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 carbon-calculatorgit 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/carbon-calculator)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/carbon-calculator"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/carbon-calculator/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/carbon-calculator"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/carbon-calculator.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 | $0.00024 | $0.03343 |
| Opus 5 | $0.00012 | $0.01672 |
| Sonnet 5 | $0.00005 | $0.00669 |
| Haiku 4.5 | $0.00002 | $0.00334 |
Grade A, and why
carbon-calculator 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:
- carbon-calculator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 419 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Carbon Calculator
Business Case
Problem Statement
Sustainability requirements demand:
- Tracking embodied carbon
- Comparing material options
- Meeting carbon targets
- Reporting emissions
Solution
Calculate and track embodied carbon for construction materials using standard emission factors.
Technical Implementation
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass
from enum import Enum
class MaterialCategory(Enum):
CONCRETE = "concrete"
STEEL = "steel"
ALUMINUM = "aluminum"
TIMBER = "timber"
BRICK = "brick"
GLASS = "glass"
INSULATION = "insulation"
PLASTIC = "plastic"
COPPER = "copper"
OTHER = "other"
@dataclass
class CarbonFactor:
material: str
category: MaterialCategory
ec_factor: float # kgCO2e per unit
unit: str
source: str
@dataclass
class MaterialInput:
material_code: str
material_name: str
quantity: float
unit: str
category: MaterialCategory
@dataclass
class CarbonResult:
material_code: str
material_name: str
quantity: float
unit: str
ec_factor: float
embodied_carbon: float # kgCO2e
category: str
# Embodied carbon factors (kgCO2e per unit)
CARBON_FACTORS = {
# Concrete
'concrete_c20': CarbonFactor('Concrete C20', MaterialCategory.CONCRETE, 240, 'm3', 'ICE Database'),
'concrete_c30': CarbonFactor('Concrete C30', MaterialCategory.CONCRETE, 290, 'm3', 'ICE Database'),
'concrete_c40': CarbonFactor('Concrete C40', MaterialCategory.CONCRETE, 350, 'm3', 'ICE Database'),
'concrete_c50': CarbonFactor('Concrete C50', MaterialCategory.CONCRETE, 410, 'm3', 'ICE Database'),
# Steel
'steel_rebar': CarbonFactor('Rebar', MaterialCategory.STEEL, 1.99, 'kg', 'ICE Database'),
'steel_section': CarbonFactor('Steel Section', MaterialCategory.STEEL, 1.55, 'kg', 'ICE Database'),
'steel_sheet': CarbonFactor('Steel Sheet', MaterialCategory.STEEL, 2.03, 'kg', 'ICE Database'),
'steel_stainless': CarbonFactor('Stainless Steel', MaterialCategory.STEEL, 6.15, 'kg', 'ICE Database'),
# Aluminum
'aluminum_general': CarbonFactor('Aluminum General', MaterialCategory.ALUMINUM, 9.16, 'kg', 'ICE Database'),
'aluminum_recycled': CarbonFactor('Aluminum Recycled', MaterialCategory.ALUMINUM, 1.81, 'kg', 'ICE Database'),
# Timber
'timber_softwood': CarbonFactor('Softwood Timber', MaterialCategory.TIMBER, 0.31, 'kg', 'ICE Database'),
'timber_hardwood': CarbonFactor('Hardwood Timber', MaterialCategory.TIMBER, 0.46, 'kg', 'ICE Database'),
'timber_glulam': CarbonFactor('Glulam', MaterialCategory.TIMBER, 0.51, 'kg', 'ICE Database'),
'timber_clt': CarbonFactor('CLT', MaterialCategory.TIMBER, 0.44, 'kg', 'ICE Database'),
'timber_plywood': CarbonFactor('Plywood', MaterialCategory.TIMBER, 0.65, 'kg', 'ICE Database'),
# Masonry
'brick_common': CarbonFactor('Common Brick', MaterialCategory.BRICK, 0.24, 'kg', 'ICE Database'),
'block_concrete': CarbonFactor('Concrete Block', MaterialCategory.BRICK, 0.10, 'kg', 'ICE Database'),
# Glass
'glass_float': CarbonFactor('Float Glass', MaterialCategory.GLASS, 1.44, 'kg', 'ICE Database'),
'glass_double': CarbonFactor('Double Glazing', MaterialCategory.GLASS, 35.0, 'm2', 'ICE Database'),
# Insulation
'insul_mineral': CarbonFactor('Mineral Wool', MaterialCategory.INSULATION, 1.28, 'kg', 'ICE Database'),
'insul_eps': CarbonFactor('EPS', MaterialCategory.INSULATION, 3.29, 'kg', 'ICE Database'),
'insul_xps': CarbonFactor('XPS', MaterialCategory.INSULATION, 3.29, 'kg', 'ICE Database'),
# Other
'copper_pipe': CarbonFactor('Copper Pipe', MaterialCategory.COPPER, 2.71, 'kg', 'ICE Database'),
'pvc_pipe': CarbonFactor('PVC Pipe', MaterialCategory.PLASTIC, 3.10, 'kg', 'ICE Database'),
}
class CarbonCalculator:
"""Calculate embodied carbon for construction."""
def __init__(self, project_name: str):
self.project_name = project_name
self.materials: List[MaterialInput] = []
self.results: List[CarbonResult] = []
self.custom_factors: Dict[str, CarbonFactor] = {}
def add_custom_factor(self,
code: str,
name: str,
category: MaterialCategory,
ec_factor: float,
unit: str,
source: str = "Custom"):
"""Add custom carbon factor."""
self.custom_factors[code] = CarbonFactor(
material=name,
category=category,
ec_factor=ec_factor,
unit=unit,
source=source
)
def get_factor(self, material_code: str) -> Optional[CarbonFactor]:
"""Get carbon factor for material."""
# Check custom first
if material_code in self.custom_factors:
return self.custom_factors[material_code]
# Check standard factors
code_lower = material_code.lower().replace('-', '_').replace(' ', '_')
return CARBON_FACTORS.get(code_lower)
def add_material(self,
material_code: str,
material_name: str,
quantity: float,
unit: str,
category: MaterialCategory = MaterialCategory.OTHER):
"""Add material to calculation."""
self.materials.append(MaterialInput(
material_code=material_code,
material_name=material_name,
quantity=quantity,
unit=unit,
category=category
))
def calculate(self) -> List[CarbonResult]:
"""Calculate embodied carbon for all materials."""
self.results = []
for mat in self.materials:
factor = self.get_factor(mat.material_code)
if factor:
# Check unit compatibility
if factor.unit == mat.unit:
ec = mat.quantity * factor.ec_factor
else:
# Assume conversion needed - simplified
ec = mat.quantity * factor.ec_factor
else:
# Use default factor based on category
default_factors = {
MaterialCategory.CONCRETE: 300,
MaterialCategory.STEEL: 1.8,
MaterialCategory.ALUMINUM: 9.0,
MaterialCategory.TIMBER: 0.4,
MaterialCategory.BRICK: 0.2,
MaterialCategory.GLASS: 1.5,
MaterialCategory.INSULATION: 2.0,
MaterialCategory.OTHER: 1.0
}
ec_factor = default_factors.get(mat.category, 1.0)
ec = mat.quantity * ec_factor
self.results.append(CarbonResult(
material_code=mat.material_code,
material_name=mat.material_name,
quantity=mat.quantity,
unit=mat.unit,
ec_factor=factor.ec_factor if factor else 0,
embodied_carbon=round(ec, 2),
category=mat.category.value
))
return self.results
def get_total_carbon(self) -> float:
"""Get total embodied carbon (kgCO2e)."""
return sum(r.embodied_carbon for r in self.results)
def get_carbon_by_category(self) -> Dict[str, float]:
"""Get carbon breakdown by category."""
by_category = {}
for r in self.results:
if r.category not in by_category:
by_category[r.category] = 0
by_category[r.category] += r.embodied_carbon
return {k: round(v, 2) for k, v in by_category.items()}
def compare_alternatives(self,
original_code: str,
original_qty: float,
alternative_code: str,
alternative_qty: float) -> Dict[str, Any]:
"""Compare carbon impact of material alternatives."""
original_factor = self.get_factor(original_code)
alt_factor = self.get_factor(alternative_code)
if not original_factor or not alt_factor:
return {}
original_carbon = original_qty * original_factor.ec_factor
alt_carbon = alternative_qty * alt_factor.ec_factor
savings = original_carbon - alt_carbon
return {
'original_material': original_factor.material,
'original_carbon': round(original_carbon, 2),
'alternative_material': alt_factor.material,
'alternative_carbon': round(alt_carbon, 2),
'carbon_savings': round(savings, 2),
'savings_percent': round(savings / original_carbon * 100, 1) if original_carbon > 0 else 0
}
def generate_report(self) -> Dict[str, Any]:
"""Generate carbon report."""
if not self.results:
self.calculate()
total = self.get_total_carbon()
by_category = self.get_carbon_by_category()
# Find top contributors
sorted_results = sorted(self.results, key=lambda x: x.embodied_carbon, reverse=True)
top_5 = sorted_results[:5]
# Convert to tonnes
total_tonnes = total / 1000
return {
'project': self.project_name,
'total_kgCO2e': round(total, 2),
'total_tCO2e': round(total_tonnes, 2),
'material_count': len(self.results),
'by_category': by_category,
'top_contributors': [
{
'material': r.material_name,
'carbon': r.embodied_carbon,
'percentage': round(r.embodied_carbon / total * 100, 1) if total > 0 else 0
}
for r in top_5
]
}
def suggest_reductions(self) -> List[Dict[str, Any]]:
"""Suggest carbon reduction opportunities."""
if not self.results:
self.calculate()
suggestions = []
for r in self.results:
# Steel -> Timber
if r.category == 'steel' and r.embodied_carbon > 1000:
suggestions.append({
'material': r.material_name,
'current_carbon': r.embodied_carbon,
'suggestion': 'Consider timber alternative where structurally feasible',
'potential_reduction': '60-80%'
})
# Standard concrete -> Low carbon
if r.category == 'concrete' and r.embodied_carbon > 5000:
suggestions.append({
'material': r.material_name,
'current_carbon': r.embodied_carbon,
'suggestion': 'Use low-carbon concrete mix with SCMs',
'potential_reduction': '20-40%'
})
# Virgin aluminum -> Recycled
if r.category == 'aluminum':
suggestions.append({
'material': r.material_name,
'current_carbon': r.embodied_carbon,
'suggestion': 'Specify recycled aluminum content',
'potential_reduction': '70-80%'
})
return suggestions
def export_to_excel(self, output_path: str) -> str:
"""Export carbon calculation to Excel."""
if not self.results:
self.calculate()
report = self.generate_report()
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary_df = pd.DataFrame([{
'Project': self.project_name,
'Total kgCO2e': report['total_kgCO2e'],
'Total tCO2e': report['total_tCO2e'],
'Materials': report['material_count']
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Details
details_df = pd.DataFrame([
{
'Material Code': r.material_code,
'Material': r.material_name,
'Quantity': r.quantity,
'Unit': r.unit,
'EC Factor': r.ec_factor,
'Embodied Carbon (kgCO2e)': r.embodied_carbon,
'Category': r.category
}
for r in self.results
])
details_df.to_excel(writer, sheet_name='Materials', index=False)
# By Category
cat_df = pd.DataFrame([
{'Category': k, 'kgCO2e': v}
for k, v in report['by_category'].items()
])
cat_df.to_excel(writer, sheet_name='By Category', index=False)
# Suggestions
suggestions = self.suggest_reductions()
if suggestions:
sug_df = pd.DataFrame(suggestions)
sug_df.to_excel(writer, sheet_name='Reduction Ideas', 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 · 419 lines · 24 tokens per session scan A f97b87df60d8
carbon-calculator 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 24 tokens to every session and 3,343 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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