test-case-reviewer-plus (EN)

Chinese version： See the corresponding Chinese skill.

When to Use

• Need a stricter review of existing test cases before test execution or release.
• Need issue severity, business impact, and retest order to be explicit.

Workflow

1. Read and follow the main prompt listed under Progressive disclosure (coverage, structure, quality bar).
2. Accept direct materials or user-supplied multi-role review reports; when role reports are used, retain source_role and source identifiers.
3. Add only project context that changes the result: scope, environment, constraints, risks, dependencies, expected deliverable.
4. If input is incomplete, return a usable first draft and explicitly mark assumptions and gaps.
5. Default to Markdown; switch formats only when the user asks.

Core Constraints

• Prioritize by risk / business impact — do not treat everything equally.
• Separate confirmed facts from current assumptions.
• Do not invent endpoints, fields, environments, or root causes the user did not provide.
• Keep output executable: concrete scenarios, clear priority, clear next steps.
• Keep blockers, high-risk coverage gaps, maintainability findings, and low-value or duplicate cases in distinct sections rather than one generic issue list.
• Produce only AI findings and a recommendation; always output human_final_decision: pending, with the final pass, conditional-pass, or reject decision owned by a Human.

Progressive Disclosure

• Before producing output, read and follow prompts/test-case-reviewer-plus.md (minimum coverage, output structure, quality bar).
• When a ready-made template fits: use matching files under output-templates/.
• When the user wants examples or alignment with existing assets: read relevant examples/.
• For format conversion or helper checks: prefer existing scripts/ over reinventing.
• For evaluating/regressing this skill: use evals/ with skill-up.

Pre-delivery Checklist

• Followed the main prompt's output structure
• Minimum coverage focus: high-severity findings, coverage gaps, missing positive scenarios, missing negative scenarios, missing boundary scenarios, traceability, step and expectation quality, business impact, ... (details in main prompt)
• Covered the minimum checklist, or explained omissions
• High-risk items have explicit priority
• Did not invent details the user did not provide
• Assumptions and gaps are marked
• Multi-role inputs retain role sources and the four key finding categories remain distinct
• The AI recommendation does not impersonate a final Human approval or rejection record

Common Pitfalls

• Do not pretend completeness when scope/context is missing.
• Do not treat every item as equally important.
• Do not skip assumptions and information gaps.
• Do not turn an AI pass/reject recommendation into a final Human decision.
• Do not dump generic theory unrelated to the current toolchain.