finding-duplicate-functions

name	finding-duplicate-functions
description	Use when auditing a codebase for semantic duplication - functions that do the same thing but have different names or implementations. Especially useful for LLM-generated codebases where new functions are often created rather than reusing existing ones.

Finding Duplicate-Intent Functions

Overview

LLM-generated codebases accumulate semantic duplicates: functions that serve the same purpose but were implemented independently. Classical copy-paste detectors (jscpd) find syntactic duplicates but miss "same intent, different implementation."

This skill uses a two-phase approach: classical extraction followed by LLM-powered intent clustering.

When to Use

• Codebase has grown organically with multiple contributors (human or LLM)
• You suspect utility functions have been reimplemented multiple times
• Before major refactoring to identify consolidation opportunities
• After jscpd has been run and syntactic duplicates are already handled

Quick Reference

Phase	Tool	Model	Output
1. Extract	scripts/extract-functions.sh	-	catalog.json
2. Categorize	scripts/categorize-prompt.md	haiku	categorized.json
3. Split	scripts/prepare-category-analysis.sh	-	categories/*.json
4. Detect	scripts/find-duplicates-prompt.md	opus	duplicates/*.json
5. Report	scripts/generate-report.sh	-	report.md

Process

digraph duplicate_detection {
  rankdir=TB;
  node [shape=box];

  extract [label="1. Extract function catalog\n./scripts/extract-functions.sh"];
  categorize [label="2. Categorize by domain\n(haiku subagent)"];
  split [label="3. Split into categories\n./scripts/prepare-category-analysis.sh"];
  detect [label="4. Find duplicates per category\n(opus subagent per category)"];
  report [label="5. Generate report\n./scripts/generate-report.sh"];
  review [label="6. Human review & consolidate"];

  extract -> categorize -> split -> detect -> report -> review;
}

Phase 1: Extract Function Catalog

./scripts/extract-functions.sh src/ -o catalog.json

Options:

• -o FILE: Output file (default: stdout)
• -c N: Lines of context to capture (default: 15)
• -t GLOB: File types (default: *.ts,*.tsx,*.js,*.jsx)
• --include-tests: Include test files (excluded by default)

Test files (*.test.*, *.spec.*, __tests__/**) are excluded by default since test utilities are less likely to be consolidation candidates.

Phase 2: Categorize by Domain

Dispatch a haiku subagent using the prompt in scripts/categorize-prompt.md.

Insert the contents of catalog.json where indicated in the prompt template. Save output as categorized.json.

Phase 3: Split into Categories

./scripts/prepare-category-analysis.sh categorized.json ./categories

Creates one JSON file per category. Only categories with 3+ functions are worth analyzing.

Phase 4: Find Duplicates (Per Category)

For each category file in ./categories/, dispatch an opus subagent using the prompt in scripts/find-duplicates-prompt.md.

Save each output as ./duplicates/{category}.json.

Phase 5: Generate Report

./scripts/generate-report.sh ./duplicates ./duplicates-report.md

Produces a prioritized markdown report grouped by confidence level.

Phase 6: Human Review

Review the report. For HIGH confidence duplicates:

1. Verify the recommended survivor has tests
2. Update callers to use the survivor
3. Delete the duplicates
4. Run tests

High-Risk Duplicate Zones

Focus extraction on these areas first - they accumulate duplicates fastest:

Zone	Common Duplicates
utils/, helpers/, lib/	General utilities reimplemented
Validation code	Same checks written multiple ways
Error formatting	Error-to-string conversions
Path manipulation	Joining, resolving, normalizing paths
String formatting	Case conversion, truncation, escaping
Date formatting	Same formats implemented repeatedly
API response shaping	Similar transformations for different endpoints

Common Mistakes

Extracting too much: Focus on exported functions and public methods. Internal helpers are less likely to be duplicated across files.

Skipping the categorization step: Going straight to duplicate detection on the full catalog produces noise. Categories focus the comparison.

Using haiku for duplicate detection: Haiku is cost-effective for categorization but misses subtle semantic duplicates. Use Opus for the actual duplicate analysis.

Consolidating without tests: Before deleting duplicates, ensure the survivor has tests covering all use cases of the deleted functions.