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.
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill labor-rategit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote 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.
[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/labor-rate)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- labor-rate — 100% identical, 0 lines differ
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'
)
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.
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.
- 8d ago First seen · 357 lines · 23 tokens per session scan A 07bbd28b30c4
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.
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…
"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…
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..
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..
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'.
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…