pacificstudio/openase/review-code

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Review behavior, risk, performance, and test coverage before style nits.

Install

npx vskill@latest install pacificstudio/openase/review-code

SKILL.md

Review Code

Overview

Conduct a thorough code review for quality, security, performance, and maintainability. Focus on the real risks first, then provide concrete, severity-rated feedback with file and line references where possible.

When To Use

  • Before merging a pull request.
  • After implementing a feature or refactor.
  • When the user asks for a review of changed files or a branch.
  • When the code looks correct at first glance but may still hide regressions or design debt.

Review Workflow

  1. Identify the review scope.
  • Review the current diff, named files, or the whole change set.
  • Prefer reviewing the actual changed surface before expanding further.
  1. Check behavioral correctness first.
  • Regressions against existing behavior.
  • Missing edge-case handling.
  • Broken invariants or data consistency risks.
  1. Review the main quality categories.
  • Security risks.
  • Code quality and complexity.
  • Performance concerns.
  • Error handling and observability.
  • Maintainability, coupling, and testability.
  1. Evaluate the test story.
  • Are the important paths covered?
  • Are the tests proving behavior rather than implementation trivia?
  • Is there an obvious missing regression test for the change?
  1. Rate findings by severity.
  • CRITICAL: must fix before merge; security or correctness issue with serious impact.
  • HIGH: should fix before merge; likely bug, major regression risk, or serious design flaw.
  • MEDIUM: worthwhile fix; non-blocking but meaningful quality issue.
  • LOW: suggestion, cleanup, or style improvement.
  1. Report findings in priority order.
  • File and line reference.
  • What is wrong.
  • Why it matters.
  • Concrete fix guidance.

Review Categories

  • Security: secrets, unsafe input handling, authz gaps, injection, XSS, CSRF.
  • Code quality: complexity, duplication, large functions, fragile branching.
  • Performance: avoidable repeated work, inefficient algorithms, N+1 patterns, unnecessary re-renders.
  • Maintainability: unclear naming, tight coupling, hidden assumptions, hard-to-test logic.
  • Reliability: missing error handling, weak retries, silent failures, incomplete logging.

Operating Rules

  • Lead with findings, not praise.
  • Prioritize behavior and risk before style nits.
  • Prefer evidence and concrete examples over general taste.
  • If there are no findings, say so explicitly and still mention residual risks or test gaps.
  • Keep recommendations actionable enough that another engineer can implement them directly.

Default Deliverable Shape

Return these sections:

  1. Findings - ordered by severity, each with file and line references.
  2. Open Questions / Assumptions - only if they affect confidence.
  3. Residual Risks - what was not fully proven by the review.
  4. Short Summary - brief overall assessment after the findings.

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
261
Repository forks
25
7-day trend score
28

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

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