nw-der-review-criteria
Evaluation criteria and scoring for data engineering artifact reviews
Best use case
nw-der-review-criteria is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Evaluation criteria and scoring for data engineering artifact reviews
Teams using nw-der-review-criteria should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/nw-der-review-criteria/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How nw-der-review-criteria Compares
| Feature / Agent | nw-der-review-criteria | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Evaluation criteria and scoring for data engineering artifact reviews
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Data Engineer Review Criteria Evaluation criteria for each review dimension. Load when performing reviews. ## Dimension 1: Research Citation Quality 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 ## Dimension 2: Security Coverage 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 ## Dimension 3: Trade-off Analysis 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 ## Dimension 4: Technical Accuracy **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 ## Dimension 5: Completeness **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 ## Dimension 6: Bias Detection **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 ## Dimension 7: Implementability **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 ## Severity Classification Guide **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.
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