What is neo4j-knowledge-graph?

A guide for building and updating Neo4j knowledge graphs from tables and other structured data. Neo4j is a database that stores entities such as people or companies and the relationships between them.

Use it to profile CSV or Excel data, design labels and relationships, write Cypher queries, add uniqueness rules, migrate older Python integrations, or build GraphRAG and vector search workflows.

It helps turn rows and columns into a consistent graph model and choose between older and newer Neo4j connection methods or graph-based search.

neo4j-knowledge-graph — Skill for Codex by MazzaWill

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add MazzaWill/neo4j-python-pandas-py2neo-v3 --skill neo4j-knowledge-graph

Clone the repo

git clone --depth 1 https://github.com/MazzaWill/neo4j-python-pandas-py2neo-v3

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/mazzawill/neo4j-python-pandas-py2neo-v3/neo4j-knowledge-graph/github.svg)](https://agentmods.dev/skills/mazzawill/neo4j-python-pandas-py2neo-v3/neo4j-knowledge-graph)

Your own site

<a href="https://agentmods.dev/skills/mazzawill/neo4j-python-pandas-py2neo-v3/neo4j-knowledge-graph"><img src="https://agentmods.dev/badge/skills/mazzawill/neo4j-python-pandas-py2neo-v3/neo4j-knowledge-graph/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/mazzawill/neo4j-python-pandas-py2neo-v3/neo4j-knowledge-graph"><img src="https://agentmods.dev/badge/skills/mazzawill/neo4j-python-pandas-py2neo-v3/neo4j-knowledge-graph.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 727 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00055	$0.00727
Opus 5	$0.00028	$0.00364
Sonnet 5	$0.00011	$0.00145
Haiku 4.5	$0.00006	$0.00073

Measured 12d ago against content hash 5eaebccf460c, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

neo4j-knowledge-graph scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/profile_table.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/neo4j-knowledge-graph/SKILL.md · 67 lines

How it starts

The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

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.
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.
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.
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.
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.
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.

Read the full file on GitHub · 67 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

12d ago First seen · 67 lines · 55 tokens per session scan A 5eaebccf460c

Subscribe to this mod's changes

neo4j-knowledge-graph is a skill published in the GitHub repository MazzaWill/neo4j-python-pandas-py2neo-v3 (579 stars, last pushed 10d ago), licensed MIT. It adds 55 tokens to every session and 727 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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