Our team just migrated our CI/CD to GitHub Actions last month, and I was tasked with improving our review process. I set up a simple workflow using Claude's API that comments on every pull request.
It checks for common security issues, suggests performance improvements, and even flags potential cost impacts for our cloud services. It's not perfect, but it catches a lot of the routine stuff. We're saving about 2 hours of manual review time per PR now.
I'm curious if others have tried similar integrations. What are you having Claude look for in your code reviews? Any pitfalls I should watch out for, especially around false positives or missing context?
learning every day
Great timing on this post. We've been running a similar setup with our Salesforce dev team, but using the Workato connector instead of direct API calls.
One pitfall we hit early was Claude flagging "cost impacts" for Apex triggers without understanding our governor limits. We ended up training it on our specific org's limits document, which cut false positives by about 40%. You might want to feed it your cloud provider's pricing docs for your exact tier.
What's your fallback for when it misses context? We have a simple rule that if Claude adds a comment but the human reviewer overrides it, that thread gets logged to a Slack channel for later review. Helps us spot patterns in what it's not getting.
That Slack log for overrides is smart. We do something similar but route those to a low-priority ticket in Jira. Lets us batch-train Claude later without interrupting the team.
The > training it on our specific org's limits document is key. Generic docs are useless. We had to do the same with AWS service quotas and our RI/SP commitments before the cost flags became reliable.
How's the lag with Workato? That was the main reason we went direct API, even though the connector looked easier.
Ask me about hidden egress costs.
Your point about flagging cloud cost impacts is a critical one. I've found those suggestions to be wildly inaccurate without a highly specific context window. We've had to embed our actual pricing agreements and reserved instance inventory before it could produce anything beyond generic alarmism.
Beyond security and cost, we've had good results directing it to analyze specific performance anti-patterns. For us, that includes N+1 query detection in ORM code, inefficient index usage, and unbounded result sets in pagination logic. The false positive rate is lower on those because they're more deterministic.
Are you tracking the precision and recall of the comments? We found that saving two hours per PR was an initial win, but the real gains came from classifying its error types and retraining. A simple confusion matrix logged per comment category (security, performance, cost) helped us tune the prompts and decide when to actually block a PR versus just suggest.
Show me the numbers, not the roadmap.