estimate-builder

estimate-builder is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 23 tokens per session (2,263 once invoked), scanned A, original, MIT.

A construction estimate builder that organizes labor, materials, equipment, subcontractors, overhead, markups, and other costs into detailed line items.

In plain words
What is it for?
Use it to create project estimates with quantities, unit costs, work breakdown structure (WBS) codes, categories, summaries, and exports.
Why use it?
It provides a consistent cost breakdown and handles totals and markups without repeated manual calculations.

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 create project estimates with quantities, unit costs, work breakdown structure (WBS) codes, categories, summaries, and exports.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/estimate-builder
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 estimate-builder
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 estimate-builder

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/estimate-builder/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/estimate-builder)
Your own site
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/estimate-builder"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/estimate-builder/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 estimate-builder

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/estimate-builder"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/estimate-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,263 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 $0.00023 $0.02263
Opus 5 $0.00012 $0.01131
Sonnet 5 $0.00005 $0.00453
Haiku 4.5 $0.00002 $0.00226

Measured 9d ago against content hash e8dea0e800b6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

estimate-builder 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 9d 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:

2_DDC_Book/3.1-Cost-Estimation/estimate-builder/SKILL.md · 323 lines

How it starts

The opening of the file, as written. The whole thing — 323 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Estimate Builder

Business Case

Problem Statement

Estimate creation challenges:

  • Complex cost structures
  • Multiple cost categories
  • Markup calculations
  • Format requirements vary

Solution

Structured estimate builder that creates professional construction estimates with proper cost categorization, markups, and export capabilities.

Technical Implementation

import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import date
from enum import Enum


class CostCategory(Enum):
    LABOR = "labor"
    MATERIAL = "material"
    EQUIPMENT = "equipment"
    SUBCONTRACTOR = "subcontractor"
    OTHER = "other"


@dataclass
class EstimateLineItem:
    line_number: int
    wbs_code: str
    description: str
    quantity: float
    unit: str
    unit_cost: float
    category: CostCategory
    notes: str = ""

    @property
    def total_cost(self) -> float:
        return round(self.quantity * self.unit_cost, 2)


@dataclass
class CostSummary:
    labor: float = 0
    material: float = 0
    equipment: float = 0
    subcontractor: float = 0
    other: float = 0

    @property
    def direct_cost(self) -> float:
        return self.labor + self.material + self.equipment + self.subcontractor + self.other


@dataclass
class Markup:
    name: str
    rate: float  # As decimal (0.10 = 10%)
    base: str = "direct"  # "direct" or "subtotal"


class EstimateBuilder:
    """Build construction project estimates."""

    def __init__(self, project_name: str, project_number: str = ""):
        self.project_name = project_name
        self.project_number = project_number
        self.estimate_date = date.today()
        self.items: List[EstimateLineItem] = []
        self.markups: List[Markup] = []
        self._next_line = 1

    def add_item(self,
                 wbs_code: str,
                 description: str,
                 quantity: float,
                 unit: str,
                 unit_cost: float,
                 category: CostCategory = CostCategory.OTHER,
                 notes: str = "") -> EstimateLineItem:
        """Add line item to estimate."""

        item = EstimateLineItem(
            line_number=self._next_line,
            wbs_code=wbs_code,
            description=description,
            quantity=quantity,
            unit=unit,
            unit_cost=unit_cost,
            category=category,
            notes=notes
        )
        self.items.append(item)
        self._next_line += 1
        return item

    def add_markup(self, name: str, rate: float, base: str = "direct"):
        """Add markup (overhead, profit, contingency, etc.)."""
        self.markups.append(Markup(name=name, rate=rate, base=base))

    def set_standard_markups(self,
                             overhead: float = 0.15,
                             profit: float = 0.10,
                             contingency: float = 0.05):
        """Set standard construction markups."""

        self.markups = [
            Markup("General Conditions / Overhead", overhead, "direct"),
            Markup("Profit", profit, "subtotal"),
            Markup("Contingency", contingency, "subtotal")
        ]

    def get_cost_summary(self) -> CostSummary:
        """Get cost summary by category."""

        summary = CostSummary()
        for item in self.items:
            cost = item.total_cost
            if item.category == CostCategory.LABOR:
                summary.labor += cost
            elif item.category == CostCategory.MATERIAL:
                summary.material += cost
            elif item.category == CostCategory.EQUIPMENT:
                summary.equipment += cost
            elif item.category == CostCategory.SUBCONTRACTOR:
                summary.subcontractor += cost
            else:
                summary.other += cost
        return summary

    def calculate_total(self) -> Dict[str, Any]:
        """Calculate total estimate with markups."""

        summary = self.get_cost_summary()
        direct_cost = summary.direct_cost

        markups_detail = []
        subtotal = direct_cost

        for markup in self.markups:
            if markup.base == "direct":
                amount = direct_cost * markup.rate
            else:
                amount = subtotal * markup.rate

            markups_detail.append({
                'name': markup.name,
                'rate': f"{markup.rate * 100:.1f}%",
                'amount': round(amount, 2)
            })
            subtotal += amount

        return {
            'cost_summary': {
                'labor': round(summary.labor, 2),
                'material': round(summary.material, 2),
                'equipment': round(summary.equipment, 2),
                'subcontractor': round(summary.subcontractor, 2),
                'other': round(summary.other, 2),
                'direct_cost': round(direct_cost, 2)
            },
            'markups': markups_detail,
            'total_markups': round(subtotal - direct_cost, 2),
            'grand_total': round(subtotal, 2)
        }

    def get_items_by_wbs(self) -> Dict[str, List[EstimateLineItem]]:
        """Group items by WBS code prefix."""

        by_wbs = {}
        for item in self.items:
            prefix = item.wbs_code.split('.')[0] if '.' in item.wbs_code else item.wbs_code
            if prefix not in by_wbs:
                by_wbs[prefix] = []
            by_wbs[prefix].append(item)
        return by_wbs

    def import_from_df(self, df: pd.DataFrame):
        """Import line items from DataFrame."""

        for _, row in df.iterrows():
            self.add_item(
                wbs_code=str(row.get('wbs_code', '')),
                description=row['description'],
                quantity=float(row['quantity']),
                unit=row['unit'],
                unit_cost=float(row['unit_cost']),
                category=CostCategory(row.get('category', 'other').lower()),
                notes=row.get('notes', '')
            )

    def export_to_df(self) -> pd.DataFrame:
        """Export estimate to DataFrame."""

        data = []
        for item in self.items:
            data.append({
                'Line': item.line_number,
                'WBS': item.wbs_code,
                'Description': item.description,
                'Qty': item.quantity,
                'Unit': item.unit,
                'Unit Cost': item.unit_cost,
                'Total': item.total_cost,
                'Category': item.category.value,
                'Notes': item.notes
            })
        return pd.DataFrame(data)

    def export_to_excel(self, output_path: str) -> str:
        """Export estimate to Excel."""

        totals = self.calculate_total()

        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Cover sheet
            cover_df = pd.DataFrame([{
                'Project Name': self.project_name,
                'Project Number': self.project_number,
                'Estimate Date': self.estimate_date,
                'Total Items': len(self.items),
                'Direct Cost': totals['cost_summary']['direct_cost'],
                'Grand Total': totals['grand_total']
            }])
            cover_df.to_excel(writer, sheet_name='Summary', index=False)

            # Line items
            items_df = self.export_to_df()
            items_df.to_excel(writer, sheet_name='Line Items', index=False)

            # Cost breakdown
            breakdown_df = pd.DataFrame([totals['cost_summary']])
            breakdown_df.to_excel(writer, sheet_name='Cost Breakdown', index=False)

            # Markups
            if totals['markups']:
                markups_df = pd.DataFrame(totals['markups'])
                markups_df.to_excel(writer, sheet_name='Markups', index=False)

        return output_path

    def validate(self) -> List[str]:
        """Validate estimate for common issues."""

        issues = []

        if not self.items:
            issues.append("Estimate has no line items")

        for item in self.items:
            if item.quantity <= 0:
                issues.append(f"Line {item.line_number}: Invalid quantity")
            if item.unit_cost < 0:
                issues.append(f"Line {item.line_number}: Negative unit cost")
            if not item.description:
                issues.append(f"Line {item.line_number}: Missing description")

        if not self.markups:
            issues.append("No markups defined (overhead, profit)")

        return issues

Read the full file on GitHub · 323 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. 9d ago First seen · 323 lines · 23 tokens per session scan A e8dea0e800b6

Subscribe to this mod's changes

estimate-builder 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 23 tokens to every session and 2,263 once invoked, about $0.0001 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 2026-09-03.

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