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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>
1.6 KiB
1.6 KiB
allowed-tools: Read, Bash, Grep, Glob
argument-hint: [project-type] | --duration | --team-size | --risk-level
description: Simulate project outcomes with variable modeling, risk assessment, and resource optimization
Project Timeline Simulator
Simulate project outcomes with comprehensive variable modeling and risk assessment: $ARGUMENTS
Current Project Context
- Project type: Based on $ARGUMENTS or codebase analysis
- Team capacity: !
git shortlog -sn --since="90 days ago" | wc -lcontributors - Velocity data: !
git log --oneline --since="30 days ago" | wc -lcommits/month - Risk indicators: @RISKS.md or project documentation
Task
Generate comprehensive project timeline simulations with multiple scenarios:
Simulation Framework:
- Variable Modeling - Team capacity, skill levels, external dependencies, technical complexity
- Scenario Generation - Baseline, optimistic, pessimistic, and disruption scenarios
- Risk Assessment - Technical, resource, business, and external risk factors
- Resource Optimization - Team allocation, budget distribution, timeline buffers
- Decision Points - Milestone gates, adaptation triggers, contingency activation
Output Deliverables:
- Timeline prediction ranges with confidence intervals
- Critical path analysis and dependency mapping
- Risk-adjusted resource allocation recommendations
- Early warning indicators and decision triggers
- Monte Carlo simulation results with probability distributions
Success Optimization: Multi-objective optimization for time, quality, and resource efficiency.