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neo4j-knowledge-graph

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Use when designing, importing, querying, or modernizing Neo4j knowledge graphs from CSV, Excel, pandas, Cypher, py2neo, the official neo4j Python driver, vector indexes, or GraphRAG workflows.

QUICK START

How to use this skill

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  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
Prompt to paste
I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/MazzaWill/neo4j-python-pandas-py2neo-v3/blob/HEAD/skills/neo4j-knowledge-graph/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/neo4j-knowledge-graph/. Do not write files or run scripts until I approve.

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

Neo4j Knowledge Graph

Overview

Use this skill to turn tabular or semi-structured data into a Neo4j knowledge graph, then choose the right path: legacy py2neo compatibility, modern official-driver Cypher, or GraphRAG/vector search.

Workflow

  1. Frame the graph task

    • Identify the source data: CSV, Excel, pandas DataFrame, database export, API data, or existing Neo4j graph.
    • Ask what the user wants to do: import, model, query, migrate, visualize, or add GraphRAG.
    • Confirm Neo4j target: local Neo4j, Aura, legacy Neo4j 3.x, Neo4j 5+, or unknown.

  2. Profile data before modeling

    • For CSV/Excel files, run scripts/profile_table.py <path> when local files are available.
    • Inspect columns, sample values, blank counts, likely identifiers, and repeated values.
    • Do not infer graph labels from one row only.

  3. Model the graph

    • Use nouns for labels: Invoice, Person, Company, Product, Location.
    • Use verbs or role phrases for relationships: ISSUED_BY, PAID_TO, HAS_PARTICIPANT.
    • Choose stable IDs before writing Cypher.
    • Put frequently queried identifiers under uniqueness constraints.
    • Keep relationship properties for roles, timestamps, source rows, and confidence.

  4. Generate safe Cypher

    • Prefer parameterized MERGE + UNWIND for imports.
    • Create constraints before loading data.
    • Avoid destructive commands unless the user explicitly asks and the target is confirmed.
    • For modern projects, prefer the official neo4j Python driver.
    • Use py2neo only when maintaining legacy Neo4j 3.x / py2neo v3 code.

  5. Add GraphRAG only when useful

    • Use vector search when users need semantic retrieval, fuzzy matching, natural-language search, or RAG.
    • Build a clear search_text from graph facts.
    • Create a Neo4j vector index on the embedding property.
    • Use deterministic embeddings only for demos/tests; use neo4j-graphrag or a real embedding provider for production.

  6. Verify

    • Dry-run on a small sample before loading the full dataset.
    • Report node/relationship counts, constraints, and index status.
    • Show representative Cypher queries users can run in Neo4j Browser.

Resource Guide

  • Read references/modeling.md for spreadsheet-to-graph modeling patterns.
  • Read references/cypher-and-graphrag.md before writing import Cypher, vector index Cypher, or GraphRAG examples.
  • Run scripts/profile_table.py --help for table profiling options.

Output Shape

For a substantial task, return:

  • graph model summary
  • column-to-label/relationship mapping
  • constraints and indexes
  • import or migration steps
  • verification queries
  • risks and assumptions

For code changes, include tests or a dry-run path that does not require a live Neo4j instance.