Can it enforce my specific project documentation?

Yes, it specifically looks for and reads guides located in docs/guides/ to ensure code compliance with your unique project standards.

How does the skill know which files to analyze?

It identifies affected files by parsing 'Affected Components' or 'Existing Code Impact' sections within your Linear implementation tasks and checking recent commits.

Does it check unit tests or just implementation code?

By default, this skill excludes tasks labeled 'tests' to focus on the core implementation logic and architectural integrity.

What kind of performance issues can it detect?

It looks for common bottlenecks such as O(n^2) loops, N+1 database queries, and improper use of async/await or locking mechanisms.

Does this skill change my task statuses in Linear?

No, the Code Quality Checker is designed as a read-only auditor. It reports findings via comments but does not change task statuses or create new tasks.

Code Quality Checker

Name: Code Quality Checker
Author: levnikolaevich

bylevnikolaevich

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Seguridad y Pruebas

Analyzes completed implementation tasks against DRY, KISS, YAGNI, and architectural principles to ensure production-ready code.

Acerca de

This skill acts as an automated quality gate for development workflows by auditing 'Done' implementation tasks before they proceed to regression testing. It systematically checks for architectural boundary breaks, code duplication, hardcoded configurations, and performance bottlenecks while ensuring compliance with project-specific style guides. By providing structured feedback and Linear comments without altering task statuses, it enables seamless verification of code integrity and maintainability within a professional delivery pipeline.

Características Principales

Integrated Linear reporting with structured issue categories and severity levels
Performance and concurrency analysis including N+1 query and race condition checks
38 GitHub stars
Automated compliance checks for DRY, KISS, and YAGNI principles
Detection of hardcoded credentials, magic numbers, and configuration errors
Deep architectural audit to prevent boundary breaks and layer leakage

Casos de Uso

Enforcing project-specific documentation and naming conventions across a distributed codebase
Identifying technical debt and over-engineering in completed development sprints
Validating feature implementation tasks before moving to the QA or regression phase

Acerca de

Características Principales

Integrated Linear reporting with structured issue categories and severity levels
Performance and concurrency analysis including N+1 query and race condition checks
38 GitHub stars
Automated compliance checks for DRY, KISS, and YAGNI principles
Detection of hardcoded credentials, magic numbers, and configuration errors
Deep architectural audit to prevent boundary breaks and layer leakage

Casos de Uso

Enforcing project-specific documentation and naming conventions across a distributed codebase
Identifying technical debt and over-engineering in completed development sprints
Validating feature implementation tasks before moving to the QA or regression phase