nWave-ai DevOp Reviewer: Review Criteria
Critique Dimension 1: Incomplete Phase Handoffs
Pattern: Phase handoffs missing required artifacts or approvals.
Required per Phase:
- DISCUSS: Requirements document + peer review approval
- DESIGN: Architecture document + ADRs + peer review approval
- DISTILL: Acceptance tests + peer review approval
- DELIVER: Production code + tests (100% passing) + peer review approval
Severity: critical. Verify all artifacts present and peer-reviewed before phase transition.
Critique Dimension 2: Deployment Readiness Gaps
Pattern: Feature marked "ready" but missing production prerequisites.
Required: All tests passing (100%) | Production configuration complete | Monitoring/alerting configured | Runbook/operational docs created | Rollback plan documented.
Severity: critical. Complete missing prerequisite before marking deployment-ready.
Critique Dimension 3: Traceability Violations
Pattern: Cannot trace production code back to requirements.
Required: User stories map to acceptance tests | Acceptance tests map to production code | Code changes traceable to commits | All AC verified in production.
Severity: high. Establish traceability chain: user-story -> acceptance-tests -> code-commits.
Critique Dimension 4: Priority Validation
Purpose: Validate roadmap addresses largest bottleneck first, not secondary concern.
Questions
Q1: Is this the largest bottleneck? Does timing data show primary problem? Larger problem being ignored? Assessment: YES / NO / UNCLEAR.
Q2: Were simpler alternatives considered? Roadmap includes rejected alternatives? Rejection reasons evidence-based? Simpler solution achieves 80% benefit? Assessment: ADEQUATE / INADEQUATE / MISSING.
Q3: Is constraint prioritization correct? Constraints quantified by impact? Architecture addresses constraint-free opportunities first? Minority constraint dominating? (flag if >50% of solution for <30% of problem). Assessment: CORRECT / INVERTED / NOT_ANALYZED.
Q4: Is architecture data-justified? Key architectural decision supported by quantitative data? Different data leads to different architecture? Assessment: JUSTIFIED / UNJUSTIFIED / NO_DATA.
Failure Conditions
- FAIL if Q1 = NO (wrong problem being addressed)
- FAIL if Q2 = MISSING (no alternatives considered)
- FAIL if Q3 = INVERTED (minority constraint dominating)
- FAIL if Q4 = NO_DATA and this is performance optimization
Critique Dimension 5: Functional Integration
Purpose: Verify feature wired into system entry point -- prevents Testing Theatre. A feature with 100% test coverage but 0% wiring tests is not complete.
Validation Criteria:
- Wiring test exists: at least one acceptance test invokes feature through driving port
- Component integrated: implemented component called from entry point module
- Boundary correct: acceptance tests do not import internal components directly
Gate failure response: Block finalization | report specific integration gap with evidence | require integration step before completion.
Quality Gate Checklist
Technical Completion
- All acceptance tests passing with stakeholder validation
- Unit test coverage meeting project standards (>=80%)
- Integration test validation of cross-component functionality
- Code review completed with approval
- Static analysis and security scan passed
- Performance tested under realistic load
Architecture Compliance
- Implementation aligns with architectural design
- Component boundaries and interfaces respected
- Security architecture implemented correctly
Production Readiness
- Monitoring and alerting configured
- Logging and debugging capability validated
- Rollback procedure documented and tested
- Operational runbook complete
- Support team trained / knowledge transferred
Business Completion
- All user stories completed with acceptance criteria met
- Business rules implemented and validated
- Stakeholder acceptance obtained
Source: nWave-ai/nWave → nWave/skills/nw-par-review-criteria/SKILL.md
Also appears in: nWave-ai/nWave/plugins/nw/skills/nw-par-review-criteria/SKILL.md