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Code review

Skill nimadorostkar/Claude-Skills-collection/skills/development/code-review

A curated library of 137 production-grade skills for Claude and other AI coding agents.

Install
npx -y skills add nimadorostkar/Claude-Skills-collection --skill code-review

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

2 things to look at

  • 22 days oldThe repository was created 22 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 23 stars23 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.

What its author says it does

Copied from the file, not written here

Use when reviewing a pull request, diff, or branch. Produces severity-ranked findings covering correctness, security, performance, and maintainability, with concrete fixes rather than opinions.

SKILL.md

4.0 KB, as published. Nobody here has run it

Code Review

Purpose

Review changes the way a senior engineer does: correctness first, then security, then performance, then design, then style. Every finding carries a severity, a reason, and a fix.

When to Use

  • Reviewing a pull request or a local diff before pushing.
  • Auditing a branch before merge to a protected branch.
  • Reviewing someone else's code in an unfamiliar area of the codebase.

Capabilities

  • Correctness analysis: edge cases, off-by-one, null and error paths, concurrency.
  • Security review: injection, authorization gaps, secret exposure, unsafe deserialization.
  • Performance review: N+1 queries, unbounded allocations, blocking calls on hot paths.
  • Design review: coupling, layering violations, missing abstractions, premature ones.
  • Test review: whether the tests would actually fail if the code were wrong.

Inputs

  • A diff, PR number, or branch reference.
  • The surrounding code, not just the changed lines.
  • The intent of the change: linked issue, PR description, or a one-line summary.

Outputs

A review with findings grouped by severity:

  • Blocking — Correctness, security, or data-loss defects. Must be fixed before merge.
  • Should fix — Design or performance problems that will cost more later than now.
  • Consider — Improvements that are genuinely optional.

Each finding states the file and line, what is wrong, why it matters, and a suggested fix.

Workflow

  1. Understand the intent — Read the PR description and the issue. A change that does something other than what it claims is the first finding.
  2. Read the diff in context — Open the surrounding functions. Most defects live in the interaction between changed and unchanged code.
  3. Trace the failure paths — For each new branch: what happens when the input is empty, null, malformed, or hostile? What happens when the network call fails?
  4. Check the boundaries — Anything crossing a trust boundary (HTTP, queue, file, DB) must be validated on the way in and encoded on the way out.
  5. Assess the tests — Would the new tests fail if the implementation were subtly wrong? If not, they are coverage, not verification.
  6. Rank and report — Sort findings by severity. Lead with the ones that block.

Best Practices

  • Review the code, not the author. Write "this returns before closing the file", not "you forgot to close the file".
  • Do not report style issues that a formatter should own. Fix the formatter config instead.
  • One blocking finding stated clearly beats twenty nitpicks that bury it.
  • If the design is wrong, say so early and stop line-by-line reviewing. Detailed feedback on code that should be deleted wastes everyone's time.
  • Approve when the change is better than what exists. Perfection is not the bar; regression is.
  • Quote the exact line. A finding without a location is not actionable.

Examples

A well-formed finding:

Blocking — api/orders.py:88 total is computed from request.json["items"] without validating quantities, so a negative quantity produces a negative total and a credit to the customer's card. Validate quantity >= 1 in the request schema, and assert total > 0 before charging.

A finding that is not actionable:

"The order logic feels a bit fragile."

Notes

  • Automated tools should catch formatting, unused imports, and obvious lint violations before a human reads the diff. If they are reaching review, fix the pipeline, not the PR.
  • Large PRs receive worse reviews. If a diff exceeds roughly 400 lines, reviewing it as one unit is unreliable — ask for it to be split.
  • A review that finds nothing is a valid outcome, but say what you checked.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.