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To stop a coding agent from flagging style nits, give it an explicit threshold: report only specific, actionable issues introduced by the change that meaningfully affect correctness, security, performance, or maintainability. Tell it to ignore non-blocking formatting, spelling, documentation, and naming preferences—except when they obscure meaning or violate a documented project standard. Put durable expectations in repository instructions, then validate the agent’s findings with human review.
What should count as a code-review finding?
A useful review comment points to a discrete issue, explains when it occurs, and describes its practical impact. “This may return an incorrect result when the input is empty” is actionable; “I prefer a different variable name” usually is not. Ask the agent to ground each finding in the change or repository context rather than report speculative concerns.
Style is not automatically irrelevant. A convention can matter when a deviation makes code hard to understand or breaks a documented project standard. State that exception so the agent does not mistake “ignore style nits” for “ignore every convention.” The OpenAI Codex review rubric says to ignore non-blocking style, formatting, typo, documentation, and other nit issues, while recognizing that more specific instructions can override general ones: Codex code-review rubric.
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Start with a concise rule that defines scope, impact, exceptions, and the evidence expected in each comment:
#1 Best Overall
Review only for discrete, actionable issues introduced by this change that meaningfully affect correctness, security, performance, or maintainability. Ignore non-blocking style, formatting, spelling, documentation, and naming preferences unless they obscure meaning or violate a documented project standard. For each finding, explain the triggering condition and impact, and keep the comment focused on one issue. Do not report speculative concerns without evidence in the diff or repository context.
Adapt the issue categories to your project. For example, a team may want a particular compatibility or reliability concern called out explicitly. Avoid vague commands such as “be less picky”: they do not tell the agent what qualifies as important.
Rank #2
Put persistent rules where the agent can find them
Keep recurring expectations in instruction files instead of relying on reviewers to repeat them in every request. Repository-wide instructions can describe shared review standards; path-specific instructions can explain conventions that apply only to a subsystem or file type.
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GitHub Copilot
GitHub documents .github/copilot-instructions.md for repository-wide guidance and .github/instructions/**/*.instructions.md for path-specific guidance. Its documentation says repository-wide instructions also apply to code review. GitHub also lists AGENTS.md among supported custom instruction sources for Copilot cloud agent. See GitHub’s repository custom-instructions documentation and its task guidance for repository instructions.
Codex and other agents
Codex’s review rubric supports the principle of following more specific instructions over general ones. Instruction-file names, precedence, and support differ among agents, however. Check the current official documentation for the particular agent before assuming that a file recognized by one product will affect another.
Match review depth to the change’s risk
Where the agent offers review-effort settings, choose based on the change rather than applying the deepest review to every diff. GitHub describes Copilot’s Lite option as cost-efficient and focused on glaring issues, and Balanced as deeper analysis suited to complex logic, security-sensitive changes, or work spanning services. These are Copilot-specific descriptions, not universal modes for coding agents; consult GitHub’s Copilot code-review guidance for the available settings.
A small, low-risk change may call for a targeted pass. A change involving sensitive logic or several interacting components warrants closer scrutiny and careful human validation. Review-effort settings do not replace clear instructions about what makes a finding actionable.
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An agent’s review is assistance, not proof that a change is safe. GitHub warns that Copilot may miss problems or make mistakes, and recommends validating its feedback carefully and supplementing it with human review. Keep the project’s normal engineering review and required checks in place; investigate each comment against the code and the conditions it describes. The warning and recommendation appear in GitHub’s code-review documentation.
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