Claude
Skill
acceptance-criteria-review
Use this skill when you need to review acceptance criteria for ambiguity, missing rules, and verifiability; triggers include acceptance criteria review.
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Download
naodeng-awesome-qa-skills-skills_en_testing-types_acceptance-criteria-review-c44b892.zip · 5 KB
Install
skills CLI
npx skills add https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/acceptance-criteria-review
Claude Code
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install naodeng-awesome-qa-skills@llmmart
Git
git clone https://github.com/naodeng/awesome-qa-skills.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole naodeng/awesome-qa-skills collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
Acceptance Criteria Review
When to Use
- Use this skill when you need to turn requirements and user stories into verifiable, unambiguous acceptance criteria that cover failure paths.
- Use it to review an existing plan, result, or evidence set and produce actionable improvements.
- Use it when context is incomplete but a bounded first pass is still valuable.
Output Format Options
- Default to Markdown for review, execution, and incremental refinement.
- When the user requests tables, CSV, JSON, or ticket fields, preserve risk, evidence, priority, and boundary information.
- For machine-consumed output, confirm the schema, enums, and required fields first.
How to Use
- Read and follow
prompts/acceptance-criteria-review.md, including its input contract, execution rules, minimum coverage, and output order. - Add only context that changes the decision: scope, environment, version, constraints, evidence, and success criteria.
- Audit the input, then separate confirmed facts, working assumptions, and open questions.
- Rank by risk and evidence strength, and produce an artifact that can be executed or reviewed directly.
- If information is missing, deliver a bounded first pass and state which conclusions remain unsupported.
Reference Files
- Always read
prompts/acceptance-criteria-review.md; it is the complete execution specification for this skill. - For evaluation or regression, read
evals/eval.yamland the relevant cases underevals/cases/. - Load
references/,examples/,scripts/, oroutput-formats.mdonly when those directories exist and the task needs them.
Core Constraints
- do not decide missing business rules on behalf of product owners
- make every criterion observable
- escalate ambiguities that block implementation or testing
- Never invent system behavior, fields, data, metrics, or root causes absent from the evidence.
- Link important conclusions to evidence; mark unsupported conclusions as hypotheses with a verification method.
- Explain priority using business impact, likelihood, or detectability.
Delivery Checklist
- Covered: testability and observable outcomes, happy, failure, and boundary paths, roles and permissions, data rules, state transitions, dependency failures, non-functional constraints, ambiguities.
- Separated facts, assumptions, gaps, and recommendations.
- Gave high-risk items a priority, evidence basis, owner or next action.
- Defined verifiable decision criteria instead of generic advice.
- Performed no unauthorized production writes or destructive actions.
Common Pitfalls
- Listing checks without preconditions, expected outcomes, or evidence.
- Marking everything high priority and avoiding tradeoffs.
- Substituting tool names or generic theory for domain reasoning.
- Refusing incomplete input, or pretending incomplete evidence supports certainty.
Best Practices
- Start with paths most likely to cause business loss, safety issues, or release blockage.
- Reduce uncertainty through the smallest verifiable experiment and record reproduction conditions.
- Make the artifact executable and independently reviewable by another engineer.
Files (awesome-qa-skills)
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agents
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openai.yaml 318 B
version: 1 metadata: key: acceptance-criteria-review interface: display_name: "Acceptance Criteria Review" short_description: "Review acceptance-criteria gaps and verifiability" default_prompt: "Use the acceptance-criteria-review skill to review acceptance criteria." policy: allow_implicit_invocation: true
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evals
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cases
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basic-success.yaml 880 B
id: basic-success title: "Acceptance Criteria Review: domain-complete scenario" description: | Checks that the skill produces domain-specific, evidence-based, prioritized, executable output. input: prompt: | Use the acceptance-criteria-review skill for this scenario: a membership refund feature with partial refunds, coupon restoration, role-based approval, and duplicate submission. The basic scope is available. Provide risk priorities, core execution items, expected results or decision criteria, evidence, and open questions. expect: must_contain: - "Task Understanding" - "Input Audit" - "Risks" - "P0" - "expected" must_not_contain: - "TODO" - "I cannot" - "unable to help" judge: type: rule_based success: - output_contains: all: - "Task Understanding" - "Input Audit" - "Risks" -
edge-incomplete-input.yaml 921 B
id: edge-incomplete-input title: "Acceptance Criteria Review: bounded first pass with incomplete input" description: | Checks that missing context leads to a bounded first pass with assumptions and gaps, not refusal or invention. input: prompt: | Use the acceptance-criteria-review skill. The only known fact is: a membership refund feature with partial refunds, coupon restoration, role-based approval, and duplicate submission. No environment, version, or supporting evidence is available. Deliver a useful first pass and state which conclusions are unsupported. expect: must_contain: - "Working Assumptions" - "Open Questions" - "evidence" - "Next Actions" must_not_contain: - "TODO" - "I cannot" - "unable to help" judge: type: rule_based success: - output_contains: all: - "Working Assumptions" - "Open Questions" - "evidence" -
edge-risk-priority.yaml 880 B
id: edge-risk-priority title: "Acceptance Criteria Review: high-risk boundary and tradeoffs" description: | Checks that a constrained window produces risk focus plus stop, escalation, or human-handoff boundaries. input: prompt: | Use the acceptance-criteria-review skill for: a membership refund feature with partial refunds, coupon restoration, role-based approval, and duplicate submission. The execution window is half a day and no destructive production action is authorized. Bound the scope, prioritize P0/P1, and state residual risk plus stop or escalation conditions. expect: must_contain: - "P0" - "P1" - "Residual Risk" - "condition" must_not_contain: - "TODO" - "I cannot" - "unable to help" judge: type: rule_based success: - output_contains: all: - "P0" - "P1" - "Residual Risk"
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eval.yaml 557 B
schema_version: v1alpha1 environment: type: none skills: - source: local_path path: . engine: name: claude_code # model is optional; omit to use engine default # model: # provider: anthropic # name: claude-sonnet-4-6 cases: files: - evals/cases/basic-success.yaml - evals/cases/edge-incomplete-input.yaml - evals/cases/edge-risk-priority.yaml defaults: timeout_seconds: 180 max_turns: 8 expect: exit_code: 0 must_not_contain: - "TODO" - "I cannot" report: formats: [json]
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prompts
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acceptance-criteria-review.md 3.4 KB
# Acceptance Criteria Review Prompt Turn requirements and user stories into verifiable, unambiguous acceptance criteria that cover failure paths and produce an artifact that can be executed, reviewed, and tracked directly. ## Role You are a senior risk- and evidence-driven QA practitioner who controls conclusion boundaries when context is incomplete. ## Input Prefer real materials supplied by the user: - requirements - user stories and personas - business rules and state transitions - designs or interface constraints - scope, environment, version, time budget, toolchain, and prohibited actions - existing results, historical failures, monitoring evidence, and stakeholder concerns If critical input is absent, list `Working Assumptions` and `Open Questions`, then still deliver a bounded first pass. ## What to do 1. Restate the objective, subject, and success criteria in one sentence. 2. Audit input completeness, credibility, recency, and comparability. 3. Build a risk or failure model and prioritize high-impact, likely, or hard-to-detect issues. 4. Convert analysis into concrete scenarios, assertions, verification steps, or decision gates. 5. Report residual risk, evidence gaps, and next actions without presenting hypotheses as facts. ## Execution Rules - do not decide missing business rules on behalf of product owners - make every criterion observable - escalate ambiguities that block implementation or testing - Give an evidence basis for every important conclusion; label unsupported claims as `Hypothesis to Verify`. - Each scenario must include preconditions, action or stimulus, expected behavior, and required evidence. - Use P0/P1/P2/P3 or an equivalent scale and explain the ranking. - Reuse the current toolchain and assets; avoid large code samples unless the user requests them. - For production, security, or privacy work, default to least privilege, masked data, mocks, dry runs, or isolated environments. ## Minimum Coverage Checklist Unless the user narrows the scope, cover at least: - testability and observable outcomes - happy, failure, and boundary paths - roles and permissions - data rules - state transitions - dependency failures - non-functional constraints - ambiguities - confirmed facts, working assumptions, and open questions - blockers for execution, release, or decision making - residual risk and how it will be accepted, mitigated, or investigated ## Output Use this order: ### 1. Task Understanding and Scope - objective, subject, success criteria, inclusions, and exclusions ### 2. Input Audit - confirmed facts, working assumptions, open questions, and evidence quality ### 3. Risks and Priorities - P0/P1/P2/P3, impact, rationale, and sequence ### 4. Core Analysis and Execution Items - acceptance criteria - gaps and ambiguities - risk priorities - rewrite proposals - open questions - include preconditions, steps, expected result or decision criterion, and evidence for each item ### 5. Blockers and Residual Risk - stop, escalation, rollback, or human-handoff conditions ### 6. Next Actions and Open Questions - smallest verification actions, suggested owners, and missing materials ## Quality Bar - Tailor the content to the input; do not merely rename a generic template. - Make high-risk paths concrete with failure modes, expected behavior, and evidence. - Never invent numbers, root causes, or system behavior. - Let an executor act without guessing and a reviewer trace every important judgment.
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SKILL.md 3.3 KB
--- name: acceptance-criteria-review description: Use this skill when you need to review acceptance criteria for ambiguity, missing rules, and verifiability; triggers include acceptance criteria review. --- # Acceptance Criteria Review ## When to Use - Use this skill when you need to turn requirements and user stories into verifiable, unambiguous acceptance criteria that cover failure paths. - Use it to review an existing plan, result, or evidence set and produce actionable improvements. - Use it when context is incomplete but a bounded first pass is still valuable. ## Output Format Options - Default to Markdown for review, execution, and incremental refinement. - When the user requests tables, CSV, JSON, or ticket fields, preserve risk, evidence, priority, and boundary information. - For machine-consumed output, confirm the schema, enums, and required fields first. ## How to Use 1. Read and follow `prompts/acceptance-criteria-review.md`, including its input contract, execution rules, minimum coverage, and output order. 2. Add only context that changes the decision: scope, environment, version, constraints, evidence, and success criteria. 3. Audit the input, then separate confirmed facts, working assumptions, and open questions. 4. Rank by risk and evidence strength, and produce an artifact that can be executed or reviewed directly. 5. If information is missing, deliver a bounded first pass and state which conclusions remain unsupported. ## Reference Files - Always read `prompts/acceptance-criteria-review.md`; it is the complete execution specification for this skill. - For evaluation or regression, read `evals/eval.yaml` and the relevant cases under `evals/cases/`. - Load `references/`, `examples/`, `scripts/`, or `output-formats.md` only when those directories exist and the task needs them. ## Core Constraints - do not decide missing business rules on behalf of product owners - make every criterion observable - escalate ambiguities that block implementation or testing - Never invent system behavior, fields, data, metrics, or root causes absent from the evidence. - Link important conclusions to evidence; mark unsupported conclusions as hypotheses with a verification method. - Explain priority using business impact, likelihood, or detectability. ## Delivery Checklist - [ ] Covered: testability and observable outcomes, happy, failure, and boundary paths, roles and permissions, data rules, state transitions, dependency failures, non-functional constraints, ambiguities. - [ ] Separated facts, assumptions, gaps, and recommendations. - [ ] Gave high-risk items a priority, evidence basis, owner or next action. - [ ] Defined verifiable decision criteria instead of generic advice. - [ ] Performed no unauthorized production writes or destructive actions. ## Common Pitfalls - Listing checks without preconditions, expected outcomes, or evidence. - Marking everything high priority and avoiding tradeoffs. - Substituting tool names or generic theory for domain reasoning. - Refusing incomplete input, or pretending incomplete evidence supports certainty. ## Best Practices - Start with paths most likely to cause business loss, safety issues, or release blockage. - Reduce uncertainty through the smallest verifiable experiment and record reproduction conditions. - Make the artifact executable and independently reviewable by another engineer.
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