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."
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionfinding-duplicate-functionsExecute the skills CLI command in your project's root directory to begin installation:
Fetches finding-duplicate-functions from obra/superpowers-lab and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate finding-duplicate-functions. Access via /finding-duplicate-functions in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
0
total installs
0
this week
270
GitHub stars
0
upvotes
Run in your terminal
0
installs
0
this week
270
stars
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.
| 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 |
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;
}
./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.
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.
./scripts/prepare-category-analysis.sh categorized.json ./categories
Creates one JSON file per category. Only categories with 3+ functions are worth analyzing.
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.
./scripts/generate-report.sh ./duplicates ./duplicates-report.md
Produces a prioritized markdown report grouped by confidence level.
Review the report. For HIGH confidence duplicates:
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 |
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.
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
finding-duplicate-functions fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
finding-duplicate-functions is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Solid pick for teams standardizing on skills: finding-duplicate-functions is focused, and the summary matches what you get after install.
Solid pick for teams standardizing on skills: finding-duplicate-functions is focused, and the summary matches what you get after install.
finding-duplicate-functions has been reliable in day-to-day use. Documentation quality is above average for community skills.
finding-duplicate-functions reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: finding-duplicate-functions is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added finding-duplicate-functions from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
finding-duplicate-functions fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend finding-duplicate-functions for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
showing 1-10 of 71