code-duplication

name	code-duplication
description	Deep analysis of codebase for code duplication. Detects exact, structural, and pattern-level duplicates, generates comprehensive reports with refactoring suggestions and metrics.. Use when Codex should run the converted code-duplication workflow.

Code Duplication

Converted Claude skill workflow for Codex/OpenAI use.

Source

Converted from skills/code-duplication/SKILL.md.

Bundled Resources

Supporting files copied from the Claude source:

• references/README.md
• scripts
• scripts/skill.sh

Converted Instructions

The content below was adapted from the Claude source. Rewrite tool and runtime assumptions as needed when they refer to Claude-only features.

Code Duplication Analysis Skill

Comprehensive code duplication detection and analysis for all programming languages.

Use this skill to identify technical debt from code duplication, quantify duplication metrics, and receive actionable refactoring suggestions.

Usage

# Analyze entire codebase
$code-duplication

# Analyze specific directory
$code-duplication src/components

# Analyze with custom configuration
$code-duplication --config .duplication-config.json

# Generate JSON output
$code-duplication --format json --output duplication-report.json

What This Skill Does

Performs comprehensive duplicate detection to answer:

• What code blocks are duplicated across the codebase?
• What is the overall duplication percentage?
• Which files have the most duplication?
• How much LOC could be saved through refactoring?
• What specific refactoring techniques should be applied?

Detection Types

1. Exact Duplicates

Identical code blocks (copy-pasted code)

• Threshold: 5-10 lines minimum
• Ignores whitespace and comments
• Groups all instances together

2. Structural Duplicates

Similar logic with minor variations

• Uses AST-based comparison for Python/JavaScript
• Detects code with different variable names but same structure
• Similarity scoring with configurable threshold

3. Pattern Duplicates

Repeated coding patterns that could be abstracted

• Common anti-patterns (repeated try-catch, validation logic, etc.)
• Opportunities for extract function/class refactoring
• Pattern catalog extensible

Report Output

Markdown Report (Default)

# Code Duplication Analysis Report

## Executive Summary
- Total LOC analyzed: 12,450
- Duplicate LOC: 1,834 (14.7%)
- Duplicate blocks found: 47
- Estimated LOC reduction: 1,200-1,500

## Top Offenders
1. src/services/user_service.py - 23% duplication (340 LOC)
2. src/api/handlers.py - 18% duplication (245 LOC)
...

## Detailed Findings
### Duplicate Block #1 (Exact)
**Instances**: 4 files
**LOC per instance**: 23 lines
**Potential savings**: 69 lines

**Locations**:
- src/services/user_service.py:145-167
- src/services/order_service.py:89-111
...

**Refactoring Suggestion**:
Extract common authentication logic into shared function:
...

JSON Report (--format json)

{
  "summary": {
    "total_files": 156,
    "total_loc": 12450,
    "duplicate_loc": 1834,
    "duplication_percentage": 14.7,
    "duplicate_blocks": 47
  },
  "duplicates": [...],
  "top_offenders": [...],
  "heatmap": {...}
}

Configuration

Create .duplication-config.json in your project root:

{
  "min_lines": 5,
  "exclude_patterns": ["**/test_*.py", "**/migrations/*.py"],
  "similarity_threshold": 0.85,
  "ignore_comments": true,
  "ignore_whitespace": true,
  "languages": ["python", "javascript", "typescript"]
}

Quality Metrics

This skill provides:

• Duplication Percentage: % of codebase that is duplicated
• Top Offenders: Files ranked by duplication amount
• File Heatmap: Visual representation of duplication distribution
• Cleanup Potential: Estimated LOC reduction from refactoring

Technical Details

• Zero external dependencies: Pure Python 3.8+ stdlib
• Multi-language support: Python, JavaScript, TypeScript, and more
• Performance: < 30 seconds for 10,000 LOC
• Memory efficient: Streaming file processing
• Git integration: Supports incremental mode (analyze only changed files)

Implementation Status

🚧 Currently in development - Wave 1 (Foundation) complete

Completed (Wave 1):

• ✅ Data models
• ✅ Configuration loader
• ✅ Utility functions

Planned (Wave 2-6):

• ⏳ Detection engines
• ⏳ Metrics calculation
• ⏳ Report generation
• ⏳ CLI interface

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Estimated completion: Waves 2-6 (~62 hours remaining) Next session: Execute Wave 2 (Detection Engines)