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 cost-estimation-resourcegit 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/cost-estimation-resource)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cost-estimation-resource"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cost-estimation-resource/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/cost-estimation-resource"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cost-estimation-resource.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 | $0.00028 | $0.02783 |
| Opus 5 | $0.00014 | $0.01392 |
| Sonnet 5 | $0.00006 | $0.00557 |
| Haiku 4.5 | $0.00003 | $0.00278 |
Grade A, and why
cost-estimation-resource 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- cost-estimation-resource — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 359 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cost Estimation - Resource Method
Business Case
Problem Statement
Traditional costing challenges:
- Fixed unit prices become outdated
- No visibility into cost components
- Difficult to adjust for conditions
- Limited cost analysis capability
Solution
Resource-based costing separates physical resource consumption (norms) from prices, enabling accurate, adjustable, and transparent cost estimation.
Technical Implementation
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
class ResourceType(Enum):
LABOR = "labor"
MATERIAL = "material"
EQUIPMENT = "equipment"
SUBCONTRACTOR = "subcontractor"
@dataclass
class Resource:
code: str
name: str
resource_type: ResourceType
unit: str
unit_price: float
currency: str = "USD"
@dataclass
class ResourceNorm:
resource_code: str
consumption: float # Units per work item unit
waste_factor: float = 1.0 # 1.1 = 10% waste
@dataclass
class WorkItem:
code: str
name: str
unit: str
resources: List[ResourceNorm] = field(default_factory=list)
@dataclass
class CostLineItem:
work_item_code: str
work_item_name: str
quantity: float
unit: str
labor_cost: float
material_cost: float
equipment_cost: float
subcontractor_cost: float
total_cost: float
class ResourceBasedEstimator:
"""Calculate costs using resource-based method."""
def __init__(self):
self.resources: Dict[str, Resource] = {}
self.work_items: Dict[str, WorkItem] = {}
self.overhead_rate: float = 0.15
self.profit_rate: float = 0.10
def add_resource(self, resource: Resource):
"""Add resource to database."""
self.resources[resource.code] = resource
def add_work_item(self, work_item: WorkItem):
"""Add work item with resource norms."""
self.work_items[work_item.code] = work_item
def load_resources_from_df(self, df: pd.DataFrame):
"""Load resources from DataFrame."""
for _, row in df.iterrows():
resource = Resource(
code=row['code'],
name=row['name'],
resource_type=ResourceType(row['type'].lower()),
unit=row['unit'],
unit_price=float(row['unit_price']),
currency=row.get('currency', 'USD')
)
self.add_resource(resource)
def load_work_items_from_df(self, items_df: pd.DataFrame, norms_df: pd.DataFrame):
"""Load work items and norms from DataFrames."""
# Group norms by work item
norms_grouped = norms_df.groupby('work_item_code')
for _, row in items_df.iterrows():
code = row['code']
resources = []
if code in norms_grouped.groups:
item_norms = norms_grouped.get_group(code)
for _, norm_row in item_norms.iterrows():
resources.append(ResourceNorm(
resource_code=norm_row['resource_code'],
consumption=float(norm_row['consumption']),
waste_factor=float(norm_row.get('waste_factor', 1.0))
))
work_item = WorkItem(
code=code,
name=row['name'],
unit=row['unit'],
resources=resources
)
self.add_work_item(work_item)
def calculate_work_item_cost(self, work_item_code: str, quantity: float) -> CostLineItem:
"""Calculate cost for a work item quantity."""
if work_item_code not in self.work_items:
raise ValueError(f"Work item {work_item_code} not found")
work_item = self.work_items[work_item_code]
labor_cost = 0.0
material_cost = 0.0
equipment_cost = 0.0
subcontractor_cost = 0.0
for norm in work_item.resources:
if norm.resource_code not in self.resources:
continue
resource = self.resources[norm.resource_code]
resource_qty = quantity * norm.consumption * norm.waste_factor
resource_cost = resource_qty * resource.unit_price
if resource.resource_type == ResourceType.LABOR:
labor_cost += resource_cost
elif resource.resource_type == ResourceType.MATERIAL:
material_cost += resource_cost
elif resource.resource_type == ResourceType.EQUIPMENT:
equipment_cost += resource_cost
elif resource.resource_type == ResourceType.SUBCONTRACTOR:
subcontractor_cost += resource_cost
total = labor_cost + material_cost + equipment_cost + subcontractor_cost
return CostLineItem(
work_item_code=work_item_code,
work_item_name=work_item.name,
quantity=quantity,
unit=work_item.unit,
labor_cost=round(labor_cost, 2),
material_cost=round(material_cost, 2),
equipment_cost=round(equipment_cost, 2),
subcontractor_cost=round(subcontractor_cost, 2),
total_cost=round(total, 2)
)
def calculate_estimate(self, items: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Calculate full estimate from list of items."""
line_items = []
totals = {
'labor': 0.0,
'material': 0.0,
'equipment': 0.0,
'subcontractor': 0.0,
'direct': 0.0
}
for item in items:
code = item['work_item_code']
qty = float(item['quantity'])
line = self.calculate_work_item_cost(code, qty)
line_items.append(line)
totals['labor'] += line.labor_cost
totals['material'] += line.material_cost
totals['equipment'] += line.equipment_cost
totals['subcontractor'] += line.subcontractor_cost
totals['direct'] += line.total_cost
# Calculate overhead and profit
overhead = totals['direct'] * self.overhead_rate
subtotal = totals['direct'] + overhead
profit = subtotal * self.profit_rate
grand_total = subtotal + profit
return {
'line_items': line_items,
'totals': {
'labor': round(totals['labor'], 2),
'material': round(totals['material'], 2),
'equipment': round(totals['equipment'], 2),
'subcontractor': round(totals['subcontractor'], 2),
'direct_cost': round(totals['direct'], 2),
'overhead': round(overhead, 2),
'overhead_rate': self.overhead_rate,
'subtotal': round(subtotal, 2),
'profit': round(profit, 2),
'profit_rate': self.profit_rate,
'grand_total': round(grand_total, 2)
},
'summary': {
'item_count': len(line_items),
'labor_pct': round(totals['labor'] / totals['direct'] * 100, 1) if totals['direct'] > 0 else 0,
'material_pct': round(totals['material'] / totals['direct'] * 100, 1) if totals['direct'] > 0 else 0,
'equipment_pct': round(totals['equipment'] / totals['direct'] * 100, 1) if totals['direct'] > 0 else 0
}
}
def adjust_prices(self, factor: float, resource_type: ResourceType = None):
"""Adjust resource prices by factor."""
for code, resource in self.resources.items():
if resource_type is None or resource.resource_type == resource_type:
resource.unit_price *= factor
def apply_regional_factor(self, factor: float):
"""Apply regional cost factor to all resources."""
self.adjust_prices(factor)
def get_resource_breakdown(self, work_item_code: str, quantity: float) -> pd.DataFrame:
"""Get detailed resource breakdown for work item."""
if work_item_code not in self.work_items:
return pd.DataFrame()
work_item = self.work_items[work_item_code]
data = []
for norm in work_item.resources:
if norm.resource_code not in self.resources:
continue
resource = self.resources[norm.resource_code]
resource_qty = quantity * norm.consumption * norm.waste_factor
resource_cost = resource_qty * resource.unit_price
data.append({
'Resource Code': resource.code,
'Resource Name': resource.name,
'Type': resource.resource_type.value,
'Unit': resource.unit,
'Consumption': norm.consumption,
'Waste Factor': norm.waste_factor,
'Total Qty': round(resource_qty, 3),
'Unit Price': resource.unit_price,
'Total Cost': round(resource_cost, 2)
})
return pd.DataFrame(data)
def export_to_excel(self, estimate: Dict[str, Any], output_path: str) -> str:
"""Export estimate to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary_df = pd.DataFrame([estimate['totals']])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Line items
items_data = [{
'Code': item.work_item_code,
'Description': item.work_item_name,
'Quantity': item.quantity,
'Unit': item.unit,
'Labor': item.labor_cost,
'Material': item.material_cost,
'Equipment': item.equipment_cost,
'Subcontractor': item.subcontractor_cost,
'Total': item.total_cost
} for item in estimate['line_items']]
items_df = pd.DataFrame(items_data)
items_df.to_excel(writer, sheet_name='Line Items', index=False)
return output_path
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.
- 9d ago First seen · 359 lines · 28 tokens per session scan A cfaad008d3f7
cost-estimation-resource 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 28 tokens to every session and 2,783 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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