code-review

Overview of code-review skill

What the code-review skill does

The code-review skill is an AI-powered review workflow built around CodeRabbit. It helps agents inspect changed code for bugs, security issues, and quality risks, then organize findings by severity so the output is easier to act on than a generic prompt-based critique.

Who should use it

Use the code-review skill if you want a fast, structured review for a PR, a local branch, staged commits, or a specific diff range. It is especially useful when you need repeatable review behavior instead of ad hoc comments from a general-purpose model.

Why it matters before install

The real job-to-be-done is not “summarize code,” but “catch actionable issues in the exact changes that matter.” That makes code-review for Code Review a good fit when you care about review triage, severity ranking, and a workflow that can run with less manual steering.

How to Use code-review skill

Install and verify the CLI

Start with the code-review install path from the official CodeRabbit CLI source, then verify the binary before relying on it:

coderabbit --version 2>/dev/null || echo "NOT_INSTALLED"
coderabbit auth status 2>&1

If the CLI is already present, confirm it is the expected version from an official source. The --agent flag needs CodeRabbit CLI v0.4.0 or later, so older installs should be upgraded before you try agent-driven review.

Feed it the right review target

The code-review usage pattern works best when you specify the exact change surface: staged files, committed changes, a base branch, a commit range, or a review directory. A vague prompt like “review my code” is weaker than “review the last commit for security and logic bugs” because the skill can anchor to a concrete diff.

Read these files first

For the best code-review guide experience, inspect SKILL.md first, then read README.md, AGENTS.md, metadata.json, and any rules/, resources/, references/, or scripts/ folders if they exist. In this repo, SKILL.md is the key file, so you should expect most operational detail to live there.

Shape a better request

A strong prompt includes the review target, the risk focus, and the expected output style. Example: “Use the code-review skill to review my staged changes for correctness, security, and regressions; prioritize high-severity findings and include exact file/line references.” That gives the skill enough context to produce useful findings instead of broad commentary.

code-review skill FAQ

Is code-review a replacement for manual review?

No. The code-review skill is best for surfacing likely defects, security risks, and quality issues quickly, but human judgment is still needed for architecture tradeoffs, product intent, and final merge decisions.

What kinds of requests fit best?

It fits best for “review this PR,” “find bugs in these changes,” “check security issues,” and similar review-oriented tasks. It is less useful for open-ended brainstorming or for code generation tasks that do not involve an actual diff.

Do I need to be an expert to use it?

No. Beginners can use code-review as long as they can point it at the correct branch, commit, or staged changes. The main failure mode is not skill level; it is giving the skill an unclear target or omitting the risk areas you care about.

When should I skip it?

Skip code-review when you do not have a concrete code change to inspect, when you only need a high-level design opinion, or when the repository’s review process depends on specialized internal rules that are not captured in the skill context.

How to Improve code-review skill

Give more precise inputs

The fastest way to improve code-review output is to tell it what matters most: correctness, security, performance, test gaps, API compatibility, or UX regressions. If you care about one area, say so explicitly instead of asking for a broad review and hoping it guesses your priority.

Narrow the scope before asking

Better results come from smaller, well-defined diffs. Review the last commit, one feature branch, or a single subsystem first; large, mixed-purpose changes make it harder for the skill to separate important issues from noise.

Ask for actionable formatting

If you want output you can use immediately, ask for severity, file paths, and concrete fix guidance. For example: “Return only blocking and high-priority issues, with the exact location and a one-sentence remediation.” That reduces low-value commentary and makes the review easier to route.

Iterate on the first pass

If the first review is too broad, tighten the request by adding constraints such as “focus on auth, data loss, and test coverage” or “ignore style-only comments.” If the first review misses something important, rerun code-review with that risk area called out so the next pass is targeted rather than generic.