About
This skill identifies semantically duplicate functions in codebases that perform the same task but have different names or implementations, which is common in LLM-generated code. It uses a two-phase approach combining classical extraction with LLM-powered intent clustering to find duplicates that syntactic tools miss. Developers should use it when auditing or refactoring a codebase to find opportunities for consolidating utility functions.
Quick Install
Claude Code
Recommendednpx skills add obra/superpowers-lab -a claude-code/plugin add https://github.com/obra/superpowers-labgit clone https://github.com/obra/superpowers-lab.git ~/.claude/skills/finding-duplicate-functionsCopy and paste this command in Claude Code to install this skill
Documentation
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:
- Verify the recommended survivor has tests
- Update callers to use the survivor
- Delete the duplicates
- 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.
GitHub Repository
Frequently asked questions
What is the finding-duplicate-functions skill?
finding-duplicate-functions is a Claude Skill by obra. Skills package instructions and resources that Claude loads on demand, so Claude can perform finding-duplicate-functions-related tasks without extra prompting.
How do I install finding-duplicate-functions?
Use the install commands on this page: add finding-duplicate-functions to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does finding-duplicate-functions belong to?
finding-duplicate-functions is in the Meta category, tagged ai.
Is finding-duplicate-functions free to use?
Yes. finding-duplicate-functions is listed on AIMCP and free to install.
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