Predict Issues

Predictive Code Analysis

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

Strategic Thinking Process

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

Token Optimization:

• ✅ Grep-before-Read for pattern detection (no speculative reads)
• ✅ Glob for file structure analysis (no file reads)
• ✅ Progressive disclosure (critical → high → medium → low)
• ✅ Focus area flags (--performance, --security, --maintainability, --scalability)
• ✅ Default to git diff scope (changed files + dependencies)
• ✅ Caching issue patterns and hotspot analysis
• ✅ Early exit when no high-risk patterns found - saves 85%
• Expected tokens: 1,200-3,500 (vs. 3,000-5,000 unoptimized) - 50-60% reduction
• Optimization status: ✅ Optimized (Phase 2 Batch 3B, 2026-01-26)

Caching Behavior:

• Cache location: .claude/cache/predict-issues/
• Caches: Known issue patterns, complexity hotspots, dependency risks
• Cache validity: Until codebase structure changes significantly
• Shared with: /review, /security-scan, /complexity-reduce skills

Usage:

• predict-issues - Analyze changed files (default, 1,200-2,000 tokens)
• predict-issues all - Full codebase analysis (3,000-3,500 tokens)
• predict-issues --performance - Performance issues only (800-1,500 tokens)
• predict-issues --security - Security risks only (800-1,500 tokens)
• predict-issues --maintainability - Code quality issues (1,000-2,000 tokens)

Token Optimization Strategy

Efficiency Patterns (60% Reduction: 4,000-6,000 → 1,500-2,500 tokens)

Phase 1: Intelligent Scoping (Saves 40-50%)

# 1. Default to git diff scope (changed files only)
git diff --name-only HEAD

# 2. Early exit for low-risk changes
# Skip analysis if only: docs/, tests/, README, config without logic
# Saves 85% tokens when no actionable issues

# 3. Focus area flags reduce scope
--performance    # Only performance patterns (50% reduction)
--security       # Only security risks (50% reduction)
--maintainability # Only code quality (40% reduction)
--scalability    # Only scaling issues (50% reduction)

Phase 2: Pattern-Based Detection with Grep (Saves 30-40%)

# Performance issue patterns
Grep "for.*for.*for" --output_mode content  # Nested loops (O(n³))
Grep "\.map\(.*\.filter\(.*\.map\(" --output_mode content  # Chained array ops
Grep "SELECT.*FROM.*WHERE.*IN.*SELECT" --output_mode content  # N+1 queries
Grep "setTimeout|setInterval" --output_mode content  # Potential memory leaks
Grep "new.*\[\].*push" --output_mode content  # Array pre-allocation issues

# Security risk patterns
Grep "eval\(|new Function\(" --output_mode content  # Code injection risks
Grep "innerHTML|dangerouslySetInnerHTML" --output_mode content  # XSS risks
Grep "Math\.random|Date\.now" --output_mode content  # Weak randomness
Grep "http://|process\.env\." --output_mode content  # Config issues

# Maintainability patterns
Grep "function.*\{$" -A 100 --output_mode content  # Long functions
Grep "if.*\{.*if.*\{.*if.*\{" --output_mode content  # Deep nesting
Grep "any|unknown" --type ts --output_mode content  # Type safety gaps
Grep "TODO|FIXME|HACK|XXX" --output_mode content  # Technical debt markers

# Scalability patterns
Grep "hardcoded|localhost|127\.0\.0\.1" --output_mode content  # Env coupling
Grep "sleep\(|Thread\.sleep|time\.sleep" --output_mode content  # Blocking calls
Grep "global|window\.|process\." --output_mode content  # Global state
Grep ".*\.length > [0-9]{3}" --output_mode content  # Hardcoded limits

Phase 3: Git History Analysis (Saves 20-30%)

# Identify hotspot files (frequent bugs = future bugs)
git log --format="%H" --all -- "*.{js,ts,py,go,java}" | head -100 | \
  xargs -I {} git diff-tree --no-commit-id --name-only -r {} | \
  sort | uniq -c | sort -rn | head -20

# Files with most recent bug fixes (analyze commit messages)
git log --grep="fix\|bug\|issue" --format="%H" -n 50 | \
  xargs -I {} git diff-tree --no-commit-id --name-only -r {} | \
  sort | uniq -c | sort -rn | head -10

# Large files growing rapidly (complexity accumulation)
git log --format="%H" --all -n 100 | \
  xargs -I {} sh -c 'git show {}:path 2>/dev/null | wc -l' | \
  awk '{if ($1 > 500) print}'

Phase 4: Complexity Metrics Caching (Saves 20-30%)

# Cache cyclomatic complexity scores
# Run once, cache results in .claude/cache/predict-issues/complexity.json
# Format: {"file": "path/to/file.js", "score": 45, "functions": [...]}

# Use Bash-based metrics (fast, no file reads)
Bash: "find . -name '*.js' -exec sh -c 'echo \"{} $(grep -c \"if\\|for\\|while\\|case\" {})\"' \;"

# Cache dependency risk scores
# npm: check package.json age, deprecated packages
# python: check requirements.txt for CVEs
# go: check go.mod for outdated versions

Phase 5: Template-Based Issue Patterns (Saves 15-25%)

// Memory leak patterns
const MEMORY_LEAK_PATTERNS = [
  "addEventListener without removeEventListener",
  "setInterval without clearInterval",
  "DOM references in closures",
  "Growing global arrays/objects",
  "Unclosed database connections",
  "Event emitter listener accumulation"
];

// Race condition patterns
const RACE_CONDITION_PATTERNS = [
  "Async operations without await/then",
  "Shared state mutation in parallel code",
  "File operations without locks",
  "Multiple async setState calls",
  "Promise.all without error handling"
];

// Scalability anti-patterns
const SCALABILITY_ANTIPATTERNS = [
  "In-memory caching without limits",
  "Synchronous file I/O in request handlers",
  "Database queries in loops",
  "Missing pagination on large collections",
  "Unbounded thread pool creation"
];

// Detection: Grep for pattern keywords, Read only matching files

Phase 6: Progressive Disclosure (Saves 10-20%)

1. Quick scan (Grep patterns only) - 500-800 tokens
   ├─ Critical: Memory leaks, security holes, data loss risks
   ├─ High: Performance bottlenecks, scaling issues
   └─ Exit early if no critical/high issues found

2. Detailed analysis (Read specific files) - 1,000-1,800 tokens
   ├─ Only files with pattern matches
   ├─ Only functions exceeding complexity thresholds
   └─ Only hotspot files from git history

3. Full report (comprehensive) - 1,500-2,500 tokens
   ├─ Include medium/low priority issues
   ├─ Trend analysis and predictions
   └─ Detailed remediation plans

Optimization Implementation

Default Behavior (Git Diff Scope)

# 1. Get changed files since last commit
git diff --name-only HEAD

# 2. Get staged files
git diff --cached --name-only

# 3. Filter to source code (skip docs, configs)
# 4. Run pattern detection with Grep (no file reads)
# 5. Read only files with detected patterns
# 6. Early exit if no high-risk issues

# Result: 1,200-2,000 tokens (vs 4,000-6,000 full analysis)

Focus Area Optimization

# Performance focus (--performance)
- Grep: nested loops, array chaining, inefficient algorithms
- Git: files with performance-related commits
- Read: Only functions with O(n²)+ complexity
- Tokens: 800-1,500 (62% savings)

# Security focus (--security)
- Grep: eval, innerHTML, weak crypto, exposed secrets
- Static patterns: No git/Read needed for many checks
- Read: Only files with detected vulnerabilities
- Tokens: 800-1,500 (62% savings)

# Maintainability focus (--maintainability)
- Bash: Line counts, complexity scores (cached)
- Grep: Deep nesting, long functions, TODO markers
- Read: Only high-complexity hotspots
- Tokens: 1,000-2,000 (50% savings)

Caching Strategy

// .claude/cache/predict-issues/analysis-cache.json
{
  "lastAnalysis": "2026-01-27T10:30:00Z",
  "hotspotFiles": [
    {"path": "src/auth/login.ts", "bugCount": 12, "changeFrequency": 45},
    {"path": "src/api/users.ts", "bugCount": 8, "changeFrequency": 32}
  ],
  "complexityScores": {
    "src/auth/login.ts": 42,
    "src/api/users.ts": 38
  },
  "knownPatterns": {
    "performance": ["src/data/processor.ts:145", "src/utils/query.ts:67"],
    "security": ["src/api/upload.ts:89"],
    "maintainability": ["src/legacy/old-handler.ts:*"]
  },
  "lastGitCommit": "a1b2c3d4e5f6",
  "analysisCount": 5
}

Token Budget Allocation

Quick Scan (Default):
├─ Git operations: 100-200 tokens
├─ Grep patterns: 400-600 tokens
├─ Analysis & output: 700-1,200 tokens
└─ Total: 1,200-2,000 tokens ✅

Detailed Analysis (Focus Area):
├─ Git operations: 100-200 tokens
├─ Grep patterns: 300-500 tokens
├─ Read files: 400-800 tokens
├─ Analysis & output: 200-500 tokens
└─ Total: 1,000-2,000 tokens ✅

Full Analysis (--all):
├─ Git history: 200-300 tokens
├─ Grep patterns: 600-800 tokens
├─ Read hotspots: 400-800 tokens
├─ Complexity analysis: 200-400 tokens
├─ Report generation: 200-400 tokens
└─ Total: 1,600-2,700 tokens ✅

Real-World Examples

Example 1: Performance Issue Detection

# Traditional approach (4,000 tokens)
Read all .js/.ts files → Analyze all code → Report issues

# Optimized approach (1,200 tokens) - 70% savings
1. git diff --name-only HEAD  # 50 tokens
2. Grep nested loops in changed files  # 400 tokens
3. Read only files with O(n²)+ patterns  # 500 tokens
4. Generate focused report  # 250 tokens

Example 2: Security Risk Scan

# Traditional approach (5,000 tokens)
Read all source files → Security analysis → Detailed report

# Optimized approach (1,200 tokens) - 76% savings
1. Grep eval/innerHTML/crypto patterns  # 500 tokens
2. Grep exposed secrets/API keys  # 300 tokens
3. Read only vulnerable files  # 300 tokens
4. Generate risk report  # 100 tokens

Example 3: Low-Risk Changes (Early Exit)

# Changed files: README.md, docs/api.md, package.json (version bump)
1. git diff --name-only HEAD  # 50 tokens
2. Detect no source code changes  # 100 tokens
3. Skip analysis, return "No high-risk changes"  # 50 tokens
# Total: 200 tokens (vs 4,000) - 95% savings ✅

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

• Grep tool to search for problematic patterns (no file reads)
• Glob tool to analyze file structures and growth (no file reads)
• Read tool to examine complex functions and hotspots (only after Grep identifies specific risks)

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.

Converted and distributed by TomeVault — claim your Tome and manage your conversions.