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>
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Estimate Assistant
Generate data-driven task estimates with confidence intervals and accuracy tracking: $ARGUMENTS
Current Estimation Context
- Team velocity: !
git log --oneline --since='1 month ago' | wc -lcommits in last month - Historical data: Git history analysis for similar task completion patterns
- Code complexity: !
find . -name "*.js" -o -name "*.ts" -o -name "*.py" | head -5 | xargs wc -l 2>/dev/null | tail -1 || echo "No code files" - Sprint tracking: Linear task completion times and estimate accuracy
Task
Execute comprehensive task estimation with historical analysis and confidence modeling:
Estimation Focus: Use $ARGUMENTS for task description analysis, historical pattern matching, complexity assessment, or team velocity calculation
Estimation Framework:
- Historical Pattern Analysis - Analyze similar past tasks, extract completion time patterns, identify velocity trends, calculate accuracy metrics
- Complexity Assessment - Evaluate technical complexity, assess scope uncertainty, identify risk factors, estimate effort distribution
- Team Velocity Integration - Calculate sprint velocity, analyze individual capacity, assess team expertise, factor in availability constraints
- Confidence Modeling - Generate confidence intervals, assess estimation uncertainty, identify risk factors, provide accuracy ranges
- Calibration Analysis - Compare past estimates vs actuals, identify systematic biases, calculate estimation accuracy, improve prediction models
- Context Integration - Factor in current sprint load, assess team familiarity, evaluate external dependencies, integrate deadline pressure
Advanced Features: Multi-point estimation, Monte Carlo simulation, reference class forecasting, estimation accuracy tracking, bias correction algorithms.
Quality Metrics: Estimation confidence levels, accuracy historical trends, velocity stability, complexity correlation analysis.
Output: Data-driven estimates with confidence intervals, historical accuracy metrics, risk assessment, and calibration recommendations.