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 energy-simulationgit 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/energy-simulation)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/energy-simulation"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/energy-simulation/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/energy-simulation"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/energy-simulation.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 | $0.00032 | $0.04698 |
| Opus 5 | $0.00016 | $0.02349 |
| Sonnet 5 | $0.00006 | $0.00940 |
| Haiku 4.5 | $0.00003 | $0.00470 |
Grade A, and why
energy-simulation 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:
- energy-simulation — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 564 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Energy Simulation
Overview
This skill implements building energy simulation and analysis. Calculate thermal loads, evaluate building envelope performance, and optimize systems for energy efficiency and code compliance.
Capabilities:
- Heating/cooling load calculations
- Envelope thermal analysis
- HVAC system sizing
- Energy code compliance
- Renewable energy integration
- Life cycle cost analysis
Quick Start
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Tuple
from enum import Enum
import numpy as np
class WallType(Enum):
CONCRETE = "concrete"
BRICK = "brick"
WOOD_FRAME = "wood_frame"
STEEL_FRAME = "steel_frame"
CURTAIN_WALL = "curtain_wall"
@dataclass
class BuildingEnvelope:
wall_area_m2: float
wall_u_value: float # W/m²K
roof_area_m2: float
roof_u_value: float
floor_area_m2: float
floor_u_value: float
window_area_m2: float
window_u_value: float
window_shgc: float # Solar Heat Gain Coefficient
@dataclass
class ClimateData:
location: str
heating_degree_days: float # HDD base 18°C
cooling_degree_days: float # CDD base 18°C
design_temp_winter: float
design_temp_summer: float
def calculate_heat_loss(envelope: BuildingEnvelope, climate: ClimateData,
indoor_temp: float = 21) -> float:
"""Calculate design heat loss (W)"""
delta_t = indoor_temp - climate.design_temp_winter
# Transmission losses
wall_loss = envelope.wall_area_m2 * envelope.wall_u_value * delta_t
roof_loss = envelope.roof_area_m2 * envelope.roof_u_value * delta_t
floor_loss = envelope.floor_area_m2 * envelope.floor_u_value * delta_t * 0.5 # Ground factor
window_loss = envelope.window_area_m2 * envelope.window_u_value * delta_t
total_loss = wall_loss + roof_loss + floor_loss + window_loss
# Add infiltration estimate (simplified)
volume = envelope.floor_area_m2 * 3 # Assume 3m height
infiltration = volume * 0.5 * 0.33 * delta_t # 0.5 ACH, 0.33 Wh/m³K
return total_loss + infiltration
# Example
envelope = BuildingEnvelope(
wall_area_m2=500, wall_u_value=0.35,
roof_area_m2=200, roof_u_value=0.25,
floor_area_m2=200, floor_u_value=0.30,
window_area_m2=100, window_u_value=1.4, window_shgc=0.4
)
climate = ClimateData(
location="Moscow",
heating_degree_days=5000,
cooling_degree_days=300,
design_temp_winter=-25,
design_temp_summer=30
)
heat_loss = calculate_heat_loss(envelope, climate)
print(f"Design heat loss: {heat_loss/1000:.1f} kW")
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 · 564 lines · 32 tokens per session scan A 5b4287124123
energy-simulation is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (308 stars, last pushed 20d ago), licensed MIT. It adds 32 tokens to every session and 4,698 once invoked, about $0.0002 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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