nw-der-review-criteria

Solid

Evaluation criteria and scoring for data engineering artifact reviews

Code & Development 526 stars 55 forks Updated 1 weeks ago MIT

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Quality Score: 92/100

Stars 20%
91
Recency 20%
90
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# 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 al...

Details

Author
nWave-ai
Repository
nWave-ai/nWave
Created
3 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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