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Reviewing code

Skill oryanmoshe/agent-skills/skills/reviewing-code

Reviews code changes for bugs, performance issues, security problems, and best practice violations. Use when reviewing PRs, before committing, after making code changes, or when user asks to review, check, or look over code. Catches N+1 queries, missing error handling, React hooks issues, test coverage gaps, and security vulnerabilities.From its SKILL.md

Install
npx -y skills add oryanmoshe/agent-skills --skill reviewing-code

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.3 KB, 935 tokens by cl100k_base, as published. Nobody here has run it

Reviewing Code

Overview

Review code changes systematically using priority-based rules. Focus on what automated linters miss — performance, architecture, test coverage, and security patterns.

Review Workflow

  1. Identify changed files — focus review on actual changes, not entire codebase
  2. Check P0 rules first — blocking issues stop the review
  3. Check P1 rules — important issues that need discussion
  4. Optionally check P2 — only if user wants a thorough review
  5. Provide fixes — include code suggestions for each issue

Priority Levels

  • P0: Blocking — must fix before merge
  • P1: Important — should fix, discuss if not
  • P2: Nice-to-have — suggest but don't block

What to Check

P0 — Blocking Issues

CategoryWhat to look for
N+1 queriesDatabase/API call inside a loop; resolver without batching
SecurityMissing auth check on endpoint; fail-open permission pattern; SQL injection; XSS
Data lossMissing error handling on write operations; no transaction for multi-step mutations
Memory leaksUncleaned subscriptions, timers, or event listeners in effects

P1 — Important Issues

CategoryWhat to look for
PerformanceO(n²) when O(n) is possible; expensive computation in render path; missing memoization for derived data; large bundle imports
Error handlingMissing try/catch on async operations; swallowed errors; missing error boundaries
Test coverageNew business logic without tests; untested error paths; missing edge cases
Null safetyAccessing nested properties without null checks; missing optional chaining
React hooksMissing dependencies in useEffect; missing cleanup functions; hooks inside conditions

P2 — Nice to Have

CategoryWhat to look for
NamingUnclear variable/function names; inconsistent naming patterns
Code organizationLogic in wrong layer (UI doing business logic); duplicated code
TypesMissing type annotations on public APIs; any types
Clean codeMagic numbers; deeply nested conditionals; functions doing too many things

Human Blind Spots — Prioritize These

These are rarely caught by human reviewers. The skill must catch them:

  1. Performance — O(n) vs O(1) lookups, unnecessary re-renders, bundle size impact
  2. Test coverage — missing tests for new logic, untested error paths
  3. Memory leaks — uncleaned subscriptions, timers, event listeners

Explanation Style

DO: Provide full verbal explanations

"This calls the user service inside a loop. With 50 users, that's
51 database calls instead of 2. Batch the IDs and make a single
query with a WHERE IN clause."

DON'T: Leave terse comments without context

"N+1 query"

Output Format

For each issue:

### [P0] Short Description

**File:** path/to/file.ts:42

**Issue:** [Full explanation of the problem and its impact]

**Fix:**
// Before
[problematic code]

// After
[fixed code]

Inline Code Markers

When reviewing code in-place, use these markers:

// FIXME(review): [P0] N+1 query — will cause performance degradation at scale
// TODO(review): [P1] Add error handling for network failure case
// NIT(review): Consider renaming for clarity

After Review

Present the summary with issue counts by severity, then ask the user what to do next:

  • Post comments to GitHub PR
  • Create tasks to track fixes
  • Just show the summary

Red Flags — STOP and Review

ThoughtReality
"This is too simple to review"Simple changes break things. Review.
"Tests pass so it's fine"Tests can't catch what they don't cover. Review.
"It's just a refactor"Refactors introduce subtle bugs. Review.
"The linter will catch it"Linters miss logic, performance, and architecture. Review.

What ships with it

Read from the repository

Just SKILL.md. No reference files, no scripts.

Gives 0 of the 12 instructions most test skills give in 935 tokens

Counted across 1,201 of the 2,096 authors here whose files we hold, read 2026-09-06

  • Write a failing test before writing codein 43 of 1201, across 36 files
  • Run the full test suitein 36 of 1201, across 35 files
  • Test only one variable per experimentin 34 of 1201, across 17 files
  • Read product marketing context before asking questionsin 34 of 1201, across 14 files
  • Mock external dependenciesin 34 of 1201, across 30 files
  • Define primary, secondary, and guardrail metricsin 33 of 1201, across 16 files
  • Pre-determine sample size before startingin 31 of 1201, across 14 files
  • Test behavior rather than implementationin 31 of 1201, across 29 files
  • Formulate a hypothesis before designing a testin 30 of 1201, across 13 files
  • Document every test hypothesis, variant, and resultin 29 of 1201, across 11 files
  • Use descriptive test function namesin 25 of 1201, across 21 files
  • Commit to the methodology without stopping earlyin 24 of 1201, across 8 files

Said here and by no other author read

  • identify changed files before reviewing
  • check P0 blocking issues first
  • check P1 important issues next
  • provide code suggestions for each issue
  • provide full verbal explanations for issues
  • use specific markers for inline code comments

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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