[−]enraged_camel · 2026-10-10 Sat 22:32 UTC ·
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>> Here’s a third one. In some of the code reviews I’ve encountered that AI gives a lot of feedback, it’s just providing noise.
This is completely normal if you haven't written your own custom skill with instructions on what types of issues the AI should emphasize, which ones would be considered nits and which ones aren't a problem at all.
In our repos we use a classification system: blocker, should-fix and nit. Each one has specific definitions, criteria and examples encoded in the skill file. When the time comes to review a PR, agents invoke the skill, and frankly do a stellar job. A human then reads each finding, asks the AI follow-up questions and makes the final decision in terms of whether the finding goes in the PR review.
The reason I know this works is that we have one guy on the team who does not use this skill, and blindly throws his agents at PRs. And the results are exactly as you describe.
> This is completely normal if you haven't written your own custom skill with instructions on what types of issues the AI should emphasize, which ones would be considered nits and which ones aren't a problem at all.
> This is completely normal if you haven't written your own custom skill with instructions
This line of thinking comes from the assumption that LLM are somehow infallible and can’t be wrong, which is obviously not the case.
Writing skills and other magic incantations is the first thing that comes to mind of anyone who sees the review results for the first few times. What makes you think we didn’t do that?
This is completely normal if you haven't written your own custom skill with instructions on what types of issues the AI should emphasize, which ones would be considered nits and which ones aren't a problem at all.
In our repos we use a classification system: blocker, should-fix and nit. Each one has specific definitions, criteria and examples encoded in the skill file. When the time comes to review a PR, agents invoke the skill, and frankly do a stellar job. A human then reads each finding, asks the AI follow-up questions and makes the final decision in terms of whether the finding goes in the PR review.
The reason I know this works is that we have one guy on the team who does not use this skill, and blindly throws his agents at PRs. And the results are exactly as you describe.
"You're holding it wrong" :)
It's a general purpose tool. There are different ways of applying it, with different results.
This line of thinking comes from the assumption that LLM are somehow infallible and can’t be wrong, which is obviously not the case.
Writing skills and other magic incantations is the first thing that comes to mind of anyone who sees the review results for the first few times. What makes you think we didn’t do that?