So I’ve been building an Agent Skill for Claude Code that does backend performance reviews, and something happened while I was testing it that I think is worth sharing. To sanity-check the thing, I set up an experiment: review the same real public repo twice. Once by me, reading the code carefully. And once by a completely separate agent that had zero memory of my review — I just handed it the skill and the repo and told it to go. It found something I’d completely missed. Buried in fastapi/full-stack-fastapi-template there’s this line: except InvalidTokenError, ValidationError: That’s Python 2 syntax. In Python 3 that’s a straight-up SyntaxError the module can’t even be imported. The whole app is dead on arrival. I had read that exact file. I skimmed right past it because I was reading for logic bugs, not syntax, and my brain just… pattern-matched it as fine. The other agent caught it because instead of reading the file, it ran an actual parser against every file in the repo. It didn’t “notice” the bug, it proved the bug, and I felt a little dumb. Anyway, that’s kind of the whole point of this project. I got tired of AI code review tools that do one of two things: spit out a generic checklist (“check your indexes,” “consider caching,” thanks, very helpful), or just make stuff up invented latency numbers, imaginary query plans, telling you to add Redis to an app with fourteen users. Both of those come from the same problem: nothing is actually checking whether a claim has evidence behind it. So I leaned hard into that as the design constraint. The skill is explicitly allowed — encouraged, even to come back with zero findings if that’s what the evidence supports, instead of padding the report to look thorough. Every finding gets scored on severity and confidence separately, and the priority is derived from a fixed matrix rather than picked, so you don’t get “this feels High” reasoning. And the technology knowledge is split so a Postgres review never drags in document-store logic right now 13 engines (Postgres, MongoDB, Redis, Node, Python, JVM, Go, .NET, Rust, MySQL, DynamoDB, Kafka, RabbitMQ) get full depth, everything else is honest about being shallower instead of faking it. I ran that same “review it twice, once blind” experiment on four different real repos, not toy examples. Every single time, the blind pass matched or beat my own review, usually because it found evidence I had sitting right in front of me and didn’t use properly. The FastAPI one is just the most dramatic. To be clear about what this isn’t: coverage is still narrow, I haven’t compared it against an actual human expert doing the same review, and everything tested so far is Go or Python no JVM/.NET/Rust repo has been through this blind-check yet. That’s all in the repo, I’m not hiding the gaps. If you’ve got Claude Code, it’s two commands: /plugin marketplace add Sanoy24/backend-performance-review /plugin install backend-performance-review Repo’s MIT licensed, here: https://github.com/Sanoy24/backend-performance-review Genuinely, if you run it on your own stuff and it says something dumb or wrong, tell me. That’s more useful to me than a star. submitted by /u/Alarmed_Offer_3213
Originally posted by u/Alarmed_Offer_3213 on r/ClaudeCode