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 jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill digital-twin-syncgit clone --depth 1 https://github.com/jdmorag97-rgb/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/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/digital-twin-sync)<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.
<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>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 | $0.00036 | $0.05202 |
| Opus 5 | $0.00018 | $0.02601 |
| Sonnet 5 | $0.00007 | $0.01040 |
| Haiku 4.5 | $0.00004 | $0.00520 |
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.
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.
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"))
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.
- 8d ago First seen · 724 lines · 36 tokens per session scan A e0220ddf87ac
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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