Week 45, updated 2 Nov 2026

AI use cases Coding agents AI code review

AI code review

AI code review means asking a coding agent to read a diff before a human does. Below: the tools we track for it, compared on public data, plus prompts and a workflow to copy. Treat it as a first pass, not a replacement for a human reviewer.

Tools compared
4
Open source
3of 4
Reviewed
23 Oct

For: Developers

Best-scored tools for this jobscore, safety
  1. 1Claude CodeTerminal coding agent; its repo ships code-review and pr-review-toolkit plugins65No dependencies scanned
  2. 2ClineCoding agent as VS Code extension, JetBrains plugin, CLI or desktop app65Caution
  3. 3GitHub MCP ServerGives an agent GitHub access, including a pull_requests toolset41No issues found
  4. 4codex-plugin-ccRuns a read-only Codex review from inside Claude Code (/codex:review)29No issues found
What the score and shapes mean

Score = public maintenance, adoption and docs signals, as a percentile of 52 tools. Nobody here tested these hands-on. Shapes are the separate safety label: No issues found, No dependencies scanned, Caution.

What it does for you

Developers and small teams who want a quick pre-review pass, and reviewers who want a summary of a large change.

  • Review a local diff before you open a pull request
  • Flag likely bugs, unhandled errors and missing tests
  • Check a change against your own written conventions
  • Get a second opinion from a different agent on the same change
  • Summarise a large pull request so a human can review it faster
More about this job

AI review does not replace a human reviewer, but it is a fast first pass. It can read a diff, point at likely bugs, missing edge cases and unclear naming, and delegate a deeper check to another agent.

In short

Computed from our data of 2 Nov 2026. Scores rank public GitHub signals and are not a hands-on test.

Workflow in 5 steps

  1. Make the diff small enough to review.
  2. Ask for a review with an explicit checklist.
  3. Verify each finding yourself.
  4. Optionally ask a second agent or model to review the same diff and compare the findings.
  5. Fix, re-run the tests and request a human review as usual.
Full steps
  1. Make the diff small enough to review; split unrelated changes.
  2. Ask for a review with an explicit checklist (bugs, tests, security, performance).
  3. Verify each finding yourself; discard those you cannot reproduce or explain.
  4. Optionally ask a second agent or model to review the same diff and compare the findings.
  5. Fix, re-run the tests and request a human review as usual.

Prompts to copy

Starting points we wrote, not tested results.

Review the changes on this branch against main. List possible bugs first, then missing tests, then style nits. Quote the lines you mean.

2 more prompts

Look only at error handling in this diff. Which failure paths are not handled or not logged?

Summarise this pull request in five bullets for a reviewer who has not seen the code, and list the three riskiest changes.

Watch out for

  • AI output can be fluent and wrong.
  • Treat AI changes like a pull request from a new colleague: read the diff, run the tests and keep a human sign-off before anything ships.
  • Prompts sent to a hosted model leave your machine.
  • Models over-report style nits and can miss design problems.
  • Licence to check before commercial use: Claude Code (Proprietary (Anthropic Commercial Terms)).
  • No dependencies scannedClaude CodeCode checks clean, no lockfile to check
  • CautionCline13 critical-severity known vulnerabilities in runtime dependencies (OSV)
  • No issues foundGitHub MCP ServerNo issues found in this week’s scan
  • No issues foundcodex-plugin-ccNo issues found in this week’s scan
Why these warnings?
  • AI output can be fluent and wrong. Check facts, code and citations yourself before you rely on them or publish them.
  • Treat AI changes like a pull request from a new colleague: read the diff, run the tests and keep a human sign-off before anything ships.
  • Prompts sent to a hosted model leave your machine. For confidential material use a local model or check the provider's data terms first.
  • Models over-report style nits and can miss design problems. Do not use "AI approved" as a merge criterion.
  • Claude Code: code and instruction-file checks found nothing, but there is no runtime lockfile or manifest, so dependencies were not checked
  • Cline: 13 critical-severity known vulnerabilities in runtime dependencies (OSV)

How the tools compare

Open the comparison table

All tools we track for this use case, highest score first. Releases count entries in the public GitHub releases feed over 90 days; issue close time is the median over the last closed issues (shown when there are at least 5, the same minimum the score uses).

AI code review: tools compared, data from 2 Nov 2026
ToolRole hereScoreSafetyLicenceLast pushReleases, 90 daysMedian issue closeStars
Claude Code AI toolTerminal coding agent; its repo ships code-review and pr-review-toolkit plugins65 #11 No dependencies scannedProprietary (Anthropic Commercial Terms)10+under 1 hour154,199
Cline AI toolCoding agent as VS Code extension, JetBrains plugin, CLI or desktop app65 #12 CautionApache-2.010+–71,190
GitHub MCP Server MCP serverGives an agent GitHub access, including a pull_requests toolset41 #40 No issues foundMIT10+4 days34,149
codex-plugin-cc PluginRuns a read-only Codex review from inside Claude Code (/codex:review)29 #48 No issues foundApache-2.0019 days34,797

Recommended tools

From our ranked data, highest score first. A high score does not mean a clean scan.

Questions

Which tool scores highest for AI code review?

Claude Code, with 65/100, rank 11 of 52 tracked tools (data of 2 Nov 2026). The score is a relative percentile on maintenance, adoption and docs, not a hands-on test, and it is separate from the safety label.

Which of these tools are open source?

Permissive open-source licence: Cline (Apache-2.0), GitHub MCP Server (MIT) and codex-plugin-cc (Apache-2.0). Check the licence before commercial use: Claude Code (Proprietary (Anthropic Commercial Terms)).

How do I get a Codex review inside Claude Code?

Install OpenAI's codex-plugin-cc and run /codex:review for a read-only review of your uncommitted changes, or /codex:review --base main to review your branch against main. /codex:adversarial-review accepts extra focus text to challenge design choices. The plugin needs a ChatGPT subscription (Free included) or an OpenAI API key, and Node.js 18.18 or later. Source: openai/codex-plugin-cc

Does Claude Code come with a pull request review command?

The claude-code repository ships a code-review plugin: /code-review runs an automated PR review with several specialised agents and confidence-based scoring to filter false positives. The same repository has a pr-review-toolkit plugin (/pr-review-toolkit:review-pr) with agents for comments, tests, error handling and type design. Source: anthropics/claude-code/blob/main/plugins/README.md

Can an AI agent read and comment on GitHub pull requests?

With GitHub's official github-mcp-server, yes: it has a pull_requests toolset and an add_issue_comment tool that also works on pull requests. You can limit the server to the toolsets you need, for example --toolsets repos,issues,pull_requests. Source: github/github-mcp-server

Where can I run Cline?

Cline runs as a VS Code extension, a JetBrains plugin, a CLI (npm i -g cline) and a desktop app for macOS and Windows. It is also available as an SDK. Source: cline/cline

Related jobs

Sources for tool facts

anthropics/claude-code · cline/cline · github/github-mcp-server · openai/codex-plugin-cc. Data fetched 2 Nov 2026. Scores and labels are automated, relative and not a hands-on test.

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