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/finding-duplicate-functions

@344b079
by Jesse Vincentobra/superpowers-lab430 stars
35

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.

Use this Skill: https://skilld.dev/gh/obra/superpowers-lab/finding-duplicate-functions

This session only. Nothing lands on disk.

SKILL.md

≈70 tokens always: the name and description. ≈1.1k when used: this file. ≈1.4k more on demand in 2 files.

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.

Source: SKILL.md on GitHub

2 warnings17d5 checks · Risk SAFE
  • Gen Agent Trust Hub17d

    The skill identifies duplicate code functions by extracting code snippets and processing them through subagents. It is generally safe but lacks protection against indirect prompt injections that might be hidden in the code being audited.

  • Socket17d

    1 alert: gptAnomaly

  • Snyk17d

    Risk: LOW · No issues

  • Runlayer7mo

    6/6 files flagged

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Signed by skilld at 344b079. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 2 months ago.

Steadyupdated 9 months ago

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