graph-database-expert_skill

This skill helps you design scalable graph schemas, optimize traversals, and implement robust relationships using SurrealDB and graph patterns.

Shell

25

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

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Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill martinholovsky/claude-skills-generator --skill graph-database-expert

SKILL.md36.3 KB

Overview

This skill is an expert consultant for graph database design and development, with deep practical knowledge of modeling, traversals, query optimization, and relationship patterns. It specializes in SurrealDB concepts and SurrealQL while applying generic graph database principles to produce intuitive, performant, and scalable schemas and queries.

How this skill works

I inspect data access patterns and existing schemas to propose node/edge models, typed relationships, and indexing strategies. I analyze query plans and traversals to recommend bounded traversals, appropriate algorithms (BFS/DFS/shortest-path), and parameterized queries. I also provide TDD-style tests, sample SurrealQL snippets, and operational guidance for caching, pagination, and schema evolution.

When to use it

Designing or refactoring a graph schema for connected data (social, knowledge graph, ACLs)
Optimizing slow traversals, reducing query latency, or diagnosing execution plans
Implementing complex relationships (weighted edges, temporal edges, hierarchies)
Building recommendation engines, fraud detection, or network topology features
Integrating SurrealDB multi-model features (documents + graph) while preserving graph semantics

Best practices

Model based on access patterns: optimize relationships and direction for frequent traversals
Always set traversal depth limits and paginate large result sets to prevent runaway queries
Create indexes on frequently queried node properties and edge properties used in filters
Use parameterized queries and row-level security; validate inputs to avoid injection
Adopt TDD for queries and traversals: write tests for cycles, depth limits, and edge cases
Plan schema evolution: version relationships, use soft deletes, and document changes

Example use cases

Design a follower/following model with bidirectional queries and depth-bounded discovery
Implement a recommendation graph using weighted edges and filters on weight thresholds
Build shortest-path or multi-hop queries for routing or dependency resolution
Optimize SurrealDB queries by adding indexes, using explain plans, and reducing Cartesian products
Model temporal relationships with valid_from/valid_to fields and queries constrained by time

FAQ

Use a graph DB when your queries require frequent multi-hop traversals or when relationships are first-class and drive application behavior. For simple CRUD or heavy OLAP aggregation, prefer relational or specialized stores.

How do I prevent traversal queries from degrading performance?

Set practical depth limits, add indexes on filtered properties, filter early in traversals, paginate results, and profile queries with explain plans.

Can I mix document and graph models in SurrealDB?

Yes. SurrealDB supports multi-model use: keep node properties as documents and use RELATE/links for graph relationships, choosing embedding vs linking based on access patterns.