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Graph Database Expert

Checked
martinholovsky·vlatest·database·Unlicense

Analyzes and optimizes graph databases like Neo4j, identifying bottlenecks and suggesting performance improvements using expert techniques.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add martinholovsky/claude-skills-generator --skill graph-database-expert -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

martinholovsky

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned martinholovsky/claude-skills-generator at 1086ef2567.

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Commit
1086ef2567
Manifest
skills/graph-database-expert/SKILL.md
Scanned
3 files, 2,748 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to analyze and optimize graph databases like Neo4j, SurrealDB, ArangoDB, Amazon Neptune, and JanusGraph. It leverages expert knowledge of graph theory, modeling, query languages (SurrealQL, Cypher, Gremlin, SPARQL), and traversal techniques to identify bottlenecks and suggest performance improvements. The skill focuses on designing intuitive, performant, flexible, scalable, and multi-model graph databases.

When to use it

  • Analyzing social networks (friends, followers, connections) for patterns or insights.
  • Designing knowledge graphs representing entities, concepts, and their relationships.
  • Building recommendation engines based on user preferences and similar items.
  • Detecting fraudulent activities by analyzing transaction patterns and network analysis.

Key capabilities

  • Expertise in graph theory (nodes, edges, paths, cycles).
  • Graph modeling techniques including entity-relationship mapping and schema design.
  • Proficiency in query languages: SurrealQL, Cypher, Gremlin, SPARQL.
  • Understanding of graph traversals like depth-first, breadth-first, and shortest path.
  • Knowledge of indexing strategies and query optimization for performance.
  • Experience with multi-model databases (graph + documents, time-series, key-value).
  • Principles of Test Driven Development (TDD) for graph queries.

Example prompts

  • "Analyze this Cypher query and suggest optimizations to improve its performance."
  • "Design a graph database schema for representing a social network with users, posts, and comments."
  • "What are the best indexing strategies for frequently traversed relationships in Neo4j?"
  • “Write tests for this SurrealQL query before implementation.”

Tips & gotchas

  • The skill is rated as MEDIUM risk level due to potential impact on data modeling and query performance.
  • Always profile queries with explain plans to identify bottlenecks.
  • Set depth limits on traversals to prevent unbounded queries and excessive resource usage.