mirror of
https://github.com/davila7/claude-code-templates.git
synced 2026-09-19 01:30:23 +08:00
Dani
054c0e56e9
Removed the deprecated 'model: sonnet' field from command frontmatter to fix compatibility issues with Claude Code. Commands will now inherit the model from the active conversation instead of using a hardcoded deprecated model identifier. Changes: - Removed 'model: sonnet' from 142 command files across all categories - Commands now use default model inheritance behavior - Preserved 'model: claude-sonnet-4-5-20250929' in gemini-review.md (valid model) - 67 commands without model field remain unchanged This resolves the "model: sonnet not found" error reported in GitHub issues and aligns with Claude Code's recommended practice of letting commands inherit the conversation model. Affected categories: - Database/Supabase commands (8 files) - Documentation commands (8 files) - Deployment commands (11 files) - Setup commands (14 files) - Performance commands (8 files) - Team commands (14 files) - Security commands (6 files) - NextJS/Vercel commands (10 files) - Sync commands (14 files) - And more across other categories 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2.0 KiB
2.0 KiB
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:
- System Mapping - Component identification, relationship modeling, and boundary definition
- Data Foundation - Quality assessment, gap analysis, and validation framework
- Model Construction - Behavior modeling, interaction dynamics, and environmental factors
- Calibration Engine - Historical validation, real-time adjustment, and accuracy monitoring
- Scenario Simulation - What-if testing, optimization scenarios, and stress testing
- 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.