neo4j_expert
Store and query Croissant datasets in a Neo4j Graph Database for relational discovery and semantic search.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/neo4j_expert/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How neo4j_expert Compares
| Feature / Agent | neo4j_expert | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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)`
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