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.
git clone --depth 1 https://github.com/neo4j-contrib/neo4j-skillsWrote 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.
[](https://agentmods.dev/plugins/neo4j-contrib/neo4j-skills/gemini-extension)<a href="https://agentmods.dev/plugins/neo4j-contrib/neo4j-skills/gemini-extension"><img src="https://agentmods.dev/badge/plugins/neo4j-contrib/neo4j-skills/gemini-extension/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.
<a href="https://agentmods.dev/plugins/neo4j-contrib/neo4j-skills/gemini-extension"><img src="https://agentmods.dev/badge/plugins/neo4j-contrib/neo4j-skills/gemini-extension.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
neo4j-skills 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 5d ago.
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.
What it actually says
{
"name": "neo4j-skills",
"version": "1.0.1",
"description": "Agent skills for Neo4j graph database \u2014 Cypher queries, graph modeling, drivers, imports, GraphRAG, GDS, vector indexes, and Aura provisioning.",
"contextFileName": "NEO4J.md",
"settings": [
{
"name": "Neo4j URI",
"description": "Bolt URI for the Neo4j instance (e.g. neo4j+s://<dbid>.databases.neo4j.io)",
"envVar": "NEO4J_URI"
},
{
"name": "Username",
"description": "Neo4j database username (default: neo4j or <dbid>)",
"envVar": "NEO4J_USERNAME"
},
{
"name": "Password",
"description": "Neo4j database password",
"envVar": "NEO4J_PASSWORD"
},
{
"name": "Database",
"description": "(Optional) Target database name (default: neo4j or <dbid>)",
"envVar": "NEO4J_DATABASE"
}
]
}
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.
- 5d ago First seen · 29 lines scan A 89d710b7362e
neo4j-skills is a plugin published in the GitHub repository neo4j-contrib/neo4j-skills (109 stars, last pushed 5d ago), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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 2026-09-07.
Other plugins, from other repositories
qdrant
Vector database collections and semantic search via direct calls to the Qdrant REST API.
skardi
Agent Skills for Skardi: build searchable context over your documents or your own datastore, answer questions from live data through a running server, and connect a property graph and query it through SQL. Hybrid search (vector + full-text) served over HTTP by skardi-server; every path is read-only unless you ask for…
skardi
Agent Skills for Skardi: build searchable context over your documents or your own datastore, answer questions from live data through a running server, and connect a property graph and query it through SQL.
agent-skills
Weaviate plugin for Claude Coding.
pinecone
Pinecone vector database integration for Cursor. Create and manage indexes, upsert data, and run semantic searches via the Pinecone MCP server. Build document Q&A assistants with citations, or get started fast with /pinecone-quickstart. Great for semantic search, RAG, and agentic AI apps.
turbopuffer
Work with turbopuffer, a serverless vector and full-text search database. Includes guidance for querying (vector ANN, BM25, hybrid), writing, and schema management, plus an MCP server for running code against your namespaces and searching turbopuffer documentation.