nWave/skills/nw-der-review-criteria/SKILL.md
Evaluation criteria and scoring for data engineering artifact reviews
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Evaluation criteria for each review dimension. Load when performing reviews.
Evaluate whether recommendations trace to specific evidence.
Checks: Each major recommendation cites a specific research finding | Citations are accurate (finding number matches content) | Vendor-specific claims have multiple independent sources | General best practices distinguished from research-validated guidance
Scoring: 10: All cited, verified | 7: Most cited, 1-2 missing on non-critical points | 4: Major recommendations lack citations | 0: No citations
Evaluate defense-in-depth for data layer.
Checks: Encryption at rest (TDE) | Encryption in transit (TLS) | Access control model (RBAC/ABAC) | SQL injection prevention (parameterized queries) | OWASP/NIST standards referenced | Credential handling (no hardcoded secrets)
Scoring: 10: All 6 checks with standard references | 7: 4-5 checks | 4: 2-3 checks, missing encryption or injection prevention | 0: Security not mentioned
Evaluate balanced presentation of alternatives.
Checks: Multiple technology options (minimum 2) | Pros/cons for each | Context factors identified (scale, consistency, latency, cost) | Recommendation justified by context fit | Limitations acknowledged
Scoring: 10: Comprehensive trade-offs with context-driven justification | 7: Trade-offs present, some alternatives missing | 4: Single recommendation without alternatives | 0: Prescriptive with no analysis
Checks: SQL/NoSQL syntax correct for specified DB | Architecture patterns appropriate for use case (OLTP vs OLAP, write-heavy vs read-heavy) | Optimization strategies valid for target DB | Normalization level appropriate for workload | Index type matches query patterns (B-tree for range, hash for equality) | CAP trade-offs correctly applied
Scoring: 10: All technical claims verified | 7: Minor syntax/edge-case issues | 4: Significant errors affecting recommendations | 0: Fundamentally incorrect guidance
Checks: Scaling strategy (vertical, horizontal, sharding, replication) | Performance characteristics (query patterns, bottlenecks) | Data governance when applicable (lineage, quality, MDM) | Compliance when personal/regulated data involved (GDPR, CCPA, HIPAA) | Backup/recovery for production designs | Monitoring/observability
Scoring: 10: All applicable aspects covered | 7: Core covered, 1-2 peripheral missing | 4: Major gaps (missing scaling or governance for production) | 0: Only immediate question, no broader context
Checks: No single-vendor preference without justification | No latest-technology bias (new tech only when justified) | Contradictory evidence acknowledged | Open-source and commercial both considered | Technology maturity/community factored in | Cost mentioned
Scoring: 10: Demonstrably balanced with explicit trade-offs | 7: Generally balanced, minor preferences | 4: Clear vendor/tech bias | 0: Single-vendor advocacy
Checks: Schema designs include column types, constraints, indexes | Architecture specifies integration points/APIs | Security has concrete steps (not just "use encryption") | Migration path described if changing systems | Dependencies/prerequisites identified | Handoff to next agent clear
Scoring: 10: Downstream agent proceeds without clarification | 7: Minor clarifications needed | 4: Significant implementation details missing | 0: Abstract guidance, no actionable content
Blocker: Prevents downstream work or introduces security vulnerability. Examples: missing encryption for PII, wrong DB choice for workload, SQL syntax errors in migrations.
Major: Significantly reduces quality or misses important considerations. Examples: missing trade-off analysis, no scaling strategy for production, incomplete security coverage.
Minor: Improvement that does not block progress. Examples: missing citation on secondary recommendation, single alternative not considered, minor syntax variation.
Suggestion: Enhancement that adds polish. Examples: additional index for edge-case query, governance for future compliance, alternative monitoring approach.
testing
Acceptance test creation methodology for the DISTILL wave. Domain knowledge for the acceptance designer agent: port-to-port principle, prior wave reading, wave-decision reconciliation, graceful degradation, and document back-propagation.
development
Cross-agent collaboration protocols, workflow handoff patterns, and commit message formats for TDD/Mikado/refactoring workflows
development
Creates a phased roadmap.json for a feature goal with acceptance criteria and TDD steps. Use when planning implementation steps before execution.
testing
Acceptance test creation methodology for the DISTILL wave. Domain knowledge for the acceptance designer agent: port-to-port principle, prior wave reading, wave-decision reconciliation, graceful degradation, and document back-propagation.