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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Session Learning Capture
Capture and integrate session learnings into project memory and knowledge base: $ARGUMENTS
Current Learning Context
- Session duration: Current Claude Code session learning opportunities
- Memory files: !
find . -name "CLAUDE*.md" | wc -lavailable memory files for knowledge integration - Project complexity: Assessment of project structure and documentation completeness
- Learning patterns: Identification of knowledge gaps and correction opportunities
Task
Execute comprehensive learning capture with automatic knowledge integration:
Capture Type: Use $ARGUMENTS to focus on project learnings, implementation corrections, structure insights, or workflow improvements
Learning Capture Framework:
- Learning Identification - Detect new project knowledge, identify implementation corrections, recognize structural insights, note workflow discoveries
- Knowledge Classification - Categorize learning type, assess importance level, determine integration location, evaluate reusability potential
- Context Analysis - Analyze session context, identify triggering conditions, assess knowledge applicability, determine documentation needs
- Integration Planning - Select appropriate memory files, determine update strategy, maintain consistency, preserve existing knowledge
- Memory Updates - Update CLAUDE.md files, enhance documentation, improve workflows, strengthen knowledge base
- Validation Process - Verify accuracy of captured knowledge, ensure integration quality, validate accessibility, confirm usefulness
Advanced Features: Automated learning detection, intelligent categorization, context-aware integration, knowledge graph enhancement, version control integration.
Quality Assurance: Learning accuracy validation, integration consistency, accessibility optimization, knowledge retrieval efficiency.
Output: Comprehensive learning integration with updated memory files, enhanced documentation, improved workflows, and validated knowledge base.