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>

Token Optimization

Expected range: 1,200–3,500 tokens (initial), 200 tokens (no high-risk patterns)

Caching: Caches issue predictions in .claude/cache/predict-issues/ for 7 days. Invalidated on new commits.

Early exit: Returns immediately if no high-risk patterns are detected in changed files.

Patterns used: Grep-before-Read, early exit, git diff scope default, caching