Point it at a pull request and get back an advisory, severity-tagged code review report that helps the author ship a change that improves the overall health of the codebase. ## What it does - **Severity-tagged findings:** issues ranked by impact so reviewers focus on what matters - **Advisory reports:** recommendations the author acts on, not automated rewrites - **Human- and AI-authored code:** works on PRs from both, with awareness of common AI-generated patterns - **Code health framing:** every finding tied to overall system quality, not just the changed lines ## Where it fits - **An engineer reviewing a PR:** "Review this change for quality and correctness." A prioritized report to work through. - **A team gating AI-generated code:** "Flag the issues in this Copilot-generated PR." Severity-ranked findings before merge. - **A lead coaching a junior:** "Give feedback on this PR for the author to learn from." An advisory report that teaches. ## How it works 1. **Point it at a PR:** the diff, the code, or the repository. 2. **It runs the review:** correctness, code health, and advisory-style findings. 3. **You get a severity-tagged report back:** ranked findings for the author to review and address. Built for engineering teams reviewing PRs. Every finding is advisory — the author owns the changes.
53 ratings · showing the 12 most relevant
James K.
Used this to review a messy AI-generated refactor of our auth module. Caught a subtle race condition and flagged missing test coverage that I would've missed. The severity tagging helped me prioritize what to fix before merge.
Priya S.
Finally have a reliable way to review pull requests from junior devs without spending 45 minutes on each one. It flags real issues — design problems, missing edge cases — and ignores style nits our linter already handles.
Marco T.
Solid code reviewer. Caught a potential SQL injection vulnerability in our data layer that I'd overlooked. Occasionally flags things that are already handled by our linter, but the 🔴/🟡 severity system makes it easy to filter signal from noise.
Chen W.
We've been using this to review auto-generated migration code and it's been a game-changer. Flags schema compatibility issues and test gaps that our team was missing. Definitely recommend for high-risk changes.
Amara O.
Decent tool, but sometimes it flags nits on already-linted code. The review is thorough though—caught a few concurrency bugs I'd have shipped. Wish it understood our team's specific code conventions better without manual setup.
David L.
Using it to review Python changes in our data pipeline. The agent spots missing error handling and suggests better test coverage without being pedantic about formatting. Much faster than waiting for a human senior engineer.
Yuki N.
Good for catching design issues in new API endpoints. It references line numbers clearly and the summary tally helps me understand what actually matters. Sometimes over-flags on pre-existing issues in nearby code.
Hassan M.
Reviewed a 600-line refactor of our payment service with this. Caught three Important issues—a missing null check, inconsistent error handling, and a security concern around token scope. Saved us from a bad deploy.
Elena R.
Works well for PR reviews when we're short-staffed. The inline comments are useful, though sometimes it lacks context about our legacy code patterns. Overall saves time and improves quality.
Raj P.
Found it a bit noisy for our TypeScript codebase—lots of nits that ESLint already catches. When it does flag something Important, it's usually right, but I have to filter heavily. Would benefit from better linter integration.
Sophie B.
Perfect for reviewing changes from contractors and new hires. Consistent, non-judgmental, and catches functional issues I'd have to hunt for manually. Especially good at spotting missing tests and edge cases.
Vikram G.
Helpful for frontend code reviews—flagged accessibility and performance issues we often miss. The severity tagging is spot-on. Just takes a bit of tuning to match our house style for it to feel less opinionated.