Using Cursor’s Bugbot to Spot Issues Early in Pull Requests

Most engineers know the familiar rhythm of a PR review: glance at the diff, hunt for logic mistakes, debate naming, rinse, repeat. It’s essential work, but it’s also incredibly easy to miss the small but critical bugs — off-by-one errors, edge case logic, subtle null handling problems — especially when PRs are large or rushed.

For teams like ours running a tight CI/CD pipeline (we use GitHub Actions), efficiency is everything. Every minute spent waiting for a human to spot a simple fix is a minute the code isn’t deployed and delivering value.

As we are already Cursor we naturally started using Bugbot from Cursor more and more as a first-pass reviewer on our pull requests: initially more out of curiosity as we had tried CodeRabbit in the past which we did not like. Bugbot is not replacing human reviewers, but it changes when and where we catch issues.

At a high level, Bugbot:

• Runs automatically on new PRs and on updates, analyzing just the diff with context about intent and code relationships. It can also be triggered manually with commands like @cursor review or bugbot run.
• Comments directly on GitHub where it finds potential problems, complete with suggested fix ideas.
• Supports custom rules via .cursor/BUGBOT.md so you can encode team-specific guardrails or conventions.

Benefits of using Bugbot in our development process

Bugbot sits right at the start of that process, essentially becoming the fastest, least-tiring ‘first reviewer.’

• Before CI: Bugbot scans the code on the PR.
• During CI: Our existing tests (like those run with Angular Testing Library and Testcontainers) catch functional regressions.
• After CI: Human reviewers focus on architectural decisions and complex logic.

We’ve noticed a few concrete benefits from incorporating Bugbot into our flow:

• Fewer “oh my bad” bugs late in review. When Bugbot flags a logic issue early, the human reviewer can focus on architecture and design trade-offs, not basic correctness.
• Better alignment on standards. Having auto-comments up front nudges authors toward patterns we care about before the first human comment lands.
• Faster iteration. If a suggestion can be fixed quickly (even automated via Cursor links), the turnaround on a clean, merge-ready PR improves.

And if something specific keeps cropping up in your project (think domain-specific invariants or architectural patterns), we can add it as a Bugbot rule and let the bot enforce it consistently. That means fewer repetitive comments from reviewers and more time spent on what actually matters. A recent example: leave a comment on the PR to remind the engineer to bump the manifest version of our browser extension if the change should trigger a browser new extension release.

As with any AI assistant, there are edge cases. Bugbot doesn’t always catch every problem, and it may not understand deep business logic the way a domain expert reviewer does. It does not eliminate the need for a code review performed by an engineer. But using it as an early filter — catching obvious bugs and surfacing fix proposals — has already saved us plenty of time.

Conclusion

If your team is serious about minimizing noise and catching bugs earlier in the cycle, giving Bugbot rules a real place in your development workflow is worth exploring. Let me know about your experiences.