deliver-acceptance-criteria
About deliver-acceptance-criteria
deliver-acceptance-criteria is a skill that turns a defined user story or feature slice into testable acceptance scenarios. It is for teams that need engineers and QA to agree on completion conditions, using observable Given/When/Then outcomes across primary flows, exceptions, and relevant non-functional expectations.
Key features
- 01Confirm story or feature scope
- 02Separate success flows from exceptions
- 03Write scenarios in Given/When/Then form
- 04Cover recovery, failures, and non-functional needs
- 05Check criteria for independent testability
Use cases
- 01Draft QA acceptance conditions for sprint planning
- 02Clarify pass and fail conditions for handoffs
- 03Specify edge cases and error behavior in a story
- 04Turn feature requirements into verifiable scenarios
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ product-on-purpose/ pm-skills --skill deliver-acceptance-criteriaAfter installing, type/deliver-acceptance-criteriain Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
HISTORY.mdevals/trigger-fixtures.jsonevals/output-scenarios/bulk-invite.mdreferences/EXAMPLE.mdTEMPLATE.mdDetails
From the project READMEAcceptance Criteria
Acceptance criteria define the observable behavior that must be true for a story or feature to be considered done. This skill turns feature context into concise, testable Given/When/Then scenarios that engineers and QA can verify without guessing intent.
When to Use
- After a user story, PRD section, or feature slice is defined
- When a team needs clear pass/fail conditions for implementation
- When writing QA-ready criteria for sprint planning or handoff
- When a story has edge cases, error paths, or non-functional expectations that should be explicit
When NOT to Use
- You need the user stories themselves -> use
deliver-user-stories; this skill deepens a story that already exists - You need systematic failure coverage across a whole feature -> use
deliver-edge-cases; this skill stays story-scoped - There is no story or slice to bind criteria to yet -> use
deliver-prdordeliver-user-storiesfirst - You are defining success metrics for an experiment, not done-ness for a story -> use
measure-experiment-design - The criterion is a rate over model output, such as "90% of answers are faithful to their sources" -> use the AI Behavior and Evaluation section of
deliver-prd, which says a rate needs a designed sample to verify it, and what to record until one exists
Instructions
When asked to create acceptance criteria, follow these steps:
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Confirm the story or feature scope Identify the exact slice of work. If the scope is unclear, ask for the user story, PRD section, or feature description before drafting criteria.
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Separate the happy path from exceptions Start with the primary success flow, then add edge cases and error states that are likely or costly if missed.
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Write each criterion as an observable scenario Use Given/When/Then language only. Keep each criterion independently testable and avoid implementation details.
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Cover recovery and failure behavior Describe what the user sees or can do when validation fails, a dependency is unavailable, or a save action cannot complete.
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Include non-functional expectations Add criteria for performance, accessibility, security, reliability, or auditability when they matter to the story.
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Avoid duplication and overlap Each criterion should test one outcome. If two criteria describe the same behavior, merge or split them until the intent is clear.
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Review for testability Ensure a reviewer can pass or fail each criterion without interpretation. If a statement is subjective, rewrite it into a measurable outcome.
Output Contract
Use references/TEMPLATE.md as the output format. A complete response should:
- Restate the feature or story context
- Group criteria into happy path, edge cases, error states, and non-functional criteria
- Use explicit Given/When/Then statements for each criterion
- Note assumptions or open questions when context is incomplete
Quality Checklist
Before finalizing, verify:
- The criteria map to a specific story or feature slice
- The happy path is covered first
- Edge cases are explicit, not implied
- Error states include user-visible recovery behavior
- Non-functional criteria are included when relevant
- Each criterion is testable and has one clear outcome
- No implementation details leak into the acceptance criteria
Examples
See references/EXAMPLE.md for a completed example based on a realistic e-commerce checkout flow.