Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
borghei avatar

Database Designer

borghei/claude-skills

  • 140 installs
  • 451 repo stars
  • Updated July 21, 2026
  • borghei/claude-skills

Design normalized schemas, indexes, constraints, and migration plans for new features needing durable storage, reporting, and transactional integrity.

About

Helps Claude architect production-grade relational databases with clear entities, keys, constraints, indexing, and migration steps aligned to SaaS and API backends needing scalable persistence.

  • Entity-relationship modeling
  • Normalization and constraint design
  • Index and query-plan awareness
  • Migration and rollout planning
  • Multi-tenant and audit patterns

Database Designer by the numbers

  • 140 all-time installs (skills.sh)
  • Ranked #284 of 911 Databases skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/borghei/claude-skills --skill database-designer

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs140
repo stars★ 451
Last updatedJuly 21, 2026
Repositoryborghei/claude-skills ↗

What it does

Design normalized schemas, indexes, constraints, and migration plans for new features needing durable storage, reporting, and transactional integrity.

Files

SKILL.mdMarkdownGitHub ↗

Database Designer

The agent analyzes SQL schemas for normalization compliance, recommends optimal indexes based on query patterns, and generates safe migration scripts with rollback procedures. It produces Mermaid ERDs, detects redundant indexes, and implements zero-downtime expand-contract migration patterns for PostgreSQL and MySQL.

Quick Start

# Analyze a schema for normalization issues and generate ERD
python schema_analyzer.py --input schema.sql --generate-erd --output-format json

# Recommend indexes based on query patterns
python index_optimizer.py --schema schema.json --queries queries.json --analyze-existing

# Generate migration scripts between schema versions
python migration_generator.py --current current.json --target target.json --zero-downtime

---

Core Workflows

Workflow 1: Analyze and Optimize a Schema

1. Provide DDL (SQL) or JSON schema definition 2. Run schema_analyzer.py to detect normalization violations (1NF-BCNF), missing constraints, and naming issues 3. Review generated Mermaid ERD for relationship visualization 4. Run index_optimizer.py with query patterns to get index recommendations 5. Validation checkpoint: All 1NF-3NF violations addressed; foreign keys declared; no redundant indexes

python schema_analyzer.py -i schema.sql -f json -e -o report.json
python index_optimizer.py -s schema.json -q queries.json -e -p 2 -o index_report.json

Workflow 2: Generate a Safe Migration

1. Export current and target schemas as JSON 2. Run migration_generator.py to produce forward and rollback SQL 3. For large tables (10M+ rows), add --zero-downtime for expand-contract pattern 4. Review validation queries that confirm migration success 5. Validation checkpoint: Every forward step has a rollback counterpart; validation queries pass on test data

python migration_generator.py -c current.json -t target.json -z --include-validations -f json -o plan.json

Workflow 3: Index Optimization for Query Patterns

1. Document top 10 query patterns as JSON (WHERE clauses, JOINs, ORDER BY) 2. Run index_optimizer.py with --analyze-existing to find redundancies 3. Review composite index column ordering (most selective first) 4. Check for covering index opportunities 5. Validation checkpoint: Query patterns covered; no overlapping indexes; estimated 40%+ query time reduction

---

Index Type Selection

Index TypeBest ForExample
B-treeRange queries, sorting, equalityCREATE INDEX idx ON tasks (status, created_date)
PartialSubset queries on hot dataCREATE INDEX idx ON users (email) WHERE status = 'active'
CoveringAvoiding table lookupsCREATE INDEX idx ON users (email) INCLUDE (name, status)
HashExact match onlyPrimary keys, cache keys
GINJSONB, array, full-textCREATE INDEX idx ON docs USING GIN (data)

---

Anti-Patterns

  • Over-indexing -- every column indexed wastes write performance and storage; index only columns appearing in WHERE, JOIN, and ORDER BY
  • Missing foreign keys -- relying on application-layer referential integrity leads to orphaned records; always declare FK constraints
  • VARCHAR(255) everywhere -- oversized columns waste memory in indexes; right-size columns based on actual data
  • Premature denormalization -- denormalize only when EXPLAIN ANALYZE shows join-related bottlenecks, not preemptively
  • Direct ALTER on large tables -- ALTER TABLE ... SET NOT NULL on a 100M-row table locks the table; use expand-contract pattern
  • No validation queries in migrations -- migrations without post-step validation risk silent data corruption

Troubleshooting

ProblemCauseSolution
Schema analyzer reports false 1NF violationsJSON or array columns detected as multi-valued fieldsReview flagged columns; intentional JSONB/array usage is valid for document-style storage patterns
Index optimizer recommends indexes on low-selectivity columnsBoolean or status columns appear in frequent WHERE clausesUse partial indexes (WHERE status = 'active') instead of full-column indexes to reduce overhead
Migration generator produces high-risk steps for column type changesDirect ALTER COLUMN ... TYPE can lock tables and fail on incompatible dataUse the --zero-downtime flag to generate expand-contract migration patterns with safe backfill steps
ERD output missing relationshipsForeign key constraints not declared in DDL or JSON inputEnsure all FK relationships are explicitly defined; the analyzer only detects declared constraints
Composite index column order seems wrongOptimizer orders by estimated selectivity, not query clause orderVerify cardinality estimates in the schema JSON; provide cardinality_estimate per column for accurate ordering
Redundancy analysis flags covering indexes as overlappingOverlap ratio calculation uses Jaccard similarity on column setsReview flagged pairs manually; covering indexes with INCLUDE columns serve a different purpose than their subsets
Validation queries fail after migrationTarget schema JSON does not match actual post-migration stateRun --validate-only before and after migration; ensure the target JSON reflects all intended changes precisely

Success Criteria

  • Schema analysis detects 90%+ of normalization violations (1NF through BCNF) when provided complete DDL input
  • Index recommendations reduce query execution time by 40%+ for analyzed query patterns (measured via EXPLAIN ANALYZE before/after)
  • Migration scripts execute with zero data loss and include verified rollback for every forward step
  • ERD generation produces valid Mermaid diagrams that render correctly for schemas with up to 50 tables
  • Redundant index detection identifies 95%+ of duplicate and overlapping indexes with less than 5% false positive rate
  • Zero-downtime migrations maintain full application availability during schema changes on tables with 10M+ rows
  • Generated SQL statements are syntactically valid and compatible with PostgreSQL 14+ and MySQL 8.0+

Scope & Limitations

Covers:

  • Schema design analysis for SQL databases (PostgreSQL, MySQL) including normalization, constraints, naming, and data types
  • Index optimization with selectivity estimation, composite index ordering, covering indexes, and redundancy detection
  • Migration generation with forward/rollback scripts, zero-downtime patterns, and validation queries
  • ERD generation in Mermaid format from DDL or JSON schema definitions

Does NOT cover:

  • Runtime query performance monitoring or live database profiling (see performance-profiler skill)
  • NoSQL-specific schema design for MongoDB, DynamoDB, or Cassandra (conceptual guidance only in the reference sections)
  • Database administration tasks such as backup/restore, replication setup, or user/role management
  • Application-level ORM configuration, connection pool tuning, or driver-specific optimizations (see database-schema-designer for ORM-adjacent patterns)

Integration Points

SkillIntegrationData Flow
migration-architectMigration strategy and execution planning for large-scale schema changesDatabase Designer generates migration SQL; Migration Architect orchestrates multi-service deployment order and rollback coordination
database-schema-designerComplementary schema design with focus on application-layer patternsDatabase Designer provides normalization analysis; Schema Designer applies ORM mapping and application modeling conventions
performance-profilerRuntime validation of index and schema optimization recommendationsDatabase Designer outputs recommended indexes; Performance Profiler measures actual query plan improvements via EXPLAIN ANALYZE
api-design-reviewerAlignment between database schema and API resource contractsDatabase Designer defines table structures; API Design Reviewer validates that endpoint schemas match underlying data models
ci-cd-pipeline-builderAutomated migration execution in deployment pipelinesDatabase Designer generates migration scripts; CI/CD Pipeline Builder integrates them into deployment stages with validation gates
observability-designerDatabase performance monitoring and alerting post-optimizationDatabase Designer identifies query patterns; Observability Designer configures slow query alerts and index usage dashboards

Tool Reference

schema_analyzer.py

Purpose: Analyzes SQL DDL statements and JSON schema definitions for normalization compliance, missing constraints, data type issues, naming convention violations, and relationship mapping. Generates Mermaid ERD diagrams.

Usage:

python schema_analyzer.py --input schema.sql --output-format json
python schema_analyzer.py --input schema.json --output-format text
python schema_analyzer.py --input schema.sql --generate-erd --output analysis.json
python schema_analyzer.py --input schema.sql --erd-only

Flags/Parameters:

FlagShortRequiredDescription
--input-iYesInput file path (SQL DDL or JSON schema)
--output-oNoOutput file path (default: stdout)
--output-format-fNoOutput format: json or text (default: text)
--generate-erd-eNoInclude Mermaid ERD diagram in output
--erd-onlyNoOutput only the Mermaid ERD diagram

Example:

python schema_analyzer.py -i my_schema.sql -f json -e -o report.json

Output Formats:

  • text -- Human-readable report with normalization findings, constraint issues, data type recommendations, and naming violations
  • json -- Structured JSON with normalization_issues, constraint_issues, data_type_issues, naming_issues, relationships, and optional erd_diagram fields

---

index_optimizer.py

Purpose: Analyzes schema definitions and query patterns to recommend optimal indexes. Identifies missing indexes, detects redundant and overlapping indexes, suggests composite index column ordering, estimates selectivity, and generates CREATE INDEX statements.

Usage:

python index_optimizer.py --schema schema.json --queries queries.json --format text
python index_optimizer.py --schema schema.json --queries queries.json --output recommendations.json --format json
python index_optimizer.py --schema schema.json --queries queries.json --analyze-existing
python index_optimizer.py --schema schema.json --queries queries.json --min-priority 2

Flags/Parameters:

FlagShortRequiredDescription
--schema-sYesSchema definition JSON file
--queries-qYesQuery patterns JSON file
--output-oNoOutput file path (default: stdout)
--format-fNoOutput format: json or text (default: text)
--analyze-existing-eNoInclude analysis of existing indexes for redundancy
--min-priority-pNoMinimum priority level to include: 1=highest, 4=lowest (default: 4)

Example:

python index_optimizer.py -s schema.json -q queries.json -f json -e -p 2 -o index_report.json

Output Formats:

  • text -- Human-readable report with analysis summary, high-priority recommendations, redundancy issues, performance impact analysis, and CREATE INDEX statements
  • json -- Structured JSON with analysis_summary, index_recommendations (by priority), redundancy_analysis, size_estimates, sql_statements, and performance_impact fields

---

migration_generator.py

Purpose: Generates safe migration scripts between schema versions. Compares current and target schemas, produces ALTER TABLE statements, implements zero-downtime expand-contract patterns, creates rollback scripts, and generates validation queries.

Usage:

python migration_generator.py --current current.json --target target.json --format text
python migration_generator.py --current current.json --target target.json --output migration.sql --format sql
python migration_generator.py --current current.json --target target.json --zero-downtime --format json
python migration_generator.py --current current.json --target target.json --validate-only

Flags/Parameters:

FlagShortRequiredDescription
--current-cYesCurrent schema JSON file
--target-tYesTarget schema JSON file
--output-oNoOutput file path (default: stdout)
--format-fNoOutput format: json, text, or sql (default: text)
--zero-downtime-zNoGenerate zero-downtime migration using expand-contract pattern
--validate-only-vNoOnly generate validation queries, skip migration steps
--include-validationsNoInclude validation queries in migration output

Example:

python migration_generator.py -c current.json -t target.json -z --include-validations -f json -o migration_plan.json

Output Formats:

  • text -- Human-readable migration plan with ordered steps, forward SQL, rollback SQL, risk levels, and execution timeline
  • json -- Structured JSON with migration_id, steps (each with sql_forward, sql_rollback, validation_sql, risk_level, zero_downtime_phase), summary, execution_order, and rollback_order
  • sql -- Raw SQL output with forward migration statements, suitable for direct execution or piping into a database client

Related skills

open.feishu.cnLark BaseLet their coding agents read from, write to, and query structured data stored in Lark Base (Feishu) spreadsheets and databases.493kmicrosoftAzure KustoRun fast KQL queries against Azure Data Explorer for log analytics, telemetry, and time-series data.484k1.3klarksuiteLark SheetsCreate, read, update, and export spreadsheets inside Lark (Feishu) directly from their AI coding agent.389k15.9ksupabaseSupabase Postgres Best PracticesAutomatically apply Supabase Postgres performance rules during query writing, schema design, and database optimization.313k2.4kfirebaseFirebase Data ConnectGenerate complete, production-ready Firebase Data Connect GraphQL schemas and queries for complex relational data models.112k392firebaseFirebase FirestoreAdd Cloud Firestore read/write capabilities to an Android Kotlin app using the official Firebase SDK.77k392
Databasesdatabasespipelines
Browse by category
AI & AgentsFrontend & DesignBackend & DataDev ToolsAutomationTesting & ReviewSecurityMonitoring & CloudDocs & Planning
Design & frontend
Design systemsAnimation & motionAccessibility (a11y)CSS & TailwindUI designUXDesign to codeshadcn/uiLanding pagesBest design skills
Most searched skills
find-skillscavemanfrontend-designbrainstormingsystematic-debuggingstop-slopemil-design-engui-ux-pro-maxkarpathy-guidelinesweb-design-guidelinesreact-best-practicesgrill-meimprovemicrosoft-foundryagent-browser
Browse all listings
All agent skillsAll MCP serversAll marketplacesAll plugins

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.