4d-simulation

4d-simulation is a skill for Claude Code, Codex from jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction. It costs 37 tokens per session (4,359 once invoked), scanned A, a copy of 4d-simulation, MIT.

A construction planning tool that links building-model parts to dates and project phases, then shows how the build progresses over time.

In plain words
What is it for?
It helps teams review construction phasing, compare model elements with a schedule, and analyze when each part should be built.
Why use it?
It makes the planned construction sequence visible and helps reveal scheduling conflicts before work begins.

Skill for Claude CodeCodex

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

Good fit It helps teams review construction phasing, compare model elements with a schedule, and analyze when each part should be built.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/4d-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 jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill 4d-simulation
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 4d-simulation

README.md
[![agentmods](https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/4d-simulation/github.svg)](https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/4d-simulation)
Your own site
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/4d-simulation"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/4d-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 4d-simulation

Your own site · 80×15
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/4d-simulation"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/4d-simulation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,359 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 $0.00037 $0.04359
Opus 5 $0.00018 $0.02180
Sonnet 5 $0.00007 $0.00872
Haiku 4.5 $0.00004 $0.00436

Measured 9d ago against content hash 7e75ca7ee9d6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

4d-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 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 4d-simulation — 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/4d-simulation/SKILL.md · 552 lines

How it starts

The opening of the file, as written. The whole thing — 552 lines — stays where its author put it; the contents beside it link to each section on GitHub.

4D Simulation for Construction

Overview

Based on DDC methodology (Chapter 3.3), this skill implements 4D BIM simulations - linking 3D model elements to the 4th dimension: time. Visualize construction sequences, detect scheduling conflicts, and optimize work phasing.

Book Reference: "4D, 6D-8D и расчет CO2" / "4D-8D BIM and CO2 Calculation"

"4D моделирование позволяет визуализировать последовательность строительства и выявлять конфликты на этапе планирования." — DDC Book, Chapter 3.3

Quick Start

import pandas as pd
from datetime import datetime, timedelta

# BIM elements with schedule data
elements = pd.DataFrame({
    'ElementId': ['E001', 'E002', 'E003', 'E004'],
    'Category': ['Foundation', 'Column', 'Beam', 'Slab'],
    'Level': ['Level 0', 'Level 1', 'Level 1', 'Level 1'],
    'Start_Date': ['2024-01-01', '2024-01-15', '2024-02-01', '2024-02-15'],
    'End_Date': ['2024-01-14', '2024-01-31', '2024-02-14', '2024-02-28'],
    'Phase': ['Structure', 'Structure', 'Structure', 'Structure']
})

elements['Start_Date'] = pd.to_datetime(elements['Start_Date'])
elements['End_Date'] = pd.to_datetime(elements['End_Date'])
elements['Duration_Days'] = (elements['End_Date'] - elements['Start_Date']).dt.days

# Get elements active on a specific date
target_date = pd.to_datetime('2024-01-20')
active_elements = elements[
    (elements['Start_Date'] <= target_date) &
    (elements['End_Date'] >= target_date)
]
print(f"Elements under construction on {target_date.date()}:")
print(active_elements[['ElementId', 'Category']])

4D Data Model

Schedule-Element Linking

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import List, Dict, Optional

class ScheduleElementLinker:
    """Link BIM elements to schedule activities"""

    def __init__(self, elements_df: pd.DataFrame, schedule_df: pd.DataFrame):
        self.elements = elements_df.copy()
        self.schedule = schedule_df.copy()
        self.links = pd.DataFrame()

    def auto_link_by_category(self, mapping: Dict[str, str]):
        """Auto-link elements to activities by category mapping

        Args:
            mapping: Dict mapping element categories to activity names
                     e.g., {'Wall': 'Structural Walls', 'Slab': 'Floor Construction'}
        """
        links = []

        for category, activity_name in mapping.items():
            # Find elements of this category
            category_elements = self.elements[
                self.elements['Category'] == category
            ]['ElementId'].tolist()

            # Find matching activity
            activity = self.schedule[
                self.schedule['Activity'].str.contains(activity_name, case=False)
            ]

            if not activity.empty and category_elements:
                for elem_id in category_elements:
                    links.append({
                        'ElementId': elem_id,
                        'ActivityId': activity.iloc[0]['ActivityId'],
                        'Activity': activity.iloc[0]['Activity'],
                        'Start_Date': activity.iloc[0]['Start_Date'],
                        'End_Date': activity.iloc[0]['End_Date']
                    })

        self.links = pd.DataFrame(links)
        return self.links

    def auto_link_by_level(self):
        """Auto-link elements based on level and construction sequence"""
        # Get unique levels in order
        levels = sorted(self.elements['Level'].unique())

        links = []
        for i, level in enumerate(levels):
            level_elements = self.elements[self.elements['Level'] == level]

            # Find activity for this level
            level_activity = self.schedule[
                self.schedule['Activity'].str.contains(level, case=False)
            ]

            if not level_activity.empty:
                for _, elem in level_elements.iterrows():
                    links.append({
                        'ElementId': elem['ElementId'],
                        'ActivityId': level_activity.iloc[0]['ActivityId'],
                        'Activity': level_activity.iloc[0]['Activity'],
                        'Start_Date': level_activity.iloc[0]['Start_Date'],
                        'End_Date': level_activity.iloc[0]['End_Date']
                    })

        self.links = pd.DataFrame(links)
        return self.links

    def manual_link(self, element_id: str, activity_id: str):
        """Manually link element to activity"""
        element = self.elements[self.elements['ElementId'] == element_id]
        activity = self.schedule[self.schedule['ActivityId'] == activity_id]

        if element.empty or activity.empty:
            raise ValueError("Element or activity not found")

        new_link = pd.DataFrame([{
            'ElementId': element_id,
            'ActivityId': activity_id,
            'Activity': activity.iloc[0]['Activity'],
            'Start_Date': activity.iloc[0]['Start_Date'],
            'End_Date': activity.iloc[0]['End_Date']
        }])

        self.links = pd.concat([self.links, new_link], ignore_index=True)
        return self.links

    def get_linked_elements(self):
        """Get elements with schedule data"""
        return self.elements.merge(
            self.links[['ElementId', 'ActivityId', 'Start_Date', 'End_Date']],
            on='ElementId',
            how='left'
        )

Read the full file on GitHub · 552 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 · 552 lines · 37 tokens per session scan A 7e75ca7ee9d6

Subscribe to this mod's changes

4d-simulation 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 37 tokens to every session and 4,359 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to 4d-simulation, differing in 0 lines, and is treated as a copy.

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