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)?

1. 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?

1. 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

1. 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