What types of issues can this skill detect?

It is designed to categorize issues into specific domains including Security (SEC), Architecture (ARC), Testing (TST), UX (UXD), Readability (RDY), Documentation (DOC), and Performance (PRF).

Does this skill provide actual code fixes?

Yes, a core requirement of the methodology is that every finding must include a concrete 'Suggestion' field providing a specific fix rather than vague advice.

How does the confidence scoring work?

Findings are scored on a 0-100 scale based on evidence strength and reproducibility. To ensure high quality, only findings with a confidence score of 80 or higher are reported.

What are the severity levels used in the review?

The skill uses four levels: P1 for critical issues (security/crashes), P2 for important feature breaks, P3 for minor bugs, and P4 for trivial or cosmetic changes.

Structured Code Reviewer

Name: Structured Code Reviewer
Author: tenzir

bytenzir

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安全与测试

Performs code reviews using a rigorous methodology based on confidence scoring and severity ratings.

关于

The Reviewing Changes skill equips Claude with a professional-grade methodology for evaluating code changes. It utilizes a dual-dimension rating system that separates the severity of an impact from the confidence in the finding, ensuring that only high-certainty, high-impact issues are reported. By following a standardized output format that includes specific evidence, reproducibility criteria, and actionable suggestions, this skill transforms code reviews into objective, data-driven assessments that help teams prioritize security, architecture, and performance fixes.

主要功能

2 GitHub stars
Evidence-based scoring criteria that factors in reproducibility, impact, and rule violations
Mandatory actionability requirements ensuring every finding includes a concrete fix
Dual-dimension rating system combining severity (P1-P4) with confidence scoring (0-100%)
Standardized issue identification using specific prefixes for security, architecture, performance, and more
Automated noise reduction by filtering out findings with less than 80% confidence

使用场景

Conducting deep security audits and architectural reviews on new pull requests
Automating the identification of performance bottlenecks and readability issues with concrete evidence
Standardizing team review outputs to ensure consistent quality and actionability

关于

主要功能

2 GitHub stars
Evidence-based scoring criteria that factors in reproducibility, impact, and rule violations
Mandatory actionability requirements ensuring every finding includes a concrete fix
Dual-dimension rating system combining severity (P1-P4) with confidence scoring (0-100%)
Standardized issue identification using specific prefixes for security, architecture, performance, and more
Automated noise reduction by filtering out findings with less than 80% confidence

使用场景

Conducting deep security audits and architectural reviews on new pull requests
Automating the identification of performance bottlenecks and readability issues with concrete evidence
Standardizing team review outputs to ensure consistent quality and actionability