Trevyn

Trevyn Documentation

Product guides and technical setup

Documentation

Using Trevyn AI

Interact with Trevyn AI directly from your GitHub Pull Requests using comment commands. Trigger new reviews, apply suggestions automatically, and teach Trevyn new rules.

Automated Reviews

By default, Trevyn AI automatically reviews your code whenever a Pull Request is opened or when new commits are pushed (synchronized). As long as the repository is activated in your dashboard, no manual action is required to get the initial review.

Triggering On-Demand Reviews

If you want Trevyn to perform a fresh review of an open Pull Request outside of the automated flow, you can invoke it manually. Simply leave a comment on the PR with one of the following commands:

  • @trevynai review
  • @trevynai re-review
  • @trevynai rereview
  • @trevynai analyze

Once triggered, Trevyn will acknowledge the request and post a new comprehensive review.

Requesting a Summary

If you only want a high-level summary of the PR rather than a line-by-line review, use the summary command:

  • @trevynai summary

This will provide a concise overview of the changes without generating individual review comments on specific lines.

Applying Code Suggestions

When Trevyn AI identifies potential improvements, it provides actionable code suggestions. You can command Trevyn to automatically commit these suggestions directly to your branch.

Apply a single suggestion

Reply to Trevyn's review comment to apply a specific suggestion by its index number:

@trevynai apply 1

You can also provide a custom commit message by using the | separator:

@trevynai apply 1 | refactor: optimize sorting logic

Apply all suggestions

To accept and commit all suggestions provided in the review at once, use:

@trevynai apply-all

Teaching Trevyn via Replies

Trevyn gets smarter through conversation. If you disagree with a suggestion or want to clarify a project-specific convention, simply reply to Trevyn's review comment with your feedback.

Trevyn automatically analyzes your reply. If the feedback is educational or establishes a pattern (e.g., "We prefer using TanStack Query for all data fetching instead of useEffect"), Trevyn will record it as a Learning. This learning is stored in your team's knowledge base and will influence how Trevyn evaluates future Pull Requests, ensuring it aligns more closely with your team's unique standards over time.