SKILL·E9782E

finding-duplicate-functions

obra
更新于 20 days ago
202 次查看
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432
在 GitHub 上查看
元ai

关于

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.

快速安装

Claude Code

推荐
主要方式
npx skills add obra/superpowers-lab -a claude-code
插件命令备选方式
/plugin add https://github.com/obra/superpowers-lab
Git 克隆备选方式
git clone https://github.com/obra/superpowers-lab.git ~/.claude/skills/finding-duplicate-functions

在 Claude Code 中复制并粘贴此命令以安装该技能

技能文档

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

PhaseToolModelOutput
1. Extractscripts/extract-functions.sh-catalog.json
2. Categorizescripts/categorize-prompt.mdhaikucategorized.json
3. Splitscripts/prepare-category-analysis.sh-categories/*.json
4. Detectscripts/find-duplicates-prompt.mdopusduplicates/*.json
5. Reportscripts/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:

ZoneCommon Duplicates
utils/, helpers/, lib/General utilities reimplemented
Validation codeSame checks written multiple ways
Error formattingError-to-string conversions
Path manipulationJoining, resolving, normalizing paths
String formattingCase conversion, truncation, escaping
Date formattingSame formats implemented repeatedly
API response shapingSimilar 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 仓库

obra/superpowers-lab
路径: skills/finding-duplicate-functions
0
FAQ

常见问题

什么是 finding-duplicate-functions Skill?

finding-duplicate-functions 是一个 Claude Skill,作者为 obra。Skill 将 Claude 按需加载的说明和资源打包,让 Claude 无需额外提示即可执行与 finding-duplicate-functions 相关的任务。

如何安装 finding-duplicate-functions?

使用本页的安装命令:将 finding-duplicate-functions 作为插件添加到 Claude Code,或将其仓库克隆到 skills 目录,然后重启 Claude 以加载该 Skill。

finding-duplicate-functions 属于哪个分类?

finding-duplicate-functions 属于元分类。

finding-duplicate-functions 可以免费使用吗?

可以。finding-duplicate-functions 已收录在 AIMCP,可免费安装。

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