lifecycle-carbon-calculator

lifecycle-carbon-calculator is a skill for Claude Code, Codex from jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction. It costs 26 tokens per session (4,385 once invoked), scanned A, a copy of lifecycle-carbon-calculator, MIT.

A calculator for the carbon emissions of construction materials and projects across their full lifecycle, from manufacturing and transport through use and disposal. Embodied carbon is the emissions associated with making, moving, using, and replacing building materials.

In plain words
What is it for?
It helps compare lower-carbon materials, track carbon-reduction targets, and prepare data for sustainability assessments and green-building certifications.
Why use it?
It shows how design and material choices affect total emissions instead of looking only at construction-stage impacts.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit It helps compare lower-carbon materials, track carbon-reduction targets, and prepare data for sustainability assessments and green-building certifications.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/lifecycle-carbon-calculator
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 jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill lifecycle-carbon-calculator
Clone the repo
git clone --depth 1 https://github.com/jdmorag97-rgb/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 lifecycle-carbon-calculator

README.md
[![agentmods](https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/lifecycle-carbon-calculator/github.svg)](https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/lifecycle-carbon-calculator)
Your own site
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/lifecycle-carbon-calculator"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/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.

agentmods 80×15 button for lifecycle-carbon-calculator

Your own site · 80×15
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/lifecycle-carbon-calculator"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/lifecycle-carbon-calculator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 26 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,385 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.
Origin 100% copy Near-identical to another mod 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 $0.00026 $0.04385
Opus 5 $0.00013 $0.02193
Sonnet 5 $0.00005 $0.00877
Haiku 4.5 $0.00003 $0.00439

Measured 9d ago against content hash 67552c134a43, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

Origin

This is a copy

100% identical to lifecycle-carbon-calculator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

2_DDC_Book/3.3-4D-BIM-CO2-Simulation/lifecycle-carbon-calculator/SKILL.md · 466 lines

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)

Read the full file on GitHub · 466 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. 9d ago First seen · 466 lines · 26 tokens per session scan A 67552c134a43

Subscribe to this mod's changes

lifecycle-carbon-calculator is a skill published in the GitHub repository jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction (2 stars, last pushed 6mo 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. It is 100% identical to lifecycle-carbon-calculator, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens