clean-code Skill Review 2026

What clean-code is

This review covers the clean-code component from the public template library, categorized under development and marked with version 2.0 and critical priority. The component provides pragmatic coding standards focused on concise, direct, and solution-oriented AI coding behavior without over-engineering or unnecessary comments. It defines allowed tools including Read, Write, and Edit. You will find rules for core principles, naming conventions, function design, code structure, anti-patterns, pre-edit checks, self-checks, and mandatory verification scripts. The framework instructs you to write code directly, fix bugs immediately, and avoid tutorials or explanations. It also outlines exact agent mappings for running validation scripts and dictates a strict procedure to read script output, summarize findings, and ask for user confirmation before applying fixes to your codebase.

clean-code at a glance

The problem it addresses

The clean-code component addresses the problem of bloated, over-engineered code and excessive conversational padding during software development. Many coding workflows suffer from verbose explanations, unnecessary comments, deep nesting, and poorly named variables or functions that obscure intent. Developers and AI assistants often introduce unrequested helpers, factories, utility files with single functions, and tutorials when the user simply wants direct solutions. Furthermore, modifications to files frequently break dependent imports or ignore test coverage because changes are made in isolation without checking what imports the target file or what interface changes might propagate across the project. Another related issue is the lack of disciplined verification after making edits, where agents either skip validation checks or auto-fix errors without user consent. This component solves these problems by establishing strict coding standards, clear naming rules, function length limits, anti-pattern prohibitions, mandatory pre-edit analysis steps, and a structured protocol for running validation scripts and presenting results for user approval.

How to install and use it

To use the clean-code component in your environment, ensure your setup permits the allowed tools Read, Write, and Edit. When you are assigned a role such as frontend-specialist, backend-specialist, mobile-developer, database-architect, security-auditor, seo-specialist, performance-optimizer, or test-engineer, you must execute only your designated verification scripts after completing your work. For general validation tasks or when operating as any agent, you can run linting, type coverage, and localization checks using the commands below.

python ~/.claude/skills/frontend-design/scripts/ux_audit.py .
python ~/.claude/skills/frontend-design/scripts/accessibility_checker.py .
python ~/.claude/skills/api-patterns/scripts/api_validator.py .
python ~/.claude/skills/mobile-design/scripts/mobile_audit.py .
python ~/.claude/skills/database-design/scripts/schema_validator.py .
python ~/.claude/skills/vulnerability-scanner/scripts/security_scan.py .
python ~/.claude/skills/seo-fundamentals/scripts/seo_checker.py .
python ~/.claude/skills/geo-fundamentals/scripts/geo_checker.py .
python ~/.claude/skills/performance-profiling/scripts/lighthouse_audit.py <url>
python ~/.claude/skills/testing-patterns/scripts/test_runner.py .
python ~/.claude/skills/webapp-testing/scripts/playwright_runner.py <url>
python ~/.claude/skills/lint-and-validate/scripts/lint_runner.py .
python ~/.claude/skills/lint-and-validate/scripts/type_coverage.py .
python ~/.claude/skills/i18n-localization/scripts/i18n_checker.py .

An example from the file

An example of applying the clean-code component involves a developer tasked with editing a file named UserService.ts. Before making any changes, the developer checks what imports UserService.ts and discovers that UserController.ts and AuthController.ts depend on it, meaning function signatures might need updates in those dependent files as well. The developer follows the rule to edit the file and all dependent files in the same task to prevent broken imports. When writing code, the developer adheres to the rule of keeping functions small, limiting them to a maximum of twenty lines with one level of abstraction, and uses guard clauses instead of deep nesting. Variable names are chosen to reveal intent, such as userCount rather than a single letter, and booleans are phrased as questions like isActive. After completing the code changes, the test-engineer runs the test runner script using python ~/.claude/skills/testing-patterns/scripts/test_runner.py . and captures all output. The agent parses the output for errors, summarizes the findings in a specific markdown format detailing errors, warnings, and passed checks, asks the user for confirmation before proceeding, and waits for user approval before fixing any identified issues.

Strengths and weaknesses

Pros

• Enforces strict and concise coding standards across all supported development tasks
• Prevents broken imports by mandating checks on dependent files before editing
• Provides clear naming conventions and function size limits to reduce complexity
• Outlines explicit agent-to-script mappings for systematic validation

Cons

• Does not mention any changelog or historical version notes
• Lacks performance benchmarks for the provided validation scripts
• Does not include installation instructions for missing Python script dependencies
• Lacks details on handling merge conflicts during multi-file edits

Who it fits

This component is intended for software developers, AI coding assistants, and technical teams who want to maintain high code quality and consistency in their projects. It suits engineers who prefer concise, direct solutions over lengthy explanations and tutorials. Project maintainers who enforce strict architectural patterns, such as single responsibility, clean naming conventions, and flat code structures, will find the rules useful for guiding AI agents and human contributors alike. It is also well-suited for specialized roles like frontend specialists, security auditors, database architects, and test engineers who need clear mapping to specific validation scripts for audits, accessibility checks, schema validations, and security scans.

Safety notes before you install

Our recommendation is to verify that your local environment contains the required script paths before execution, as running python commands pointing to user home directories will execute whatever code resides at those paths. Ensure you run commands in the correct project folder and review all script outputs before granting confirmation to fix errors. The component instructs agents to capture script output and wait for user confirmation rather than auto-fixing, which protects your codebase from unintended modifications. Use appropriate file permissions and inspect the contents of scripts located in your user directory to ensure they align with your security requirements before invoking them.

Limits of this page

Not hands-on tested. This review page is based on the public template file as read on Oct 29, 2026. The component relies on external Python scripts whose presence and execution depend on the local file system layout under the home directory.

Source

Based on the public template file https://raw.githubusercontent.com/davila7/claude-code-templates/main/cli-tool/components/skills/development/clean-code/SKILL.md in the repository davila7/claude-code-templates, which is published under the MIT License. Adapted from the claude-code-templates library.

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