Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
日本語の概要は準備中です。原文の説明を表示しています。
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.
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概要と使いどころ
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
日本語の概要は準備中です。原文の説明を表示しています。
Review dimensions for validating agent quality - template compliance, safety, testing, and priority validation
日本語の概要は準備中です。原文の説明を表示しています。
Review dimensions for acceptance test quality - happy path bias, GWT compliance, business language purity, coverage completeness, walking skeleton user-centricity, priority validation, observable behavior assertions, traceability coverage, and walking skeleton boundary proof
日本語の概要は準備中です。原文の説明を表示しています。
Detailed 5-phase workflow for creating agents - from requirements analysis through validation and iterative refinement
日本語の概要は準備中です。原文の説明を表示しています。
5-layer testing approach for agent validation including adversarial testing, security validation, and prompt injection resistance
日本語の概要は準備中です。原文の説明を表示しています。
Architectural style selection decision matrices, trade-off analysis, structural enforcement rules, and combination patterns. Load when choosing or evaluating architecture styles.
日本語の概要は準備中です。原文の説明を表示しています。