boundary-bbcr-fallback
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Intelligently refactor and improve code quality
$ npx -y skills add qdhenry/Claude-Command-Suite --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
/refactor-codeContext preview
What this command does when you run it.
Intelligently refactor and improve code quality
Intelligently refactor and improve code quality
Follow this systematic approach to refactor code: **$ARGUMENTS**
1. **Pre-Refactoring Analysis**
2. **Test Coverage Verification**
3. **Refactoring Strategy**
4. **Environment Setup**
5. **Incremental Refactoring**
6. **Code Quality Improvements**
7. **Performance Optimizations**
8. **Design Pattern Application**
9. **Error Handling Improvement**
10. **Documentation Updates**
11. **Testing Enhancements**
12. **Static Analysis**
13. **Performance Verification**
14. **Integration Testing**
15. **Code Review Preparation**
16. **Documentation of Changes**
17. **Deployment Considerations**
Remember: Refactoring should preserve external behavior while improving internal structure. Always prioritize safety over speed, and maintain comprehensive test coverage throughout the process.
A comprehensive development toolkit designed following Anthropic's Claude Code Best Practices for AI-assisted software development.
Repo: qdhenry/Claude-Command-Suite
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Analyze semantic position relative to knowledge boundaries to prevent hallucination and identify uncertainty zones.
Generate a visual heatmap of knowledge boundaries showing safe zones, risk areas, and semantic coverage.
Evaluate the current risk level and provide detailed analysis of potential hallucination or reasoning failure.
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles:…