digital-twin-creator — Claude Code command: build calibrated digital
Walks the agent through mapping, calibration and scenario testing to produce a digital twin of a process or system.
npx claude-code-templates@latest --command simulation/digital-twin-creatorWhat digital-twin-creator does
The command drives the agent through six stages. It starts with system mapping: components, relationships, boundaries. Then comes a data quality check and gap analysis, followed by behaviour and interaction modelling.
Calibration runs against historical records with real-time adjustment, then what-if scenarios and stress tests, and finally a recommendation engine with risk assessment. You name the subject up front — manufacturing, a business process, a customer journey or system performance. The flags --manufacturing, --business-process, --customer-journey and --system-performance set that frame, and free text in the argument narrows it further. Allowed tools are Read, Write, Edit and WebSearch.
What you get back depends on what you feed it. With no historical records or monitoring data there is nothing to calibrate against, and the report stays at the architecture level. Worth gathering the dataset first and deciding which decision the twin is supposed to support.
What it helps with
- map components and their relationships in a system
- assess data quality before modelling
- calibrate a model against historical records
- run what-if and stress scenarios
- get recommendations with risk assessment
Example prompts for the agent
- /digital-twin-creator --manufacturing bottling line, cut downtime
- /digital-twin-creator --customer-journey checkout flow in the web store
- /digital-twin-creator --system-performance API load during peak hours
How to install digital-twin-creator
1claude-code-templates
npx claude-code-templates@latest --command simulation/digital-twin-creator2Manually: put the files in place
.claude/commands/digital-twin-creator.mdCall it with /digital-twin-creator. For all projects use ~/.claude/commands/
Manually: put the files in place
~/.codex/prompts/digital-twin-creator.mdCall it with /prompts:digital-twin-creator. Codex marks this format deprecated in favour of skills
Manually: put the files in place
.github/prompts/digital-twin-creator.prompt.mdManually: put the files in place
.gemini/commands/digital-twin-creator.tomlGemini stores commands as TOML: the prompt text goes into the prompt field
Questions about digital-twin-creator
How do I install digital-twin-creator in Claude?
Run npx claude-code-templates@latest --command simulation/digital-twin-creator; the command lands at .claude/commands/digital-twin-creator.md.
What does digital-twin-creator do?
It builds a digital twin of the subject you name: system map, behaviour model, calibration against data, scenarios and decision recommendations.
What data does the calibration need?
Historical records, sensor readings or monitoring system data — the command compares the model against them and tunes parameters.
Source text
digital-twin-creator.md · MIT
--- allowed-tools: Read, Write, Edit, WebSearch argument-hint: "[twin-subject] | --manufacturing | --business-process | --customer-journey | --system-performance" description: Create calibrated digital twins with real-world validation, scenario testing, and decision optimization --- # Digital Twin Creator Create comprehensive digital twins with systematic calibration and validation: **$ARGUMENTS** ## Current System State - Twin subject: Based on $ARGUMENTS (manufacturing, business process, customer journey, system performance) - Available data: Existing datasets, sensors, monitoring systems, and historical records - System boundaries: Components, interfaces, and environmental factors to model - Decision requirements: Specific use cases and accuracy needs for the digital twin ## Task Build production-ready digital twin with comprehensive modeling and calibration: **Twin Subject**: Use $ARGUMENTS to model manufacturing systems, business processes, customer journeys, or system performance **Digital Twin Architecture**: 1. **System Mapping** - Component identification, relationship modeling, and boundary definition 2. **Data Foundation** - Quality assessment, gap analysis, and validation framework 3. **Model Construction** - Behavior modeling, interaction dynamics, and environmental factors 4. **Calibration Engine** - Historical validation, real-time adjustment, and accuracy monitoring 5. **Scenario Simulation** - What-if testing, optimization scenarios, and stress testing 6. **Decision Integration** - Recommendation engine, optimization algorithms, and risk assessment **Advanced Features**: Real-time synchronization, predictive analytics, automated parameter tuning, and continuous learning. **Quality Assurance**: Validation metrics, confidence intervals, model drift detection, and performance monitoring. **Output**: Production-ready digital twin with calibration reports, scenario testing capabilities, decision support features, and comprehensive documentation.
© davila7 · MIT · davila7/claude-code-templates