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 co2-estimationgit 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/co2-estimation)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/co2-estimation"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/co2-estimation/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/co2-estimation"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/co2-estimation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00029 | $0.04538 |
| Opus 5 | $0.00015 | $0.02269 |
| Sonnet 5 | $0.00006 | $0.00908 |
| Haiku 4.5 | $0.00003 | $0.00454 |
Grade A, and why
co2-estimation 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 7d 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:
- co2-estimation — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 514 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CO2 Estimation for Construction
Overview
Based on DDC methodology (Chapter 3.3), this skill provides comprehensive CO2 and carbon footprint calculations for construction projects. Sustainability is no longer optional - clients and regulations demand accurate environmental impact assessments.
Book Reference: "4D, 6D-8D и расчет CO2" / "4D-8D BIM and CO2 Calculation"
"Расчет углеродного следа становится обязательным требованием для многих проектов. BIM-модель содержит все необходимые данные для автоматизации этого процесса." — DDC Book, Chapter 3.3
Quick Start
import pandas as pd
# Load BIM elements with materials
df = pd.read_excel("bim_elements.xlsx")
# CO2 emission factors (kg CO2 per unit)
emission_factors = {
'Concrete': 0.13, # kg CO2 per kg
'Steel': 1.85, # kg CO2 per kg
'Brick': 0.24, # kg CO2 per kg
'Timber': -1.6, # negative = carbon sink
'Glass': 0.85, # kg CO2 per kg
'Aluminum': 8.14 # kg CO2 per kg
}
# Calculate emissions
df['CO2_kg'] = df.apply(
lambda row: row['Weight_kg'] * emission_factors.get(row['Material'], 0),
axis=1
)
total_co2 = df['CO2_kg'].sum()
print(f"Total Carbon Footprint: {total_co2:,.0f} kg CO2")
print(f"Equivalent: {total_co2/1000:,.1f} tonnes CO2")
Emission Factors Database
Material Emission Factors (Embodied Carbon)
# Comprehensive emission factors database (kg CO2e per kg material)
EMISSION_FACTORS_KG = {
# Concrete and cement
'Concrete_C20': 0.10,
'Concrete_C30': 0.13,
'Concrete_C40': 0.16,
'Concrete_C50': 0.19,
'Cement_Portland': 0.83,
'Mortar': 0.20,
# Metals
'Steel_Reinforcing': 1.85,
'Steel_Structural': 1.55,
'Steel_Recycled': 0.47,
'Aluminum_Primary': 8.14,
'Aluminum_Recycled': 0.52,
'Copper': 2.71,
# Masonry
'Brick_Clay': 0.24,
'Brick_Concrete': 0.12,
'Stone_Natural': 0.06,
'Block_Concrete': 0.10,
# Wood (negative = carbon sequestration)
'Timber_Softwood': -1.60,
'Timber_Hardwood': -1.40,
'Plywood': 0.45,
'CLT': -1.20, # Cross-Laminated Timber
'Glulam': -1.10,
# Insulation
'Insulation_Mineral': 1.20,
'Insulation_EPS': 3.29,
'Insulation_XPS': 3.45,
'Insulation_Cellulose': 0.10,
# Glass
'Glass_Float': 0.85,
'Glass_Double': 1.30,
'Glass_Triple': 1.80,
# Plastics
'PVC': 2.61,
'HDPE': 1.93,
'Polycarbonate': 5.00,
# Other
'Gypsum_Board': 0.39,
'Ceramic_Tile': 0.78,
'Asphalt': 0.05
}
# Emission factors per volume (kg CO2e per m³)
EMISSION_FACTORS_M3 = {
'Concrete_C30': 312, # ~2400 kg/m³ * 0.13
'Steel': 14430, # ~7800 kg/m³ * 1.85
'Timber': -800, # ~500 kg/m³ * -1.6
'Brick': 432, # ~1800 kg/m³ * 0.24
'Glass': 2125 # ~2500 kg/m³ * 0.85
}
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.
- 7d ago First seen · 514 lines · 29 tokens per session scan A 8eb88fe293a8
co2-estimation is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (308 stars, last pushed 19d ago), licensed MIT. It adds 29 tokens to every session and 4,538 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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