---

name: tech-debt-check description: Detect technical debt patterns in code including duplication, complexity, and maintainability issues. Use at phase checkpoints or on-demand to assess code quality.

Technical Debt Check Skill

Analyze the codebase for technical debt patterns that commonly accumulate during AI-assisted development.

Why This Matters

Research shows AI-generated code creates:

• 8x increase in code duplication (GitClear 2024)
• 1.64x more maintainability issues than human code
• Frequent DRY principle violations

This skill catches these issues before they compound.

Workflow Overview

Copy1. Detect project language/framework
2. Run duplication analysis
3. Run complexity analysis
4. Run file size analysis
5. Check for common AI code smells
6. Generate report with actionable items

Thresholds Reference

Step 1: Detect Project Type

Identify the project's primary language and available tooling:

If no package manager found, fall back to file extension analysis.

Step 2: Duplication Analysis

Check for duplicate code blocks (a primary AI coding failure mode).

Using jscpd (JS/TS projects)

Copy# Install if needed
npm list -g jscpd || npm install -g jscpd

# Run analysis
jscpd src/ --min-lines 5 --min-tokens 50 --reporters json --output .tech-debt-report/

Parse output for total duplicate lines, percentage, and specific blocks (file, start line, end line).

Manual detection (fallback)

If jscpd unavailable, use grep-based pattern matching for repeated code blocks.

Step 3: Complexity Analysis

JavaScript/TypeScript

Use eslint with complexity rules:

Copynpx eslint src/ --rule '{"complexity": ["error", 10]}' --format json

Or check manually for:

• Functions with >10 branches
• Nested callbacks >3 levels deep
• Files with >300 lines

Python

Use radon for cyclomatic complexity:

Copyradon cc src/ -a -s --json

Or use pylint:

Copypylint src/ --disable=all --enable=R0912,R0915 --output-format=json

Step 4: File Size Analysis

Large files often indicate poor separation of concerns. Find files exceeding thresholds:

Copyfind src/ -name "*.ts" -o -name "*.js" -o -name "*.py" | xargs wc -l | sort -rn | head -20

Step 5: AI Code Smell Detection

Check for patterns commonly produced by AI:

5.1 Excessive Error Handling

Copy# Check try-catch density (ratio > 1:1 suggests over-defensive code)
echo "try blocks: $(grep -r 'try {' src/ | wc -l)"
echo "catch blocks: $(grep -r 'catch' src/ | wc -l)"

5.2 Unused Code

Copy# TypeScript/JavaScript
npx eslint src/ --rule '{"no-unused-vars": "error"}' --format json

# Python
pylint src/ --disable=all --enable=W0611,W0612 --output-format=json

5.3 Inconsistent Patterns

Look for multiple implementations of the same concern (date formatting, HTTP clients, validation):

Copygrep -r "new Date\|moment\|dayjs\|date-fns" src/ | cut -d: -f1 | sort | uniq -c
grep -r "fetch\|axios\|got\|request" src/ | cut -d: -f1 | sort | uniq -c

5.4 Comment Ratio

Healthy ratio is 10-20%. AI tends to over-comment or under-comment.

Step 6: Generate Report

CopyTECHNICAL DEBT REPORT
=====================
Project: {name}
Analyzed: {timestamp}
Files scanned: {N}

SUMMARY
-------
Overall Health: GOOD | WARNING | CRITICAL
Tech Debt Score: {0-100} (lower is better)

DUPLICATION ({status})
----------------------
Duplicate code: {N} blocks, {X}% of codebase
Largest duplicates:
1. {file1}:{lines} ↔ {file2}:{lines} ({N} lines)
2. {file1}:{lines} ↔ {file2}:{lines} ({N} lines)

Action: Consider extracting to shared utility

COMPLEXITY ({status})
---------------------
Average complexity: {N}
High complexity functions:
1. {file}:{function} — complexity {N}
2. {file}:{function} — complexity {N}

Action: Refactor functions with complexity >15

FILE SIZE ({status})
--------------------
Large files (>300 lines):
1. {file} — {N} lines
2. {file} — {N} lines

Action: Split into smaller, focused modules

AI CODE SMELLS ({status})
-------------------------
- Excessive try-catch: {found/not found}
- Unused code: {N} instances
- Inconsistent patterns: {list}

Action: Review flagged patterns for consolidation

RECOMMENDATIONS
---------------
Priority fixes:
1. {specific action with file reference}
2. {specific action with file reference}
3. {specific action with file reference}

Deferred items:
- {lower priority items}

Integration with Phase Checkpoint

When invoked from /phase-checkpoint:

1. Run full analysis
2. Return summary status: PASSED | PASSED WITH NOTES | FAILED
3. FAILED if any CRITICAL thresholds exceeded
4. PASSED WITH NOTES if WARNING thresholds exceeded
5. PASSED if all metrics GOOD

Exit Criteria

Limitations

• Duplication detection requires jscpd or similar tool
• Complexity analysis requires language-specific linters
• Manual review still needed for semantic duplication
• Cannot detect architectural debt or design issues

Example

Given a TypeScript project:

Copy$ /tech-debt-check

TECHNICAL DEBT REPORT
=====================
Project: my-api
Analyzed: 2025-01-10 14:30:00
Files scanned: 45

SUMMARY
-------
Overall Health: WARNING
Tech Debt Score: 34/100

DUPLICATION (WARNING)
----------------------
Duplicate code: 8 blocks, 4.2% of codebase

Largest duplicates:
1. src/api/users.ts:45-60 ↔ src/api/posts.ts:32-47 (15 lines)
   → Both validate request body identically

Action: Extract to src/middleware/validateBody.ts

COMPLEXITY (GOOD)
-----------------
Average complexity: 4.2
No functions exceed threshold.

FILE SIZE (WARNING)
-------------------
Large files:
1. src/services/auth.ts — 342 lines

Action: Split token management into separate module

AI CODE SMELLS (GOOD)
---------------------
No significant issues detected.

RECOMMENDATIONS
---------------
Priority fixes:
1. Extract duplicate validation logic (saves 30 lines)
2. Split auth.ts into auth.ts + tokens.ts

Status: PASSED WITH NOTES