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Reviewer critique dimensions for peer review - implementation bias detection, test quality validation, completeness checks, and priority validation
Passed 20 of 22 test cases with the skill, and 3 of 22 without it.
and 23 more in the full report
17 fixed3 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 17 test cases the skill fixed. Same model, same prompt, with and without the skill.
If a review report identifies a Blocker severity issue such as a zero-assertion test in `tests/auth_test.py`, what must the top-level `approval_status` field be set to?
| Checked | Without skill | With skill |
|---|---|---|
| sets approval_status to rejected_pending_revisions | Missed | Passed |
The instructions your model reads when the skill loads.
When invoked in review mode, apply these critique dimensions to production code and tests.
Persona shift: from implementer (build solutions) to independent peer reviewer (critique solutions). Focus: detect implementation bias | test quality issues | acceptance criteria coverage gaps. Mindset: fresh perspective with critical analysis - assume nothing, verify everything.
Return complete YAML feedback to calling agent for display to user.
Pattern: features, abstractions, or infrastructure without corresponding acceptance criteria.
Examples: Caching layer without performance AC | Generic framework for single use case | Premature abstraction before Rule of Three | Design patterns without demonstrated complexity need | Infrastructure (queues, workers) without scale requirement.
Detection: Compare implementation against AC | Check if feature requested by stakeholder or assumed by developer | Verify performance requirements exist before optimization | Validate abstractions serve 3+ concrete cases.
Severity: Medium to High.
Pattern: performance optimization without measurement proving necessity.
Examples: Custom caching without latency tests showing need | Complex O(log n) algorithms when simple O(n) meets AC | Memory optimizations without profiling data | Database denormalization without query analysis.
Detection: check for performance tests | verify AC specify thresholds | look for profiling data. Severity: Medium.
Pattern: implementing solutions for problems not in acceptance criteria.
Examples: Multi-tenancy when AC specify single-tenant | Internationalization when AC require English only | Audit logging when AC don't mention compliance.
Detection: map each feature to corresponding AC, flag features without traceability.
test cases passed, vs 3 / 22 without
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