Mock Quality Review

review whether mocks protect real risks from contracts, mock implementations, interaction assertions, and environment differences. Produce MQR-## findings. This Skill organizes traceable test-double quality candidates only; it does not execute tests or turn a design inventory into coverage, pass, or release evidence.

When to Use

• When you need mock quality review candidates from API or service contracts, mock implementations, stub data, interaction assertions, integration tests, drift records, and real responses.
• When you need selection rationale, applicability constraints, evidence gaps, and the smallest validation action.
• When inputs are incomplete but a bounded first pass can preserve blocked or unassessed boundaries.

Do not use it to execute tests, invent behavior conclusions, replace a complete strategy, or accept risk for a Human.

Output Format Options

• Use Markdown by default; use tables, JSON, or CSV only when explicitly requested or required by the delivery format.
• Separate static analysis, unexecuted work, evidence states, and Human decisions; keep items unassessed, blocked, or NOT_RUN when runtime evidence is absent.

How to Use

1. Read prompts/mock-quality-review.md and provide the objective, scope, material, environment, and evidence.
2. Complete the known, missing, conflicting, stale, out_of_scope, and assumptions input audit before findings.
3. Record MQR-## with the subject, preconditions, concern, source evidence, and validation, plus impact/priority, owner role, close condition, and evidence state.
4. Preserve conflicts, unknown constraints, and open questions when evidence is incomplete.

Core Constraints

• Do not execute tests, assume missing rules, versions, thresholds, data, or outcomes, or treat candidate counts as coverage proof.
• File presence, names, design declarations, and Eval configuration are not runtime evidence.
• Mark unknowns unassessed, blocked, or pending clarification instead of filling them with convention.
• Do not edit requirements, code, test assets, or target systems.

Pre-delivery Check

• Recorded the known, missing, conflicting, stale, out_of_scope, and assumptions input audit.
• Every MQR-## has source, evidence state, impact/priority, owner role, close condition, and validation.
• Facts, inferences, recommendations, unexecuted work, and Human decisions remain separate.
• Findings are not execution results, coverage proof, or release claims.

Reference Files

• Read evals/eval.yaml and matching cases for regression; configuration does not prove project results.
• Use evals/trigger-prompts.csv and evals/local-rules.json for trigger checks; missing skill.selection evidence is BLOCKED.

Common Pitfalls

• Do not turn a method name, file presence, or candidate count into test execution, coverage, pass, or release evidence when scope or evidence is incomplete.
• Do not fill in missing rules, thresholds, data, environments, or results from convention; preserve unassessed, blocked, and pending items.
• Do not expand this specialist design or review into a complete strategy, full test cases, runtime execution, or a release decision.

Best Practices

• Complete the six-part input audit before selecting the smallest traceable and verifiable finding scope.
• Keep the source, evidence state, impact/priority, owner role, close condition, validation method, and residual risk for every finding.
• Write validation suggestions as next actions; do not upgrade package structure, candidate counts, or local Eval configuration into real quality conclusions.