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Using Graph Databases

Checked
ancoleman·vlatest·database·MIT

This skill leverages graph databases to analyze interconnected data, revealing relationships and insights missed by traditional methods for improved understanding.

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 ancoleman/ai-design-components --skill using-graph-databases -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

ancoleman

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 ancoleman/ai-design-components at 76551b7b19.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
76551b7b19
Manifest
skills/using-graph-databases/SKILL.md
Scanned
10 files, 4,427 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 interact with and query graph databases. It facilitates the retrieval of relationships between data points, allowing for complex analysis beyond traditional relational database capabilities. The agent can execute queries, interpret results, and present insights derived from connected data.

When to use it

  • Recommendation Engines: Identify users or products with similar attributes based on connections within a graph database representing user behavior and product features.
  • Fraud Detection: Analyze transaction networks to identify suspicious patterns and potential fraudulent activities by exploring relationships between accounts, transactions, and devices.
  • Knowledge Graph Exploration: Traverse a knowledge graph to discover facts and relationships about entities (people, places, concepts) and answer complex questions.
  • Supply Chain Analysis: Map out the flow of goods and materials through a supply chain network to identify bottlenecks or vulnerabilities.

Key capabilities

  • Graph database query execution
  • Relationship traversal
  • Data interpretation from graph structures
  • Insight presentation based on connected data

Example prompts

  • "Find all customers who purchased products also bought by customer X."
  • "Show me the connections between these two entities in the knowledge graph: 'Albert Einstein' and 'Theory of Relativity'."
  • "Identify potential fraud rings based on transaction patterns within the financial network."

Tips & gotchas

The agent requires access to a configured graph database instance (e.g., Neo4j, Amazon Neptune) with appropriate permissions. Complex queries may require careful prompt engineering to ensure accurate and efficient results.