Getting it into your agent
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npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill lifecycle-carbon-calculatorgit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote this? Show the measurements
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[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/lifecycle-carbon-calculator)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/lifecycle-carbon-calculator"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/lifecycle-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/lifecycle-carbon-calculator"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/lifecycle-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 | $0.00026 | $0.04385 |
| Opus 5 | $0.00013 | $0.02193 |
| Sonnet 5 | $0.00005 | $0.00877 |
| Haiku 4.5 | $0.00003 | $0.00439 |
Grade A, and why
lifecycle-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:
- lifecycle-carbon-calculator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 466 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Lifecycle Carbon Calculator for Construction
Overview
Calculate embodied carbon (EC) and lifecycle carbon emissions for construction materials, assemblies, and projects. Support sustainable design decisions and carbon reduction targets.
Business Case
Carbon calculation supports:
- Regulatory Compliance: Meet carbon reporting requirements
- Green Certifications: LEED, BREEAM, Living Building Challenge
- Design Optimization: Choose lower-carbon alternatives
- Sustainability Goals: Track progress toward net-zero
Technical Implementation
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional
from enum import Enum
import pandas as pd
class LifecycleStage(Enum):
A1_A3 = "Product Stage (A1-A3)" # Raw materials, transport, manufacturing
A4 = "Transport to Site (A4)"
A5 = "Construction (A5)"
B1_B7 = "Use Stage (B1-B7)" # Maintenance, repair, replacement
C1_C4 = "End of Life (C1-C4)" # Demolition, transport, disposal
D = "Beyond Lifecycle (D)" # Reuse, recycling potential
@dataclass
class MaterialCarbon:
material_id: str
name: str
category: str
unit: str
carbon_a1_a3: float # kgCO2e per unit
carbon_a4: float
carbon_a5: float
carbon_b: float
carbon_c: float
carbon_d: float # Usually negative (credit)
density: float # kg/m³ if applicable
source: str
epd_url: str = ""
@dataclass
class AssemblyCarbon:
assembly_id: str
name: str
materials: List[Dict[str, Any]]
total_carbon: float
carbon_by_stage: Dict[str, float]
@dataclass
class ProjectCarbon:
project_id: str
name: str
gross_area: float
assemblies: List[AssemblyCarbon]
total_embodied_carbon: float
carbon_per_area: float
carbon_by_stage: Dict[str, float]
carbon_by_category: Dict[str, float]
benchmark_comparison: Dict[str, Any]
class LifecycleCarbonCalculator:
"""Calculate lifecycle carbon for construction."""
# Sample material carbon data (kgCO2e per unit)
DEFAULT_MATERIALS = {
'concrete_30mpa': MaterialCarbon(
material_id='C30', name='Concrete 30MPa', category='Concrete',
unit='m³', carbon_a1_a3=300, carbon_a4=5, carbon_a5=2,
carbon_b=0, carbon_c=10, carbon_d=-20, density=2400,
source='EPD Database'
),
'concrete_40mpa': MaterialCarbon(
material_id='C40', name='Concrete 40MPa', category='Concrete',
unit='m³', carbon_a1_a3=350, carbon_a4=5, carbon_a5=2,
carbon_b=0, carbon_c=10, carbon_d=-20, density=2400,
source='EPD Database'
),
'steel_rebar': MaterialCarbon(
material_id='REBAR', name='Steel Reinforcing Bar', category='Steel',
unit='kg', carbon_a1_a3=1.99, carbon_a4=0.05, carbon_a5=0.02,
carbon_b=0, carbon_c=0.05, carbon_d=-0.5, density=7850,
source='WorldSteel EPD'
),
'steel_structural': MaterialCarbon(
material_id='STEEL', name='Structural Steel', category='Steel',
unit='kg', carbon_a1_a3=1.55, carbon_a4=0.05, carbon_a5=0.03,
carbon_b=0, carbon_c=0.05, carbon_d=-0.8, density=7850,
source='AISC EPD'
),
'timber_clt': MaterialCarbon(
material_id='CLT', name='Cross-Laminated Timber', category='Timber',
unit='m³', carbon_a1_a3=-500, carbon_a4=10, carbon_a5=5,
carbon_b=0, carbon_c=50, carbon_d=-100, density=500,
source='AWC EPD'
),
'gypsum_board': MaterialCarbon(
material_id='GYP', name='Gypsum Board 12.5mm', category='Finishes',
unit='m²', carbon_a1_a3=3.2, carbon_a4=0.2, carbon_a5=0.1,
carbon_b=0, carbon_c=0.3, carbon_d=-0.1, density=10,
source='EUROGYPSUM EPD'
),
'insulation_mineral': MaterialCarbon(
material_id='INS_MW', name='Mineral Wool Insulation', category='Insulation',
unit='m³', carbon_a1_a3=45, carbon_a4=2, carbon_a5=1,
carbon_b=0, carbon_c=5, carbon_d=-2, density=40,
source='EURIMA EPD'
),
'glass_double': MaterialCarbon(
material_id='GLASS', name='Double Glazed Unit', category='Glazing',
unit='m²', carbon_a1_a3=35, carbon_a4=1, carbon_a5=0.5,
carbon_b=0, carbon_c=2, carbon_d=-5, density=25,
source='Glass for Europe EPD'
),
'aluminum': MaterialCarbon(
material_id='ALU', name='Aluminum Profile', category='Metals',
unit='kg', carbon_a1_a3=8.0, carbon_a4=0.1, carbon_a5=0.05,
carbon_b=0, carbon_c=0.1, carbon_d=-4.0, density=2700,
source='EAA EPD'
),
}
# Building type benchmarks (kgCO2e/m²)
BENCHMARKS = {
'Office': {'typical': 500, 'good': 350, 'best': 200},
'Residential': {'typical': 400, 'good': 280, 'best': 150},
'Retail': {'typical': 450, 'good': 320, 'best': 180},
'Industrial': {'typical': 350, 'good': 250, 'best': 150},
'Healthcare': {'typical': 700, 'good': 500, 'best': 350},
}
def __init__(self):
self.materials: Dict[str, MaterialCarbon] = dict(self.DEFAULT_MATERIALS)
self.assemblies: Dict[str, AssemblyCarbon] = {}
def add_material(self, material: MaterialCarbon):
"""Add or update a material."""
self.materials[material.material_id] = material
def calculate_material_carbon(self, material_id: str, quantity: float,
stages: List[LifecycleStage] = None) -> Dict:
"""Calculate carbon for a material quantity."""
if material_id not in self.materials:
raise ValueError(f"Unknown material: {material_id}")
material = self.materials[material_id]
if stages is None:
stages = list(LifecycleStage)
carbon_by_stage = {}
total = 0
for stage in stages:
if stage == LifecycleStage.A1_A3:
carbon = material.carbon_a1_a3 * quantity
elif stage == LifecycleStage.A4:
carbon = material.carbon_a4 * quantity
elif stage == LifecycleStage.A5:
carbon = material.carbon_a5 * quantity
elif stage == LifecycleStage.B1_B7:
carbon = material.carbon_b * quantity
elif stage == LifecycleStage.C1_C4:
carbon = material.carbon_c * quantity
elif stage == LifecycleStage.D:
carbon = material.carbon_d * quantity
else:
carbon = 0
carbon_by_stage[stage.value] = carbon
total += carbon
return {
'material_id': material_id,
'material_name': material.name,
'quantity': quantity,
'unit': material.unit,
'total_carbon': total,
'carbon_by_stage': carbon_by_stage
}
def create_assembly(self, assembly_id: str, name: str,
components: List[Dict]) -> AssemblyCarbon:
"""Create an assembly from multiple materials."""
total_carbon = 0
carbon_by_stage = {stage.value: 0 for stage in LifecycleStage}
material_details = []
for comp in components:
material_id = comp['material_id']
quantity = comp['quantity']
result = self.calculate_material_carbon(material_id, quantity)
total_carbon += result['total_carbon']
for stage, carbon in result['carbon_by_stage'].items():
carbon_by_stage[stage] += carbon
material_details.append({
'material': result['material_name'],
'quantity': quantity,
'unit': result['unit'],
'carbon': result['total_carbon']
})
assembly = AssemblyCarbon(
assembly_id=assembly_id,
name=name,
materials=material_details,
total_carbon=total_carbon,
carbon_by_stage=carbon_by_stage
)
self.assemblies[assembly_id] = assembly
return assembly
def calculate_project_carbon(self, project_id: str, project_name: str,
gross_area: float, building_type: str,
quantities: List[Dict]) -> ProjectCarbon:
"""Calculate total project carbon."""
assemblies = []
total_carbon = 0
carbon_by_stage = {stage.value: 0 for stage in LifecycleStage}
carbon_by_category = {}
for qty in quantities:
if 'assembly_id' in qty:
# Use predefined assembly
if qty['assembly_id'] in self.assemblies:
assembly = self.assemblies[qty['assembly_id']]
multiplier = qty.get('multiplier', 1)
scaled_carbon = assembly.total_carbon * multiplier
assemblies.append(AssemblyCarbon(
assembly_id=assembly.assembly_id,
name=assembly.name,
materials=assembly.materials,
total_carbon=scaled_carbon,
carbon_by_stage={k: v * multiplier for k, v in assembly.carbon_by_stage.items()}
))
total_carbon += scaled_carbon
elif 'material_id' in qty:
# Direct material
result = self.calculate_material_carbon(
qty['material_id'], qty['quantity']
)
total_carbon += result['total_carbon']
for stage, carbon in result['carbon_by_stage'].items():
carbon_by_stage[stage] += carbon
# Track by category
material = self.materials[qty['material_id']]
cat = material.category
carbon_by_category[cat] = carbon_by_category.get(cat, 0) + result['total_carbon']
# Calculate metrics
carbon_per_area = total_carbon / gross_area if gross_area > 0 else 0
# Compare to benchmarks
benchmark = self.BENCHMARKS.get(building_type, self.BENCHMARKS['Office'])
benchmark_comparison = {
'carbon_per_area': carbon_per_area,
'typical_benchmark': benchmark['typical'],
'good_benchmark': benchmark['good'],
'best_benchmark': benchmark['best'],
'vs_typical': (carbon_per_area / benchmark['typical'] - 1) * 100,
'rating': self._get_rating(carbon_per_area, benchmark)
}
return ProjectCarbon(
project_id=project_id,
name=project_name,
gross_area=gross_area,
assemblies=assemblies,
total_embodied_carbon=total_carbon,
carbon_per_area=carbon_per_area,
carbon_by_stage=carbon_by_stage,
carbon_by_category=carbon_by_category,
benchmark_comparison=benchmark_comparison
)
def _get_rating(self, carbon: float, benchmark: Dict) -> str:
"""Get rating based on benchmark comparison."""
if carbon <= benchmark['best']:
return 'A (Best Practice)'
elif carbon <= benchmark['good']:
return 'B (Good Practice)'
elif carbon <= benchmark['typical']:
return 'C (Typical)'
else:
return 'D (Above Typical)'
def compare_alternatives(self, base_project: ProjectCarbon,
alternatives: List[Dict]) -> pd.DataFrame:
"""Compare carbon of design alternatives."""
comparisons = [{
'Option': 'Base Design',
'Total Carbon (tCO2e)': base_project.total_embodied_carbon / 1000,
'Carbon/m² (kgCO2e)': base_project.carbon_per_area,
'vs Base': '0%',
'Rating': base_project.benchmark_comparison['rating']
}]
for alt in alternatives:
project = self.calculate_project_carbon(
alt['id'], alt['name'], alt['gross_area'],
alt.get('building_type', 'Office'), alt['quantities']
)
change = (project.total_embodied_carbon - base_project.total_embodied_carbon) / base_project.total_embodied_carbon * 100
comparisons.append({
'Option': alt['name'],
'Total Carbon (tCO2e)': project.total_embodied_carbon / 1000,
'Carbon/m² (kgCO2e)': project.carbon_per_area,
'vs Base': f'{change:+.1f}%',
'Rating': project.benchmark_comparison['rating']
})
return pd.DataFrame(comparisons)
def suggest_reductions(self, project: ProjectCarbon) -> List[Dict]:
"""Suggest carbon reduction opportunities."""
suggestions = []
# Analyze by category
if 'Concrete' in project.carbon_by_category:
concrete_carbon = project.carbon_by_category['Concrete']
if concrete_carbon > project.total_embodied_carbon * 0.3:
suggestions.append({
'category': 'Concrete',
'current_carbon': concrete_carbon,
'suggestion': 'Consider low-carbon concrete (GGBS/PFA replacement)',
'potential_reduction': '20-40%',
'impact': concrete_carbon * 0.3
})
if 'Steel' in project.carbon_by_category:
steel_carbon = project.carbon_by_category['Steel']
if steel_carbon > project.total_embodied_carbon * 0.2:
suggestions.append({
'category': 'Steel',
'current_carbon': steel_carbon,
'suggestion': 'Specify high recycled content steel',
'potential_reduction': '10-25%',
'impact': steel_carbon * 0.2
})
# Benchmark-based suggestions
if project.benchmark_comparison['vs_typical'] > 0:
suggestions.append({
'category': 'Overall',
'current_carbon': project.total_embodied_carbon,
'suggestion': 'Project exceeds typical benchmark - review high-carbon elements',
'potential_reduction': f"{abs(project.benchmark_comparison['vs_typical']):.0f}%",
'impact': project.total_embodied_carbon * abs(project.benchmark_comparison['vs_typical']) / 100
})
return sorted(suggestions, key=lambda x: -x['impact'])
def generate_report(self, project: ProjectCarbon) -> str:
"""Generate carbon assessment report."""
lines = ["# Embodied Carbon Assessment Report", ""]
lines.append(f"**Project:** {project.name}")
lines.append(f"**Gross Area:** {project.gross_area:,.0f} m²")
lines.append(f"**Assessment Date:** {pd.Timestamp.now().strftime('%Y-%m-%d')}")
lines.append("")
# Summary
lines.append("## Carbon Summary")
lines.append(f"- **Total Embodied Carbon:** {project.total_embodied_carbon/1000:,.0f} tCO2e")
lines.append(f"- **Carbon Intensity:** {project.carbon_per_area:,.0f} kgCO2e/m²")
lines.append(f"- **Rating:** {project.benchmark_comparison['rating']}")
lines.append("")
# By lifecycle stage
lines.append("## Carbon by Lifecycle Stage")
for stage, carbon in project.carbon_by_stage.items():
if carbon != 0:
pct = carbon / project.total_embodied_carbon * 100
lines.append(f"- {stage}: {carbon/1000:,.1f} tCO2e ({pct:.1f}%)")
lines.append("")
# By category
lines.append("## Carbon by Material Category")
for cat, carbon in sorted(project.carbon_by_category.items(), key=lambda x: -x[1]):
pct = carbon / project.total_embodied_carbon * 100
lines.append(f"- {cat}: {carbon/1000:,.1f} tCO2e ({pct:.1f}%)")
lines.append("")
# Benchmark
lines.append("## Benchmark Comparison")
bc = project.benchmark_comparison
lines.append(f"- Project: {bc['carbon_per_area']:.0f} kgCO2e/m²")
lines.append(f"- Typical: {bc['typical_benchmark']} kgCO2e/m²")
lines.append(f"- Good Practice: {bc['good_benchmark']} kgCO2e/m²")
lines.append(f"- Best Practice: {bc['best_benchmark']} kgCO2e/m²")
lines.append("")
# Reduction opportunities
suggestions = self.suggest_reductions(project)
if suggestions:
lines.append("## Reduction Opportunities")
for sug in suggestions[:5]:
lines.append(f"\n### {sug['category']}")
lines.append(f"- **Suggestion:** {sug['suggestion']}")
lines.append(f"- **Potential Reduction:** {sug['potential_reduction']}")
lines.append(f"- **Impact:** {sug['impact']/1000:,.1f} tCO2e")
return "\n".join(lines)
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 · 466 lines · 26 tokens per session scan A 67552c134a43
lifecycle-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 26 tokens to every session and 4,385 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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