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>
2.1 KiB
Monte Carlo Simulator
Run comprehensive Monte Carlo simulations with advanced statistical analysis: $ARGUMENTS
Current Analysis Context
- Simulation target: Based on $ARGUMENTS (financial projections, project timelines, market scenarios, risk assessment)
- Key variables: Uncertain parameters that drive outcome variability
- Available data: Historical data, expert estimates, and probability distributions
- Decision requirements: Confidence levels and risk tolerance for decision-making
Task
Execute sophisticated Monte Carlo simulations with comprehensive uncertainty quantification:
Simulation Target: Use $ARGUMENTS to simulate financial projections, project timelines, market scenarios, or risk assessments
Monte Carlo Framework:
- Variable Definition - Uncertain parameter identification, probability distribution selection, and correlation modeling
- Simulation Engine - Random sampling, scenario generation, and statistical convergence analysis
- Output Analysis - Probability distributions, confidence intervals, and sensitivity analysis
- Risk Quantification - Value at Risk (VaR), extreme scenario analysis, and tail risk assessment
- Scenario Clustering - Pattern recognition, outcome categorization, and decision-relevant grouping
- Decision Integration - Risk-adjusted recommendations, optimization strategies, and contingency planning
Advanced Features: Latin hypercube sampling, copula modeling, importance sampling, and variance reduction techniques.
Statistical Rigor: Convergence testing, goodness-of-fit validation, and robust statistical inference with comprehensive uncertainty bounds.
Output: Complete Monte Carlo analysis with probability distributions, risk metrics, scenario analysis, and statistically-grounded decision recommendations with quantified confidence levels.