arch-database

Purpose

This skill helps the AI agent advise on database architecture decisions, including selecting between relational, document, graph, and vector databases; designing schemas; optimizing indexing; and planning replication and sharding for scalable systems.

When to Use

Use this skill when architecting a new application backend, migrating databases, handling high-traffic data needs, or resolving performance issues. For example, choose it for e-commerce apps needing transactions (relational) versus social graphs (graph DBs). Avoid for low-level coding tasks like writing SQL queries; pair with query-focused skills instead.

Key Capabilities

• Compare database types: Evaluate relational (e.g., PostgreSQL) vs. document (e.g., MongoDB) based on data structure and queries.
• Design schemas: Generate ER diagrams or JSON schemas with constraints, e.g., defining primary keys and relationships.
• Optimize indexing: Recommend indexes like B-tree for relational or full-text for document DBs to reduce query times.
• Handle replication and sharding: Suggest setups like master-slave for fault tolerance or horizontal sharding for load balancing.
• Support polyglot persistence: Advise on mixing SQL and NoSQL in a system, e.g., using Redis for caching with PostgreSQL.

Usage Patterns

Invoke this skill via OpenClaw prompts prefixed with "arch-database:", e.g., "arch-database: compare relational and graph for a social network." For programmatic use, call the OpenClaw API endpoint /api/skills/arch-database with a JSON payload. Always include context like app requirements (e.g., read-heavy vs. write-heavy). If using in a script, wrap calls in error checks to handle API failures. For multi-step tasks, chain with other se-architecture skills, like starting with schema design then moving to indexing.