clean-code-reviewer_skill

This skill analyzes code quality per Clean Code principles, spotting naming, function size, duplication, over-engineering, and magic numbers with actionable

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Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill hylarucoder/skills-for-vibe-coder --skill clean-code-reviewer

SKILL.md4.5 KB

Overview

This skill analyzes code quality using Clean Code principles and highlights naming, function size, duplication, over-engineering, magic numbers, clarity, and convention issues. It rates each finding by severity and returns concrete refactoring suggestions that preserve existing behavior. The output is a prioritized report that developers can act on immediately.

How this skill works

The reviewer scans the codebase or specified scope and runs checks across seven dimensions: naming, functions, duplication (DRY), YAGNI/over-design, magic numbers, structural clarity, and project conventions. For each issue it records file locations, assigns a severity (high/medium/low), describes the problem, and proposes minimal refactorings that do not change functionality. Reports are sorted by severity and can be scoped by directory, dimension, or language for parallel review.

When to use it

Before a refactor or cleanup sprint to prioritize technical debt
As part of PR reviews to catch code smells early
When onboarding to enforce consistent naming and conventions
To audit legacy modules for over-engineering and duplication
When preparing a release and you need quick maintainability fixes

Best practices

Run the review on focused scopes (module, directory, or single PR) to produce actionable results
Treat recommendations as implementation suggestions—never change external behavior without tests
Prioritize high-severity issues first: naming, large functions, and duplicated logic
Use parameter objects and constants to replace long parameter lists and magic numbers
Combine automated linting with this review for consistent enforcement

Example use cases

--scope=components to detect duplicated UI logic and inconsistent props naming
--dimension=naming to list meaningless identifiers and unify naming across modules
Review a legacy service to find unused compatibility branches (YAGNI) and remove dead code safely
Scan for magic numbers and generate a constants extraction plan
Run per-language checks (TypeScript/Python/Go) in parallel and merge a unified severity-sorted report

FAQ

No. It only produces findings and refactoring suggestions. All suggestions preserve behavior and require manual or CI-driven application.

How are severities determined?

Severity is based on maintainability and readability impact: high for issues that harm comprehension or increase bug risk, medium for notable improvements, and low for optional stylistic cleanups.