Tech Debt

Tech Debt Mode

Identify, catalog, and eliminate technical debt.

Core Philosophy

"Deletion is the most powerful refactoring."

The 40% Rule: In AI-assisted coding, expect to spend 30-40% of your time on code health—reviews, smell detection, and refactoring. Without this investment, vibe-coded bases accumulate invisible debt that slows agents and breeds bugs. Schedule regular code health passes, not just reactive fixes.

Every line of code:

• Must be understood
• Must be tested
• Must be maintained
• Can contain bugs

Less code = less of all the above.

Debt Indicators to Find

Rationalization Prevention

Process

1. Scan

Search for debt indicators across the codebase:

• Grep for TODO/FIXME comments
• Find functions over threshold length
• Identify files with type errors
• Check for unused exports

2. Categorize

For each finding, assess:

• Severity: How bad is this?
• Effort: How hard to fix?
• Risk: What could go wrong?

3. Prioritize

Focus on:

• 🎯 Quick Wins - Low effort, high impact
• 🔒 Safety First - Fix risky debt before adding features
• 📍 Hot Paths - Prioritize frequently-touched code

4. Fix or Document

• Simple fixes: Just do it (with tests)
• Complex fixes: Create a plan for later

Quick Win Examples

• Dead imports: Remove unused imports (e.g., from typing import List, Dict, Optional when only Optional is used)
• Bare excepts: Replace except: pass with specific exception handling and logging
• Unused variables: Delete variables that are assigned but never read

Tech Debt Report Format

## Tech Debt Analysis

### Summary

- **Total issues found**: X
- **Critical**: X (fix immediately)
- **Quick wins**: X (easy to fix)
- **Requires planning**: X (complex)

### Findings

#### Critical 🔴

| Location     | Type     | Issue                     | Effort |
| ------------ | -------- | ------------------------- | ------ |
| `file.py:42` | security | bare except hiding errors | Low    |

#### Quick Wins 🎯

| Location      | Type   | Issue             | Effort |
| ------------- | ------ | ----------------- | ------ |
| `utils.py:10` | unused | import never used | Low    |

#### Requires Planning 📋

| Location | Type        | Issue              | Why Complex              |
| -------- | ----------- | ------------------ | ------------------------ |
| `api.py` | duplication | 3 similar handlers | Needs abstraction design |

### Recommendations

[Suggested order of tackling debt]

### Fixed This Session

[List of debt items resolved]

When Fixing Debt

• ✅ Run tests after each change
• ✅ Keep changes atomic and focused
• ✅ Verify no regressions
• ❌ Don't mix debt fixes with new features
• ❌ Don't "refactor" working code without reason

Safe Deletion Patterns

Before removing code, verify it's unused:

# Check for usages
ag "function_name" --python

# Check imports
ag "from module import function_name"

Watch for code that might be used dynamically:

# ✅ Safe to delete: unused import
from typing import List  # 'List' never used in file

# ✅ Safe to delete: unused variable
result = calculate()  # 'result' never read
log(value)  # This is the actual intent

# ✅ Safe to delete: dead branch
if False:  # Will never execute
    do_something()

# ⚠️ Verify first: might be used dynamically
def _helper():  # Underscore suggests private, but check usages
    pass

# ❌ Don't delete without checking: exported function
def public_api():  # Might be called by external code
    pass

Also watch for:

• Dynamically called code (getattr, eval)
• Reflection-based frameworks
• External API contracts
• CLI entry points

Cleaning Checklist

- [ ] Unused imports removed
- [ ] Unused variables removed
- [ ] Dead functions removed
- [ ] Commented-out code removed
- [ ] Debug statements removed
- [ ] Duplicate code consolidated
- [ ] Tests still pass
- [ ] Types still check

Debt Prevention Tips

Add TODOs with issue tracker links, use type hints from the start, and review for simplification opportunities.

"The best code is no code at all."