nw-ab-critique-dimensi…
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Quality dimensions and review checklist for devop reviews
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Quality dimensions and review checklist for devop reviews
name: nw-par-review-criteria description: Quality dimensions and review checklist for devop reviews user-invocable: false disable-model-invocation: true
**Pattern**: Phase handoffs missing required artifacts or approvals.
**Required per Phase**:
**Severity**: critical. Verify all artifacts present and peer-reviewed before phase transition.
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**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.
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**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.
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**Purpose**: Validate roadmap addresses largest bottleneck first, not secondary concern.
**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.
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**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**: 1. **Wiring test exists**: at least one acceptance test invokes feature through driving port 2. **Component integrated**: implemented component called from entry point module 3. **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.
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AI agents that guide you from idea to working code, with human judgment at every gate. nWave runs inside Claude Code. It breaks feature delivery into seven waves (discover, diverge, discuss, design, devops, distill, deliver).
Repo: nWave-ai/nWave
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
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