energy-simulation

energy-simulation is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 32 tokens per session (4,698 once invoked), scanned A, original, MIT.

A building-energy analysis tool that estimates heating and cooling needs from the building envelope, climate, and systems. The building envelope is the parts separating indoors from outdoors, such as walls, roofs, floors, and windows.

In plain words
What is it for?
Use it to calculate heating and cooling loads, evaluate envelope performance, size HVAC systems, assess renewable-energy options, and estimate life-cycle costs.
Why use it?
It helps designers compare insulation and system choices before construction. It can also check energy-code requirements and avoid incorrectly sized HVAC equipment.

Skill for Claude CodeCodex

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

Good fit Use it to calculate heating and cooling loads, evaluate envelope performance, size HVAC systems, assess renewable-energy options, and estimate life-cycle costs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/energy-simulation
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 datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill energy-simulation
Clone the repo
git clone --depth 1 https://github.com/datadrivenconstruction/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 energy-simulation

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/energy-simulation/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/energy-simulation)
Your own site
<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.

agentmods 80×15 button for energy-simulation

Your own site · 80×15
<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>
Per session 32 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,698 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found 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 $0.00032 $0.04698
Opus 5 $0.00016 $0.02349
Sonnet 5 $0.00006 $0.00940
Haiku 4.5 $0.00003 $0.00470

Measured 7d ago against content hash 5b4287124123, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

5_DDC_Innovative/energy-simulation/SKILL.md · 564 lines

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")

Read the full file on GitHub · 564 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. 7d ago First seen · 564 lines · 32 tokens per session scan A 5b4287124123

Subscribe to this mod's changes

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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