Claude-skill-registry db-architect

Expert database architecture including schema design, partitioning, replication, and performance optimization

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/db-architect" ~/.claude/skills/majiayu000-claude-skill-registry-db-architect && rm -rf "$T"
manifest: skills/data/db-architect/SKILL.md
source content

Database Architect

Purpose

Design optimal database architectures including schema design, partitioning strategies, replication, and performance optimization.

Activation Keywords

  • database architecture, schema design
  • partitioning, sharding
  • replication, clustering
  • normalization, denormalization
  • data modeling

Core Capabilities

1. Schema Design

  • Normalization (1NF-BCNF)
  • Denormalization strategies
  • Temporal data patterns
  • Multi-tenant design
  • Audit trail design

2. Partitioning

  • Horizontal partitioning (sharding)
  • Vertical partitioning
  • Partition keys selection
  • Cross-partition queries
  • Rebalancing strategies

3. Replication

  • Master-slave replication
  • Multi-master replication
  • Synchronous vs async
  • Conflict resolution
  • Read replicas

4. Database Selection

Use CaseRecommended
TransactionsPostgreSQL, MySQL
DocumentsMongoDB, CouchDB
Time-seriesTimescaleDB, InfluxDB
GraphNeo4j, Neptune
Key-ValueRedis, DynamoDB
SearchElasticsearch

5. Performance

  • Index design
  • Query optimization
  • Connection pooling
  • Caching integration
  • Archival strategies

Design Process

1. Data Analysis
   → Entity identification
   → Relationships
   → Access patterns
   → Volume estimates

2. Logical Design
   → ERD creation
   → Normalization
   → Constraint definition

3. Physical Design
   → Table structures
   → Index strategies
   → Partition plan

4. Optimization
   → Query analysis
   → Index tuning
   → Denormalization decisions

Architecture Patterns

Multi-Tenant

-- Schema per tenant
CREATE SCHEMA tenant_123;

-- Shared schema with tenant_id
CREATE TABLE orders (
    id BIGSERIAL PRIMARY KEY,
    tenant_id INT NOT NULL,
    -- Row-level security
    CONSTRAINT tenant_isolation
        CHECK (tenant_id = current_setting('app.tenant_id')::INT)
);

Partitioning

-- Range partitioning by date
CREATE TABLE events (
    id BIGSERIAL,
    event_time TIMESTAMP,
    data JSONB
) PARTITION BY RANGE (event_time);

CREATE TABLE events_2024
    PARTITION OF events
    FOR VALUES FROM ('2024-01-01') TO ('2025-01-01');

Example Usage

User: "Design database for a social media platform"

DB Architect Response:
1. Entities: users, posts, comments, likes, follows
2. Schema design with proper relationships
3. Partitioning strategy for posts (by user_id)
4. Index strategy for feed queries
5. Caching strategy for popular content
6. Archival plan for old data