code-reviewer_skill

This skill performs a structured, multi-pass code review to assess correctness, security, maintainability, and performance, guiding fixes and quality
  • Shell

2

GitHub Stars

1

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 masanao-ohba/claude-manifests --skill code-reviewer

  • SKILL.md5.0 KB

Overview

This skill provides a technology-agnostic, systematic code review methodology for assessing correctness, security, maintainability, and performance. It defines multi-pass review steps, severity classifications, checklists, feedback patterns, and a quality scoring model to produce actionable, prioritized review results.

How this skill works

Reviews follow a multi-pass process: correctness, security, maintainability, and performance. Each pass applies focused checks and checklists, classifies findings by severity, and produces constructive feedback using a what/why/how/severity template. A quality score is computed by weighting categories and deducting points for issues to summarize overall health.

When to use it

  • During pull request reviews to standardize assessment across teams
  • As a pre-merge gate to block critical or major issues
  • For self-review by developers before submitting changes
  • When auditing code for security and data-protection risks
  • To generate objective quality reports for deliverables

Best practices

  • Define project-specific natural-language constraints up front and load them into review configuration
  • Run the multi-pass review in sequence and record findings per pass to avoid missed coverage
  • Classify each finding with the provided severity levels and take the prescribed action
  • Use the constructive feedback template: describe what, explain why, propose how, and set severity
  • Acknowledge good patterns and keep tone focused on the code, not the author

Example use cases

  • Block a merge when a security vulnerability or data-loss risk is detected (critical)
  • Request changes for missing error handling or a major logic bug (major)
  • Suggest stylistic or documentation improvements that can be merged (minor)
  • Evaluate a release candidate by computing an overall quality score to decide readiness
  • Automate checklist checks for input validation, auth, and test coverage during CI

FAQ

Findings are prioritized using severity levels: critical blocks merge, major requests changes, minor suggests improvements, and info is commentary.

How is the overall quality score calculated?

Categories (correctness, security, maintainability, performance) have weights totaling 100. Deductions apply per issue type (critical, major, minor, info) to produce a final score and threshold-based assessment.

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