Evaluate Schema

Comprehensive database schema analysis using a 5-perspective expert panel.

When to Use

Use this skill when you need to:

•Review a database schema for quality and best practices
•Evaluate data modeling decisions before implementation
•Plan schema migrations with risk assessment
•Analyze normalization and denormalization trade-offs
•Assess schema scalability and performance implications

Workflow

Invoke the database-schema-evaluator skill for:

$ARGUMENTS

Default Parameters

Parameter	Value	Rationale
`evaluation_depth`	comprehensive	Full 5-perspective analysis
`include_alternatives`	false	Focus on current schema; enable for redesign
`scoring_rubric`	standard	Balanced across all dimensions

Expert Panel Composition

The evaluation assembles 5 domain experts:

Expert	Focus Area	Key Questions
Data Architect	Normalization, patterns, relationships	Is the schema properly normalized? Are relationships clear?
Performance Engineer	Indexing, query optimization, scalability	Will this scale? Are indexes appropriate?
Data Integrity Guardian	Constraints, referential integrity, validation	Can invalid data enter? Are constraints complete?
Evolution Strategist	Migrations, extensibility, versioning	How hard will changes be? Is it future-proof?
Operations Specialist	Backup, maintenance, monitoring	Can we maintain this? Recovery implications?

Evaluation Dimensions

Each expert scores on their primary dimensions:

Data Architect:

•Normalization level (1NF-BCNF appropriateness)
•Relationship clarity
•Naming conventions
•Domain modeling accuracy

Performance Engineer:

•Index coverage
•Query pattern support
•Partitioning strategy
•Denormalization trade-offs

Data Integrity Guardian:

•Primary key design
•Foreign key completeness
•Check constraints
•NULL handling

Evolution Strategist:

•Additive change ease
•Breaking change risk
•Schema versioning support
•Migration complexity

Operations Specialist:

•Backup/restore complexity
•Monitoring surface area
•Maintenance windows required
•Disaster recovery impact

Output Format

The schema evaluation produces:

xml

<schema-evaluation>
  <header>
    <id>[unique identifier]</id>
    <schema_name>[schema identifier]</schema_name>
    <evaluation_depth>comprehensive</evaluation_depth>
    <overall_score>[1-10]</overall_score>
  </header>

  <expert-assessments>
    <assessment expert="Data Architect">
      <score>[1-10]</score>
      <findings>
        <finding severity="[critical|high|medium|low]">
          [Specific issue or observation]
        </finding>
      </findings>
      <recommendations>
        <recommendation priority="[1-n]">
          [Specific improvement suggestion]
        </recommendation>
      </recommendations>
    </assessment>
    <!-- ... more expert assessments ... -->
  </expert-assessments>

  <consensus-findings>
    <strengths>
      <strength>[What the schema does well]</strength>
    </strengths>
    <concerns>
      <concern severity="[critical|high|medium|low]" experts="[which experts flagged]">
        [Shared concern across experts]
      </concern>
    </concerns>
  </consensus-findings>

  <prioritized-recommendations>
    <recommendation priority="1" effort="[low|medium|high]" impact="[low|medium|high]">
      <description>[What to change]</description>
      <rationale>[Why this matters]</rationale>
      <example>[Concrete example if applicable]</example>
    </recommendation>
  </prioritized-recommendations>

  <migration-considerations>
    [If changes recommended, migration implications]
  </migration-considerations>
</schema-evaluation>

Quality Gates

• All 5 expert perspectives represented
• Scores calibrated consistently across experts
• Critical issues flagged with severity
• Recommendations are specific and actionable
• Migration implications considered for recommended changes
• Trade-offs explicitly documented

Related Skills

After schema evaluation, consider:

•Run /compare-options if multiple schema designs exist
•Run /write-reference to document the final schema
•Run /research-brief for technology-specific best practices