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neo4j-graph-data

Explore Compliance Theater Neo4j graphs for case-file evidence and relationships.

2|Updated Jan 30, 2025
Search Tags:#vector search#neo4j#cypher#graph traversal#evidence linking#case theory#email threads
One-click install
npx skills add https://github.com/seanmobrien/we-dont-need-no-education --skill neo4j-graph-data
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: neo4j-graph-data
Source: https://github.com/seanmobrien/we-dont-need-no-education/tree/main/codex-mcp/src/skills/neo4j-graph-data
Command: npx skills add https://github.com/seanmobrien/we-dont-need-no-education --skill neo4j-graph-data

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It helps users understand and navigate a Compliance Theater Neo4j knowledge graph to answer case-file, policy, evidence, and relationship questions without writing blind Cypher or guessing where information lives.

Core Features & Use Cases

  • Graph-schema-first guidance: Uses the graph schema reference to safely plan Cypher that matches the live data model.
  • Read-only evidence exploration: Explores case-file documents, actors, email threads, attachments, and compliance artifacts using graph_read patterns, keeping mutations out unless explicitly requested.
  • Named case-theory retrieval: Resolves a user’s theory phrasing to the correct case_theory.theory_key (e.g., improper_records_supporting_correction_denial) and returns ordered evidence with role and notes.
  • Relationship and traversal reasoning: Traverses evidence relationships (threads, replies, attachments, policy references, subject-matter links) to explain how documents connect.
  • Embedding-aware discovery: Performs vector search using the graph’s embedding indexes (e.g., case_file_document_embedding) to find relevant nodes by meaning while avoiding manual vector handling.

Quick Start

Ask: "What graph data exists for this case-file and how is it connected to the named theory improper records supporting correction denial?"

Frequently Asked Questions about neo4j-graph-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I query a Neo4j evidence graph to find documents linked to a specific case theory?▼

To query a Neo4j evidence graph for case theories, resolve your phrasing to the exact case_theory.theory_key, then use read-only graph_read patterns to retrieve ordered evidence with roles and notes. This requires exact case-file IDs and theory_key normalization.

What is the safest way to explore Neo4j relationships between email threads and compliance policies?▼

The safest way to explore Neo4j relationships is schema-first planning via graph_schema, followed by read-only graph_read exploration to traverse email threads, attachments, and policy references without risking data mutations.

Can I use vector search to find case-file documents in Neo4j without manually generating embeddings?▼

Yes, you can use vector search to find case-file documents by querying the graph's embedding indexes using vectorParams with the small model default, which performs embedding-aware discovery by meaning while avoiding manual vector handling.

How does graph traversal connect actors and subject-matter concepts in a compliance case file?▼

Graph traversal connects actors and subject-matter concepts by traversing evidence relationships across the Neo4j graph, explaining how case-file documents link to people, policies, and compliance artifacts through structured relationship paths.

Do I need to know Cypher to find evidence links across actors and policy references in Neo4j?▼

You do not need to write blind Cypher to find evidence links; the system provides graph_read patterns and schema-first guidance to safely explore how actors and policy references connect without guessing query syntax.

What are the limitations when running vector search queries for case theory anchors in Neo4j?▼

Limitations include the requirement to use vectorParams with the small model default for embedding queries and the need for theory_key normalization, meaning exact case-file IDs must be used to avoid mismatched theory anchors.

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