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>
2.2 KiB
Timeline Compressor
Compress real-world timelines into rapid simulation cycles for exponential learning acceleration: $ARGUMENTS
Current Timeline Context
- Timeline type: Based on $ARGUMENTS (product development, market adoption, business transformation, competitive response)
- Original duration: Real-world timeline length and key phases
- Compression goals: Decision acceleration, risk exploration, learning speed, or option generation
- Critical milestones: Key events and dependencies that must be preserved
Task
Implement systematic timeline compression with rapid iteration and decision acceleration:
Timeline Type: Use $ARGUMENTS to compress product development cycles, market adoption patterns, business transformations, or competitive responses
Compression Framework:
- Timeline Architecture - Temporal structure mapping, dependency analysis, and compressible component identification
- Compression Strategy - Methodology selection, acceleration factor calibration, and fidelity trade-off optimization
- Rapid Iteration Engine - Micro, mini, and macro-cycle design with parallel processing capabilities
- Confidence Management - Uncertainty quantification, risk-adjusted decision making, and validation systems
- Scenario Multiplication - Exponential scenario exploration with interaction modeling and synthesis
- Decision Integration - Acceleration optimization, validation frameworks, and strategic momentum creation
Advanced Features: Monte Carlo acceleration, scenario interaction modeling, real-time validation, and adaptive compression ratios.
Learning Optimization: Continuous improvement tracking, model refinement, and knowledge transfer for institutional capability building.
Output: Compressed timeline analysis with acceleration strategies, scenario outcomes, confidence assessments, and implementation roadmaps for exponential learning and decision advantage.