neo4j_expert

Store and query Croissant datasets in a Neo4j Graph Database for relational discovery and semantic search.

7 stars

Best use case

neo4j_expert is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Store and query Croissant datasets in a Neo4j Graph Database for relational discovery and semantic search.

Teams using neo4j_expert should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/neo4j_expert/SKILL.md --create-dirs "https://raw.githubusercontent.com/codata/croissant-toolkit/main/skills/neo4j_expert/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/neo4j_expert/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How neo4j_expert Compares

Feature / Agentneo4j_expertStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Store and query Croissant datasets in a Neo4j Graph Database for relational discovery and semantic search.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 🕸️ Neo4j Expert Skill

The Neo4j Expert skill transforms hierarchical Croissant JSON-LD files into a knowledge graph. This allows users to discover relationships between datasets, creators, locations, and keywords using powerful graph queries.

## Features
- **Knowledge Graph Ingestion**: Automatically maps Croissant structures to Dataset, Person, Place, and Keyword nodes.
- **Natural Language Querying**: Ask questions in plain English (e.g., "Who created datasets about Ukraine?") and the and get answers powered by Cypher + Gemini 3.
- **Relational Discovery**: Find datasets that share creators or locations.

## Configuration
Set your Neo4j credentials in the environment:
```bash
export NEO4J_URI="bolt://your-neo4j-instance:7687"
export NEO4J_USER="neo4j"
export NEO4J_PASSWORD="your-strong-password"
```

## Usage

### 1. Ingest a Croissant File
```bash
python3 skills/neo4j_expert/scripts/ingest.py "./data/croissant/my_dataset.jsonld"
```

### 2. Query the Knowledge Graph
```bash
python3 skills/neo4j_expert/scripts/query.py "Which datasets mention Kyiv?"
```

## Graph Schema
- **Nodes**: `Dataset`, `Person`, `Organization`, `Place`, `Keyword`, `FileObject`.
- **Relationships**: 
    - `(:Person/Organization)-[:CREATOR_OF]->(:Dataset)`
    - `(:Dataset)-[:SPATIAL_COVERAGE]->(:Place)`
    - `(:Dataset)-[:HAS_KEYWORD]->(:Keyword)`
    - `(:Dataset)-[:HAS_DISTRIBUTION]->(:FileObject)`

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