How is this different from a basic 'grep' or standard linting tool?

Unlike standard linters, this skill uses identifier abstraction and normalization to find 'near-duplicates' where variables were renamed or comments were changed, which simple text searches typically miss.

Which programming languages does the detector support?

The skill is language-agnostic; because it focuses on logical patterns and structural normalization, it can detect duplicates across Python, JavaScript, C++, and many other common programming languages.

Can I integrate this into my CI/CD pipeline to block messy commits?

Yes, it provides specific exit codes and configurable thresholds, allowing you to automatically fail builds or pull requests if the percentage of duplicate code exceeds your team's limits.

What exactly is included in the purchase?

You will receive the complete detection engine, documentation for CLI integration, and example configuration files to help you set up duplication thresholds and file exclusions immediately.

Are there any performance issues when scanning massive enterprise codebases?

While it can handle large projects, we recommend using the file globbing feature to exclude third-party libraries or build artifacts to ensure the fastest possible scanning performance.

Duplicate Code Detector

Example session with this skill installed

Scan the src/ directory for any code clones longer than 8 lines, including cases where variables were renamed. Report the duplication percentage.

Read your context and instructions
Compiled the duplicate code detector
Generated the document

I have completed a deep scan of the src/ directory. By normalizing identifiers and ignoring whitespace, I identified 4 distinct clone groups. The overall duplication rate for this directory is 8.4%.

Duplicate Code Analysis Report

Clone Group	Occurrences	Avg. Lines	Similarity Type	Locations
GROUP_001	3	12	Type-2 (Renamed)	`src/auth/service.py:45`, `src/users/service.py:112`
GROUP_002	2	10	Type-1 (Exact)	`src/ui/buttons.tsx:12`, `src/ui/inputs.tsx:84`
GROUP_003	2	9	Type-2 (Renamed)	`src/api/v1/handlers.go:204`, `src/api/v2/handlers.go:198`
GROUP_004	4	8	Type-1 (Exact)	`src/db/migrations/001_init.sql`, `src/db/migrations/002_add_logs.sql`

Refactoring Recommendations

Group 001: The logic for validate_session_token is repeated with different variable names in Auth and User services. Recommend extracting to src/common/security.py.

Group 002: Exact CSS-in-JS object detected in two UI components. Move to a shared theme.ts constant.

Group 003: API response wrapping logic is duplicated across versions. Wrap in a middleware or a shared response utility.

Next steps

Extract the validate_session_token logic to a shared service.
Run the scanner with --fail --max-percent 5 in your CI config to prevent future regressions.
Consolidate the UI styling constants into the design system.

buttons.tsx

PDF · document

Generated

Example file from a real run - the skill writes it into your workspace.

Connects securely to your tools. The creator never sees your data.

About this skill

Duplicate code blocks often bypass standard linters and static analysis, leading to high maintenance debt and hidden bugs. This skill scans your codebase to identify identical (Type-1) and renamed (Type-2) clones that simple grep commands miss.

What it does

Normalization scanning ignores whitespace, comments, and literals to find logical duplicates.
Identifier abstraction detects code blocks where variables or functions were renamed.
Clone grouping merges overlapping matches into logical groups for refactoring.
Duplication metrics calculates percentage of duplicate lines per file and across the project.
CI enforcement provides exit codes to fail builds if duplication exceeds a set threshold.

How it works

Run the detector on a target directory or file glob.
Specify the minimum line count to filter out trivial repetitions.
Toggle identifier ignoring to catch near-duplicate logic.
Review the report of clone groups and their specific locations.

Frameworks & tools

This tool uses a Python-based implementation compatible with any language. It supports standard CLI integration for use in local environments or CI/CD pipelines.

Why this beats prompting it yourself

Manual prompting often fails to track rolling hashes or maintain state across large file trees. This skill handles the heavy lifting of normalization and percentage calculation that LLMs struggle to do accurately at scale.

Use cases

Identify refactoring opportunities for shared utility modules.
Audit a legacy codebase to quantify technical debt.
Prevent copy-paste growth during large-scale migrations.
Set a hard quality gate in CI pipelines for team projects.

Known limitations

Requires Python 3 environment. Large codebases may require specific file exclusions to maintain performance.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

Reviews

No reviews yet

Be one of the first to try it. Every listed skill passes our trust checks below.

Security scanned

Passed our 8-point scan before listing

Fresh listing

Recently published to Agensi

30-day refund

Not a fit? Get your money back

Duplicate Code Detector