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Acceptance Criteria Review

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Make criteria testable and complete.

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Review acceptance criteria. User story: [USER_STORY]. Current acceptance criteria: [CURRENT_CRITERIA]. Product context: [CONTEXT].

Check each criterion: is it testable (a clear pass or fail)? unambiguous? does it describe behavior not implementation? Rewrite weak criteria into Given/When/Then. Before "The page loads quickly." After "Given a user on a standard connection, when they open the dashboard, then the main content appears within 2 seconds in 95 percent of test runs."

Then find gaps: error cases, empty states, permissions, concurrency, input validation, accessibility (keyboard use, screen reader labels, contrast), localization, data limits, and undo. Return a table: Area | Missing criterion | Suggested wording | Priority. Add a short list of questions for the product owner, and a test checklist for QA. Do not invent product behavior; mark assumptions [VERIFY].

Return each criterion with a verdict (testable, needs rewrite, missing detail) and the rewritten version, then list the five test cases a tester would write first, so I can see whether the criteria are sufficient.

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Replace each bracketed placeholder with your own details, then paste into Any AI or another assistant.

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Story: As a manager, I want to approve leave requests from my phone so that I can respond quickly.
Weak: "The page loads quickly." Rewritten: Given a user on a standard connection, when they open the approvals page, then the list appears within 2 seconds in 95 percent of test runs.
Weak: "Works on mobile." Rewritten: Given a 375-pixel-wide screen, when the manager taps Approve, then the request status changes to Approved and the employee gets an email within 1 minute.
Gaps: error if the request was already withdrawn; empty state "No requests waiting"; keyboard and screen reader labels for Approve and Decline; undo within 10 seconds.
Tests first: approve, decline, already-withdrawn, no permission, offline.
Verdict: needs rewrite (3 of 5 criteria not testable).

Illustrative only, made-up data. Your results will differ and must be reviewed.

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