                comparison_type=ComparisonType.ALTERNATIVE
            )
            results[name] = result

        return results

    def benchmark_comparison(self,
                              project_df: pd.DataFrame,
                              benchmark_df: pd.DataFrame,
                              project_name: str,
                              benchmark_name: str = "Industry Benchmark") -> ComparisonResult:
        """Compare project against benchmark."""

        return self.compare_estimates(
            base_df=benchmark_df,
            compare_df=project_df,
            base_name=benchmark_name,
            compare_name=project_name,
            comparison_type=ComparisonType.BENCHMARK
        )

    def version_comparison(self,
                           versions: List[Tuple[str, pd.DataFrame]]) -> List[ComparisonResult]:
        """Compare sequential versions."""

        results = []

        for i in range(1, len(versions)):
            prev_name, prev_df = versions[i-1]
            curr_name, curr_df = versions[i]

            result = self.compare_estimates(
                base_df=prev_df,
                compare_df=curr_df,
                base_name=prev_name,
                compare_name=curr_name,
                comparison_type=ComparisonType.VERSION
            )
            results.append(result)

        return results

    def export_comparison(self,
                          result: ComparisonResult,
                          output_path: str) -> str:
        """Export comparison to Excel."""

        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Summary
            summary_df = pd.DataFrame([{
                'Comparison Type': result.comparison_type.value,
                'Base': result.base_name,
                'Compare': result.compare_name,
                'Base Total': result.base_total,
                'Compare Total': result.compare_total,
                'Variance': result.total_variance,
                'Variance %': result.total_variance_pct,
                'Generated': result.created_at.strftime('%Y-%m-%d %H:%M')
            }])
            summary_df.to_excel(writer, sheet_name='Summary', index=False)

            # Details
            details_df = pd.DataFrame([
                {
                    'Work Item': i.work_item_code,
                    'Description': i.description,
                    f'{result.base_name} Qty': i.base_quantity,
                    f'{result.base_name} Cost': i.base_cost,
                    f'{result.compare_name} Qty': i.compare_quantity,
                    f'{result.compare_name} Cost': i.compare_cost,
                    'Qty Variance': i.quantity_variance,
                    'Qty Variance %': i.quantity_variance_pct,
                    'Cost Variance': i.cost_variance,
                    'Cost Variance %': i.cost_variance_pct,
                    'Significance': i.significance.value
                }
                for i in result.items
            ])
            details_df.to_excel(writer, sheet_name='Details', index=False)

            # By Category
            cat_df = pd.DataFrame([
                {
                    'Category': cat,
                    'Base Cost': data['base_cost'],
                    'Compare Cost': data['compare_cost'],
                    'Variance': data['variance'],
                    'Variance %': data['variance_pct'],
                    'Items': data['item_count']
                }
                for cat, data in result.summary_by_category.items()
            ])
            cat_df.to_excel(writer, sheet_name='By Category', index=False)

            # Significant Variances
            significant = self.get_significant_variances(result)
            sig_df = pd.DataFrame([
                {
                    'Work Item': i.work_item_code,
                    'Description': i.description,
                    'Cost Variance': i.cost_variance,
                    'Variance %': i.cost_variance_pct,
                    'Significance': i.significance.value
                }
                for i in significant
            ])
            sig_df.to_excel(writer, sheet_name='Significant', index=False)

        return output_path


class ComparisonAnalytics:
    """Analytics for comparison results."""

    def __init__(self, comparison_tool: CWICRComparisonTool):
        self.tool = comparison_tool

    def variance_distribution(self, result: ComparisonResult) -> Dict[str, int]:
        """Get distribution of variance significance."""

        distribution = {s.value: 0 for s in VarianceSignificance}

        for item in result.items:
            distribution[item.significance.value] += 1

        return distribution

    def top_variances(self,
                      result: ComparisonResult,
                      n: int = 10,
                      positive: bool = True) -> List[ComparisonItem]:
        """Get top N variances (positive or negative)."""

        if positive:
            sorted_items = sorted(result.items, key=lambda x: x.cost_variance, reverse=True)
        else:
            sorted_items = sorted(result.items, key=lambda x: x.cost_variance)

        return sorted_items[:n]

    def category_impact(self, result: ComparisonResult) -> pd.DataFrame:
        """Analyze which categories contribute most to variance."""

        data = []
        for cat, values in result.summary_by_category.items():
            contribution_pct = (values['variance'] / result.total_variance * 100) if result.total_variance != 0 else 0
            data.append({
                'Category': cat,
                'Variance': values['variance'],
                'Contribution %': round(contribution_pct, 1)
            })

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

    def trend_analysis(self,
                        version_results: List[ComparisonResult]) -> pd.DataFrame:
        """Analyze cost trend across versions."""

        data = []
        cumulative = 0

        for result in version_results:
            cumulative += result.total_variance
            data.append({
                'From': result.base_name,
                'To': result.compare_name,
                'Variance': result.total_variance,
                'Variance %': result.total_variance_pct,
                'Cumulative Variance': cumulative
            })

        return pd.DataFrame(data)

Quick Start

python
# Initialize comparison tool
tool = CWICRComparisonTool()

# Compare two estimate versions
result = tool.compare_estimates(
    base_df=estimate_v1,
    compare_df=estimate_v2,
    base_name="Estimate v1.0",
    compare_name="Estimate v2.0"
)

print(f"Total Variance: ${result.total_variance:,.2f} ({result.total_variance_pct}%)")

Common Use Cases

1. Significant Variances

python
significant = tool.get_significant_variances(result)
for item in significant[:5]:
    print(f"{item.work_item_code}: ${item.cost_variance:,.2f} ({item.significance.value})")

2. Version History

python
versions = [
    ("v1.0", estimate_v1),
    ("v2.0", estimate_v2),
    ("v3.0", estimate_v3)
]
version_results = tool.version_comparison(versions)

analytics = ComparisonAnalytics(tool)
trend = analytics.trend_analysis(version_results)

3. Design Alternatives

python
alternatives = [
    ("Option A - Steel", option_a),
    ("Option B - Concrete", option_b),
    ("Option C - Hybrid", option_c)
]
comparisons = tool.compare_multiple(alternatives, base_index=0)

4. Export Report

python
tool.export_comparison(result, "estimate_comparison.xlsx")

Resources

GitHub: OpenConstructionEstimate-DDC-CWICR
DDC Book: Chapter 3.1 - Estimate Management

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

Cwicr Comparison Tool

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

CWICR Comparison Tool

Business Case

Problem Statement

Solution

Business Value

Technical Implementation

Quick Start

Common Use Cases

1. Significant Variances

2. Version History

3. Design Alternatives

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 Comparison Tool

Business Case

Problem Statement

Solution

Business Value

Technical Implementation

Quick Start

Common Use Cases

1. Significant Variances

2. Version History

3. Design Alternatives

4. Export Report

Resources

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now