Predictive Code Analysis

I'll analyze your codebase to predict potential problems before they impact your project.

Strategic Thinking Process

<think> To make accurate predictions, I need to consider:

1. Pattern Recognition

   • Which code patterns commonly lead to problems?
   • Are there growing complexity hotspots?
   • Do I see anti-patterns that will cause issues at scale?
   • Are there ticking time bombs (hardcoded values, assumptions)?

2. Risk Assessment Framework

   • Likelihood: How probable is this issue to occur?
   • Impact: How severe would the consequences be?
   • Timeline: When might this become a problem?
   • Effort: How hard would it be to fix now vs later?

3. Common Problem Categories

   • Performance: O(n²) algorithms, memory leaks, inefficient queries
   • Maintainability: High complexity, poor naming, tight coupling
   • Security: Input validation gaps, exposed secrets, weak auth
   • Scalability: Hardcoded limits, single points of failure

4. Prediction Strategy

   • Start with highest risk areas (critical path code)
   • Look for patterns that break at 10x, 100x scale
   • Check for technical debt accumulation
   • Identify brittleness in integration points </think>

Based on this analysis framework, I'll use native tools for comprehensive analysis:

• Grep tool to search for problematic patterns
• Glob tool to analyze file structures and growth
• Read tool to examine complex functions and hotspots

I'll examine:

• Code complexity trends and potential hotspots
• Performance bottleneck patterns forming
• Maintenance difficulty indicators
• Architecture stress points and scaling issues
• Error handling gaps

For each prediction, I'll:

• Show specific code locations with file references
• Explain why it's likely to cause future issues
• Estimate potential timeline and impact
• Suggest preventive measures with priority levels

When I find multiple issues, I'll create a todo list for systematic review and prioritization.

Analysis areas:

• Functions approaching complexity thresholds
• Files with high change frequency (potential hotspots)
• Dependencies with known issues or update requirements
• Performance patterns that don't scale
• Code duplication leading to maintenance issues

After analysis, I'll ask: "How would you like to track these predictions?"

• Create todos: I'll add items to track resolution progress
• Create GitHub issues: I'll generate properly formatted issues with details
• Summary only: I'll provide actionable report without task creation

Important: I will NEVER:

• Add "Created by Claude" or any AI attribution to issues
• Include "Generated with Claude Code" in descriptions
• Modify repository settings or permissions
• Add any AI/assistant signatures or watermarks

Predictions will include:

• Risk level assessment (Critical/High/Medium/Low)
• Estimated timeline for potential issues
• Specific remediation recommendations
• Impact assessment on project goals

This helps prevent problems before they impact your project, saving time and maintaining code quality proactively.