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digital-twin-engineering

v0.1.2

Digital-twin engineering team — agents (digital-twin-architect, twin-integration-engineer) for the layer answering 'what should this twin MODEL, at what FIDELITY, and does it MATCH the real asset?': twin taxonomy (asset vs process vs system-of-systems; descriptive/predictive/prescriptive; digital-shadow vs true bidirectional), only-as-much-fidelity-as-the-decision-needs, modeling approach (physics vs data-driven/surrogate/reduced-order vs hybrid), telemetry ingestion (MQTT/OPC-UA/Kafka, edge vs cloud), state sync/drift/calibration, simulation & what-if, and fidelity validation. Fluent in DTDL + Azure Digital Twins, ISO 23247, AAS, Eclipse Ditto, NVIDIA Omniverse, Unity/Unreal, Bentley/Siemens. skills, a knowledge bank (decision tree + 2026 patterns), and templates. Distinct from embedded-iot-engineering (device firmware), robotics-autonomous-systems-engineering (robot control/autonomy), and data-platform (BI/warehouse). Requires ravenclaude-core@>=0.7.0.

Claude Code3 Skills

By Matt CorbettLicense: MIT7 GitHub starsUpdated last week

Directory evidence

Runtimes
Claude Code
Parsed components
3 skill or MCP entries
Source updated
Sep 15, 2026
Manifest status
Canonical path parsed

The directory validates manifest shape and source location. It does not execute the plugin or provide a security endorsement. Review the indexing methodology

Install digital-twin-engineering for Claude Code

Installs for the current user
claude plugin marketplace add IchenDEV/agent-plugin-mkt
claude plugin marketplace update agent-plugin-marketplace
claude plugin install digital-twin-engineering@agent-plugin-marketplace

Paste and run these commands in a terminal with Claude Code. They add and refresh the PluginsMP catalog, then install this plugin.

The installer fetches third-party code from the source repository shown on this page. This directory validates manifest structure and source location, but does not perform a security audit; review the manifest, components, and source before installing.

Get the source manually
git clone https://github.com/mcorbett51090/RavenClaude

Clone the source repository, then follow its setup instructions to add the plugin to a compatible client. The plugin root is plugins/digital-twin-engineering/.

Plugin files

plugins/digital-twin-engineering/
├── .claude-plugin/plugin.json
├── skills/choose-digital-twin-architecture/SKILL.md
├── skills/design-twin-data-and-sync-model/SKILL.md
└── skills/implement-twin-integration-and-simulation/SKILL.md

Included Skills3

choose-digital-twin-architectureskills/choose-digital-twin-architecture/SKILL.md

Scope and architect a digital twin for a described asset and decision by traversing the digital-twin architecture decision tree (decision it informs → twin type → shadow-vs-bidirectional → descriptive/predictive/prescriptive → modeling approach → fidelity → sync → platform), then return the twin scope/type, the fidelity level (only as much as the decision needs), the modeling approach (physics vs data-driven/surrogate/reduced-order vs hybrid), the platform (DTDL+Azure Digital Twins / AAS / Eclipse Ditto / Omniverse / Unity-Unreal / Bentley-Siemens), the state-sync strategy, and the conditions that would flip the choice. Reach for this when the user asks "we want a twin of <X> — where do we start?", "physics vs data-driven vs hybrid?", "how much fidelity?", "which twin platform?", or "shadow or bidirectional?". Used by `digital-twin-architect` (primary).

design-twin-data-and-sync-modelskills/design-twin-data-and-sync-model/SKILL.md

From a physical asset and its telemetry, derive the twin's data and synchronization model — the signal list and sampling rates, the transport protocol (MQTT / OPC-UA / Kafka), the edge-vs-cloud split, the sync-latency budget the decision tolerates, the binding of each signal to the twin's model properties (DTDL properties / AAS submodel elements), and the drift-detection + recalibration policy. Reach for this when the user asks "what data does this twin need and how often?", "map our sensor stream to the twin model", or "how fresh must the twin be?". Used by `twin-integration-engineer` and `digital-twin-architect`.

implement-twin-integration-and-simulationskills/implement-twin-integration-and-simulation/SKILL.md

Build a digital twin end to end — ingest the telemetry (MQTT / OPC-UA / Kafka, edge-vs-cloud) and bind it to the model (DTDL properties / AAS submodels), wire the physics or reduced-order/surrogate model, run simulation and what-if scenarios against live twin state, stand up 3D or dashboard visualization where it earns its keep, and — the step teams skip — validate fidelity against the real asset (predicted-vs-actual SLIs, error bounds, calibration) plus a drift monitor. Reach for this when the user asks "ingest our stream into the twin", "run a what-if simulation", "does the twin match the asset?", or "the twin has drifted". Used by `twin-integration-engineer` (primary).

Plugin manifests1

plugins/digital-twin-engineering/.claude-plugin/plugin.json
{
  "name": "digital-twin-engineering",
  "version": "0.1.2",
  "description": "Digital-twin engineering team — agents (digital-twin-architect, twin-integration-engineer) for the layer answering 'what should this twin MODEL, at what FIDELITY, and does it MATCH the real asset?': twin taxonomy (asset vs process vs system-of-systems; descriptive/predictive/prescriptive; digital-shadow vs true bidirectional), only-as-much-fidelity-as-the-decision-needs, modeling approach (physics vs data-driven/surrogate/reduced-order vs hybrid), telemetry ingestion (MQTT/OPC-UA/Kafka, edge vs cloud), state sync/drift/calibration, simulation & what-if, and fidelity validation. Fluent in DTDL + Azure Digital Twins, ISO 23247, AAS, Eclipse Ditto, NVIDIA Omniverse, Unity/Unreal, Bentley/Siemens. skills, a knowledge bank (decision tree + 2026 patterns), and templates. Distinct from embedded-iot-engineering (device firmware), robotics-autonomous-systems-engineering (robot control/autonomy), and data-platform (BI/warehouse). Requires ravenclaude-core@>=0.7.0.",
  "author": {
    "name": "Matt Corbett"
  },
  "homepage": "https://github.com/mcorbett51090/RavenClaude",
  "license": "MIT",
  "keywords": [
    "digital-twin",
    "dtdl",
    "azure-digital-twins",
    "iso-23247",
    "asset-administration-shell",
    "eclipse-ditto",
    "nvidia-omniverse",
    "physics-simulation",
    "reduced-order-model",
    "predictive-maintenance",
    "telemetry-ingestion",
    "twin-fidelity"
  ],
  "requires": {
    "plugins": [
      "ravenclaude-core@>=0.7.0"
    ]
  }
}

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[digital-twin-engineering on Agent Plugins Marketplace](https://pluginsmp.com/plugins/digital-twin-engineering)