labor-rate

labor-rate is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 23 tokens per session (2,581 once invoked), scanned A, original, MIT.

A calculator for the hourly cost of construction workers and crews, including wages, benefits, taxes, insurance, overhead, profit, location, and productivity.

In plain words
What is it for?
Use it to price labor for estimates, compare crew setups, and calculate rates for work such as new construction, renovation, demolition, or maintenance.
Why use it?
It avoids assembling these cost parts by hand and helps account for regional rates and different kinds of construction 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 to price labor for estimates, compare crew setups, and calculate rates for work such as new construction, renovation, demolition, or maintenance.

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

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/labor-rate"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/labor-rate.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 2,581 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 $0.02581
Opus 5 $0.00012 $0.01290
Sonnet 5 $0.00005 $0.00516
Haiku 4.5 $0.00002 $0.00258

Measured 8d ago against content hash 07bbd28b30c4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

labor-rate 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 8d 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/labor-rate/SKILL.md · 357 lines

How it starts

The opening of the file, as written. The whole thing — 357 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Labor Rate Calculator

Overview

Labor costs account for 30-50% of construction costs. This skill calculates all-in labor rates including wages, benefits, overhead, and regional adjustments.

Python Implementation

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


class LaborCategory(Enum):
    """Labor skill categories."""
    LABORER = "laborer"
    CARPENTER = "carpenter"
    ELECTRICIAN = "electrician"
    PLUMBER = "plumber"
    IRONWORKER = "ironworker"
    MASON = "mason"
    OPERATOR = "equipment_operator"
    FOREMAN = "foreman"
    SUPERINTENDENT = "superintendent"


class WorkType(Enum):
    """Work type for productivity."""
    NEW_CONSTRUCTION = "new"
    RENOVATION = "renovation"
    DEMOLITION = "demolition"
    MAINTENANCE = "maintenance"


@dataclass
class LaborRate:
    """Complete labor rate breakdown."""
    category: str
    base_wage: float
    benefits: float
    taxes: float
    insurance: float
    overhead: float
    profit: float
    total_rate: float
    unit: str = "hour"


@dataclass
class CrewComposition:
    """Crew composition for work."""
    name: str
    workers: List[Dict[str, Any]]
    total_hourly_cost: float
    output_per_hour: float
    unit: str


class LaborRateCalculator:
    """Calculate construction labor rates."""

    # Default burden rates (percent of base wage)
    DEFAULT_BURDENS = {
        'benefits': 0.30,        # Health, pension, vacation
        'taxes': 0.10,           # FICA, unemployment
        'insurance': 0.08,       # Workers comp, liability
        'overhead': 0.15,        # General conditions
        'profit': 0.10           # Contractor profit
    }

    # Base wages by category (USD/hour, US average)
    BASE_WAGES = {
        LaborCategory.LABORER: 22,
        LaborCategory.CARPENTER: 32,
        LaborCategory.ELECTRICIAN: 38,
        LaborCategory.PLUMBER: 36,
        LaborCategory.IRONWORKER: 35,
        LaborCategory.MASON: 34,
        LaborCategory.OPERATOR: 40,
        LaborCategory.FOREMAN: 45,
        LaborCategory.SUPERINTENDENT: 55
    }

    # Regional factors
    REGIONAL_FACTORS = {
        'US_National': 1.00,
        'New_York': 1.45,
        'San_Francisco': 1.40,
        'Chicago': 1.15,
        'Houston': 0.95,
        'Atlanta': 0.90,
        'Germany_Berlin': 1.20,
        'UK_London': 1.35
    }

    def __init__(self, burden_rates: Dict[str, float] = None):
        self.burdens = burden_rates or self.DEFAULT_BURDENS

    def calculate_rate(self, category: LaborCategory,
                       region: str = 'US_National',
                       custom_wage: float = None) -> LaborRate:
        """Calculate all-in labor rate."""

        # Get base wage
        base = custom_wage or self.BASE_WAGES.get(category, 25)

        # Apply regional factor
        regional_factor = self.REGIONAL_FACTORS.get(region, 1.0)
        base *= regional_factor

        # Calculate burden components
        benefits = base * self.burdens['benefits']
        taxes = base * self.burdens['taxes']
        insurance = base * self.burdens['insurance']

        # Subtotal before markup
        subtotal = base + benefits + taxes + insurance

        # Overhead and profit
        overhead = subtotal * self.burdens['overhead']
        profit = (subtotal + overhead) * self.burdens['profit']

        total = subtotal + overhead + profit

        return LaborRate(
            category=category.value,
            base_wage=round(base, 2),
            benefits=round(benefits, 2),
            taxes=round(taxes, 2),
            insurance=round(insurance, 2),
            overhead=round(overhead, 2),
            profit=round(profit, 2),
            total_rate=round(total, 2)
        )

    def calculate_crew_cost(self, composition: Dict[LaborCategory, int],
                            region: str = 'US_National') -> float:
        """Calculate hourly cost for crew composition."""

        total = 0
        for category, count in composition.items():
            rate = self.calculate_rate(category, region)
            total += rate.total_rate * count

        return round(total, 2)

    def get_rate_table(self, region: str = 'US_National') -> pd.DataFrame:
        """Generate rate table for all categories."""

        rates = []
        for category in LaborCategory:
            rate = self.calculate_rate(category, region)
            rates.append({
                'category': rate.category,
                'base_wage': rate.base_wage,
                'benefits': rate.benefits,
                'taxes': rate.taxes,
                'insurance': rate.insurance,
                'overhead': rate.overhead,
                'profit': rate.profit,
                'total_rate': rate.total_rate
            })

        return pd.DataFrame(rates)


class ProductivityFactor:
    """Calculate productivity factors for labor."""

    # Base productivity factors
    WORK_TYPE_FACTORS = {
        WorkType.NEW_CONSTRUCTION: 1.0,
        WorkType.RENOVATION: 0.75,
        WorkType.DEMOLITION: 0.90,
        WorkType.MAINTENANCE: 0.65
    }

    # Condition factors
    CONDITION_FACTORS = {
        'ideal': 1.0,
        'normal': 0.90,
        'difficult': 0.75,
        'hazardous': 0.60,
        'confined_space': 0.50
    }

    # Weather factors
    WEATHER_FACTORS = {
        'clear': 1.0,
        'hot': 0.85,
        'cold': 0.80,
        'rain': 0.60,
        'wind': 0.75
    }

    def calculate_factor(self, work_type: WorkType,
                         condition: str = 'normal',
                         weather: str = 'clear',
                         overtime_hours: int = 0) -> float:
        """Calculate combined productivity factor."""

        base = self.WORK_TYPE_FACTORS.get(work_type, 1.0)
        cond = self.CONDITION_FACTORS.get(condition, 0.9)
        weath = self.WEATHER_FACTORS.get(weather, 1.0)

        # Overtime degradation (productivity drops after 8 hours)
        overtime_factor = 1.0
        if overtime_hours > 0:
            # Each OT hour is ~15% less productive
            overtime_factor = 1 - (overtime_hours * 0.015)

        combined = base * cond * weath * overtime_factor
        return round(max(combined, 0.3), 2)  # Minimum 30% productivity

    def adjust_labor_hours(self, base_hours: float,
                           work_type: WorkType,
                           condition: str = 'normal',
                           weather: str = 'clear') -> float:
        """Adjust labor hours for conditions."""

        factor = self.calculate_factor(work_type, condition, weather)
        return round(base_hours / factor, 1)


class CrewBuilder:
    """Build and optimize crew compositions."""

    # Standard crew compositions
    STANDARD_CREWS = {
        'concrete_pour': {
            LaborCategory.FOREMAN: 1,
            LaborCategory.CARPENTER: 2,
            LaborCategory.LABORER: 4,
            LaborCategory.OPERATOR: 1
        },
        'framing': {
            LaborCategory.FOREMAN: 1,
            LaborCategory.CARPENTER: 4,
            LaborCategory.LABORER: 2
        },
        'electrical_rough': {
            LaborCategory.FOREMAN: 1,
            LaborCategory.ELECTRICIAN: 3,
            LaborCategory.LABORER: 1
        },
        'plumbing_rough': {
            LaborCategory.FOREMAN: 1,
            LaborCategory.PLUMBER: 2,
            LaborCategory.LABORER: 1
        },
        'masonry': {
            LaborCategory.FOREMAN: 1,
            LaborCategory.MASON: 4,
            LaborCategory.LABORER: 4
        }
    }

    def __init__(self, rate_calculator: LaborRateCalculator):
        self.calc = rate_calculator

    def get_crew(self, work_type: str,
                 region: str = 'US_National') -> CrewComposition:
        """Get standard crew composition with costs."""

        if work_type not in self.STANDARD_CREWS:
            raise ValueError(f"Unknown work type: {work_type}")

        composition = self.STANDARD_CREWS[work_type]
        total_cost = self.calc.calculate_crew_cost(composition, region)

        workers = []
        for category, count in composition.items():
            rate = self.calc.calculate_rate(category, region)
            workers.append({
                'category': category.value,
                'count': count,
                'hourly_rate': rate.total_rate,
                'subtotal': rate.total_rate * count
            })

        return CrewComposition(
            name=work_type,
            workers=workers,
            total_hourly_cost=total_cost,
            output_per_hour=1.0,  # Placeholder
            unit='hour'
        )

    def custom_crew(self, workers: Dict[LaborCategory, int],
                    region: str = 'US_National') -> CrewComposition:
        """Build custom crew composition."""

        total_cost = self.calc.calculate_crew_cost(workers, region)

        worker_list = []
        for category, count in workers.items():
            rate = self.calc.calculate_rate(category, region)
            worker_list.append({
                'category': category.value,
                'count': count,
                'hourly_rate': rate.total_rate,
                'subtotal': rate.total_rate * count
            })

        return CrewComposition(
            name='custom',
            workers=worker_list,
            total_hourly_cost=total_cost,
            output_per_hour=1.0,
            unit='hour'
        )

Read the full file on GitHub · 357 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. 8d ago First seen · 357 lines · 23 tokens per session scan A 07bbd28b30c4

Subscribe to this mod's changes

labor-rate is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (310 stars, last pushed 20d ago), licensed MIT. It adds 23 tokens to every session and 2,581 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.

Related

Other skills, from other repositories

stripe-payments

Add Stripe payments to a web app — Checkout Sessions, Payment Intents, subscriptions, webhooks, customer portal, and pricing pages. Covers the decision of which Stripe API to use, produces working integration code, and handles webhook verification. No MCP server needed — uses Stripe npm package directly. Triggers…

jezweb/claude-skills · 101 tokens

"biz-management-accounting"

"Management accounting toolkit for internal decision support: ABC costing, variance analysis, transfer pricing, and responsibility accounting. Use for product profitability disputes, budget variance diagnosis, inter-division pricing design, and business-unit manager performance evaluation. Triggers…

charlieviettq/awesome-agent-skill · 148 tokens

actuarial-modeling

Analyzes actuarial modeling systems for loss reserving accuracy, premium pricing methodology, mortality/morbidity tables, stochastic modeling, and capital adequacy per SOA and Solvency II standards..

tinh2/skills-hub-registry · 45 tokens

asset-lifecycle

Analyzes asset lifecycle planning systems for capital expenditure forecasting, replacement scheduling, total cost of ownership modeling, depreciation tracking, and facility condition assessments using IFMA standards and Facility Condition Index scoring..

tinh2/skills-hub-registry · 41 tokens

commodity-pricing

Analyze commodity pricing and trading systems including forward curves, option models, position management, risk metrics, and regulatory reporting. Triggers: 'review pricing models', 'audit trading system', 'evaluate VaR implementation', 'check commodity risk management'.

tinh2/skills-hub-registry · 52 tokens

fraud-detection

Analyze fraud detection systems including rule engines, ML scoring models, real-time transaction monitoring, alert triage workflows, false positive management, SAR/CTR regulatory reporting, adversarial robustness testing, and adaptive retraining pipelines for payment fraud, account takeover, identity theft, and AML…

tinh2/skills-hub-registry · 61 tokens