digital-twin-sync

digital-twin-sync is a skill for Claude Code, Codex from jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction. It costs 36 tokens per session (5,202 once invoked), scanned A, a copy of digital-twin-sync, MIT.

A digital-twin synchronization tool that links a BIM model—a structured 3D building model—with live sensor readings, progress updates, and field information.

In plain words
What is it for?
Use it to bind sensors to BIM elements, update statuses in real time, track history, detect anomalies, combine data from multiple sources, and support predictive analysis.
Why use it?
It keeps the model closer to current site conditions and makes changes, issues, and historical states easier to monitor.

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 bind sensors to BIM elements, update statuses in real time, track history, detect anomalies, combine data from multiple sources, and support predictive analysis.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/digital-twin-sync
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 digital-twin-sync
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 digital-twin-sync

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/digital-twin-sync"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/digital-twin-sync.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 36 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,202 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 $0.00036 $0.05202
Opus 5 $0.00018 $0.02601
Sonnet 5 $0.00007 $0.01040
Haiku 4.5 $0.00004 $0.00520

Measured 8d ago against content hash e0220ddf87ac, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

digital-twin-sync 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 8d 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 digital-twin-sync — 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.

5_DDC_Innovative/digital-twin-sync/SKILL.md · 724 lines

How it starts

The opening of the file, as written. The whole thing — 724 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Digital Twin Synchronization

Overview

This skill implements digital twin synchronization for construction projects. Connect BIM models with real-time sensor data, progress updates, and field information to create a living digital representation.

Capabilities:

  • BIM-IoT data binding
  • Real-time status updates
  • Historical data tracking
  • Anomaly detection
  • Predictive analytics
  • Multi-source data fusion

Quick Start

from dataclasses import dataclass, field
from datetime import datetime
from typing import Dict, List, Optional, Any
from enum import Enum
import json

class ElementStatus(Enum):
    PLANNED = "planned"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    ISSUE = "issue"

@dataclass
class TwinElement:
    element_id: str
    ifc_guid: str
    element_type: str
    status: ElementStatus
    properties: Dict[str, Any] = field(default_factory=dict)
    sensor_bindings: List[str] = field(default_factory=list)
    last_updated: datetime = field(default_factory=datetime.now)

@dataclass
class SensorData:
    sensor_id: str
    value: float
    unit: str
    timestamp: datetime
    quality: float = 1.0

class SimpleTwin:
    """Simple digital twin implementation"""

    def __init__(self, project_id: str):
        self.project_id = project_id
        self.elements: Dict[str, TwinElement] = {}
        self.sensor_data: Dict[str, List[SensorData]] = {}

    def add_element(self, element: TwinElement):
        self.elements[element.element_id] = element

    def bind_sensor(self, element_id: str, sensor_id: str):
        if element_id in self.elements:
            self.elements[element_id].sensor_bindings.append(sensor_id)

    def update_sensor(self, data: SensorData):
        if data.sensor_id not in self.sensor_data:
            self.sensor_data[data.sensor_id] = []
        self.sensor_data[data.sensor_id].append(data)

        # Update linked elements
        for elem in self.elements.values():
            if data.sensor_id in elem.sensor_bindings:
                elem.properties[f'sensor_{data.sensor_id}'] = data.value
                elem.last_updated = data.timestamp

    def get_element_state(self, element_id: str) -> Dict:
        elem = self.elements.get(element_id)
        if not elem:
            return {}

        state = {
            'element_id': elem.element_id,
            'status': elem.status.value,
            'properties': elem.properties,
            'last_updated': elem.last_updated.isoformat()
        }

        # Add latest sensor values
        for sensor_id in elem.sensor_bindings:
            if sensor_id in self.sensor_data and self.sensor_data[sensor_id]:
                latest = self.sensor_data[sensor_id][-1]
                state[f'sensor_{sensor_id}'] = {
                    'value': latest.value,
                    'unit': latest.unit,
                    'timestamp': latest.timestamp.isoformat()
                }

        return state

# Example
twin = SimpleTwin("PROJECT-001")
twin.add_element(TwinElement(
    element_id="WALL-001",
    ifc_guid="2O2Fr$t4X7Zf8NOew3FLOH",
    element_type="IfcWall",
    status=ElementStatus.IN_PROGRESS
))
twin.bind_sensor("WALL-001", "TEMP-001")
twin.update_sensor(SensorData("TEMP-001", 22.5, "°C", datetime.now()))
print(twin.get_element_state("WALL-001"))

Read the full file on GitHub · 724 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. 8d ago First seen · 724 lines · 36 tokens per session scan A e0220ddf87ac

Subscribe to this mod's changes

digital-twin-sync 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 36 tokens to every session and 5,202 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 digital-twin-sync, differing in 0 lines, and is treated as a copy.

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