Install
$ agentstack add skill-neo4j-contrib-neo4j-skills-neo4j-modeling-skill ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
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Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
When to Use
- Designing graph model from scratch (domain → nodes, rels, props)
- Reviewing existing model for anti-patterns
- Deciding node vs property vs relationship vs label
- Migrating relational or document schema to graph
- Designing intermediate nodes for n-ary or complex relationships
- Detecting and mitigating supernode / high-fanout problems
- Choosing and creating constraints + indexes for a model
When NOT to Use
- Writing or optimizing Cypher →
neo4j-cypher-skill - Spring Data Neo4j (@Node, @Relationship) →
neo4j-spring-data-skill - GraphQL type definitions →
neo4j-graphql-skill - Importing data (LOAD CSV, APOC import) →
neo4j-import-skill
Inspect Before Designing
On existing database, run first — never propose changes without current state:
CALL db.schema.visualization() YIELD nodes, relationships RETURN nodes, relationships;
SHOW CONSTRAINTS YIELD name, type, labelsOrTypes, properties RETURN name, type, labelsOrTypes, properties;
SHOW INDEXES YIELD name, type, labelsOrTypes, state WHERE state = 'ONLINE' RETURN name, type, labelsOrTypes;
If APOC available:
CALL apoc.meta.schema() YIELD value RETURN value;
MCP tool map:
| Operation | Tool | |---|---| | Inspect schema | get-schema | | SHOW CONSTRAINTS, SHOW INDEXES | read-cypher | | CREATE CONSTRAINT ... IF NOT EXISTS | write-cypher (show + confirm first) |
Defaults — Apply to Every Model
- Use-case first — list 5+ queries the model must answer before designing
- Nodes = entities (nouns) with identity; rels = connections (verbs) with direction
- Labels PascalCase; rel types SCREAMINGSNAKECASE; properties camelCase
- Every node type used in MERGE has a uniqueness constraint on its key property
- Add property type constraints (
REQUIRE n.prop IS :: STRING) where the type is known — helps the query planner and catches bad writes early - No generic labels (
:Entity,:Node,:Thing); no generic rel types (:RELATED_TO,:HAS) - Security labels (used for row-level access control) should start with a common prefix (e.g.
Sec) so application code can reliably filter them out of the domain schema - Rel direction encodes semantic meaning — not arbitrary
- Inspect schema before proposing any change on an existing database
- All constraint/index DDL uses
IF NOT EXISTS— safe to rerun - On Neo4j 2026.02+ (Enterprise/Aura): consider
ALTER CURRENT GRAPH TYPE SET { … }orEXTEND GRAPH TYPE WITH { … }to declare the full model in one block instead of individualCREATE CONSTRAINTstatements — seeneo4j-cypher-skill/references/graph-type.md. PREVIEW — syntax may change before GA.
Key Patterns
Node vs Relationship vs Property — Decision Table
| Question | Answer | Model as | |---|---|---| | Is it a thing with identity, queried as entry point? | Yes | Node | | Is it a connection between two things with direction? | Yes | Relationship | | Does the connection have its own properties or multiple targets? | Yes | Intermediate node | | Is it a scalar always returned with its parent, never filtered alone? | Yes | Property on parent | | Is it a category used for type-based filtering or path traversal? | Yes | Label (not a property) | | Does the same attribute value repeat across many nodes (low cardinality)? | Yes | Label, not a property node | | Is it a fact connecting >2 entities? | Yes | Intermediate node |
Property vs Label — Decision Table
| Use label when | Use property when | |---|---| | Values are few, fixed, used as traversal filters (WHERE n:Active) | Values are many, dynamic, or unique per node | | You traverse by type (MATCH (n:VIPCustomer)) | You filter by value (WHERE n.tier = 'vip') | | Category drives index selection | Fine-grained value drives range scans | | Example: :Active, :Verified, :Premium | Example: status, score, email |
Rule: adding a label is cheap; scanning all :Label nodes is fast. Never model high-cardinality values as labels.
Intermediate Node Pattern
Use when a relationship needs its own properties, connects >2 entities, or is independently queryable.
Before (relationship with property — limited):
(Person)-[:ACTED_IN {role: "Neo"}]->(Movie)
// Cannot query roles independent of movies
After (intermediate node — queryable, extensible):
(Person)-[:PLAYED]->(Role {name: "Neo"})-[:IN]->(Movie)
// MATCH (r:Role) WHERE r.name STARTS WITH 'Neo' RETURN r
Employment overlap example:
// Find colleagues who overlapped at same company
MATCH (p1:Person)-[:WORKED_AT]->(e1:Employment)-[:AT]->(c:Company) p2
AND e1.startDate 2 properties
- Relationship is the subject of another query
- Multiple entities share the same connection context
- You need to connect >2 entities in one fact
---
### Relational → Graph Migration Table
| Relational construct | Graph equivalent | Notes |
|---|---|---|
| Table row | Node | One label per table (add more as needed) |
| Column (scalar) | Node property | |
| Primary key | Uniqueness constraint property | Use `tmdbId`, not `id` (too generic) |
| Foreign key | Relationship | Direction: from dependent → referenced |
| Many-to-many junction table | Intermediate node | Especially if junction has own columns |
| Junction table (no own columns) | Direct relationship | Simpler; upgrade to intermediate node later |
| NULL FK (optional relation) | Absent relationship | No node created; absence is the signal |
| Polymorphic FK (Rails-style) | Multiple labels or relationship types | Split into type-specific rels |
| Self-referential FK | Same-label relationship | `:Employee {managerId}` → `(e)-[:REPORTS_TO]->(m)` |
| Audit/history columns | Intermediate versioning node | See References for versioning pattern |
---
### Supernode Detection and Mitigation
**Detect:**
```cypher
// Find top-10 highest-degree nodes
MATCH (n)
RETURN labels(n) AS labels, elementId(n) AS id, count{ (n)--() } AS degree
ORDER BY degree DESC LIMIT 10
Node with degree >> median for its label = supernode candidate. Any node with >100K relationships will degrade traversal queries that pass through it.
Causes:
- Domain supernodes: airports, celebrities, popular hashtags — unavoidable
- Modeling supernodes: gender, country, status modeled as nodes with millions of edges — avoidable
Mitigation strategies (in priority order):
| Strategy | When to use | Implementation | |---|---|---| | Query direction | Directional asymmetry exists | Query from low-degree side; exploit direction | | Relationship type split | Supernode serves multiple roles | :FOLLOWS + :FAN instead of single :RELATED_TO | | Label segregation | Supernode conflates entity types | :Celebrity vs :User → query only relevant subtype | | Bucket pattern | Time-series or high-volume event nodes | See below | | Avoid modeling | Low-cardinality categoricals | Use label instead of node (:Active not (:Status {name:"Active"})) | | Join hint | Query tuning last resort | USING JOIN ON n in Cypher |
Bucket pattern (time-series / high-volume):
// Instead of: (:User)-[:VIEWED]->(:Page) (millions of rels per user)
// Bucket by hour:
(u:User)-[:VIEWED_IN]->(b:ViewBucket {userId: u.id, hour: '2025-04-28T14'})-[:VIEWED]->(p:Page)
// Query last hour's views without traversing full history:
MATCH (u:User {id: $uid})-[:VIEWED_IN]->(b:ViewBucket {hour: $hour})-[:VIEWED]->(p)
RETURN p.url
Naming Conventions
| Element | Convention | Good | Bad | |---|---|---|---| | Node label | PascalCase, singular noun | :Person, :BlogPost | :person, :blog_posts, :Entity | | Relationship type | SCREAMINGSNAKECASE, verb phrase | :ACTED_IN, :WORKS_FOR | :actedin, :relatedTo, :HAS | | Property key | camelCase | firstName, createdAt | FirstName, first_name | | Constraint name | snakecase descriptive | person_id_unique | constraint1 | | Index name | snakecase descriptive | person_name_idx | index2 |
Schema Enforcement — What to Create for Each Element
Run all DDL with IF NOT EXISTS. Apply before importing data.
// 1. Uniqueness constraint — every node type used in MERGE
CREATE CONSTRAINT person_id_unique IF NOT EXISTS
FOR (p:Person) REQUIRE p.id IS UNIQUE;
// 2. Existence constraint (Enterprise) — mandatory properties
CREATE CONSTRAINT person_name_exists IF NOT EXISTS
FOR (p:Person) REQUIRE p.name IS NOT NULL;
// 3. Property type constraint (Enterprise) — enforce data type
CREATE CONSTRAINT person_born_integer IF NOT EXISTS
FOR (p:Person) REQUIRE p.born IS :: INTEGER;
// 4. Key constraint (Enterprise) — unique + exists in one
CREATE CONSTRAINT movie_tmdbid_key IF NOT EXISTS
FOR (m:Movie) REQUIRE m.tmdbId IS NODE KEY;
// 5. Range index — equality and range filters on properties
CREATE INDEX person_name_idx IF NOT EXISTS
FOR (p:Person) ON (p.name);
// 6. Fulltext index — CONTAINS, STARTS WITH, free text search
CREATE FULLTEXT INDEX person_fulltext IF NOT EXISTS
FOR (n:Person) ON EACH [n.name, n.bio];
// 7. Vector index — embedding similarity search
CREATE VECTOR INDEX chunk_embedding_idx IF NOT EXISTS
FOR (c:Chunk) ON (c.embedding)
OPTIONS { indexConfig: { `vector.dimensions`: 1536, `vector.similarity_function`: 'cosine' } };
// 8. Relationship index — filter on rel properties
CREATE INDEX acted_in_year_idx IF NOT EXISTS
FOR ()-[r:ACTED_IN]-() ON (r.year);
After creating indexes, poll until ONLINE:
SHOW INDEXES YIELD name, state WHERE state <> 'ONLINE' RETURN name, state;
Do NOT use an index until state = ONLINE.
Vector / Embedding Property Modeling
Store embeddings on dedicated :Chunk nodes, never on business nodes:
(:Document)-[:HAS_CHUNK]->(c:Chunk {text: "...", embedding: [...]})
Rules:
- Chunk node:
text(source text),embedding(float array),chunkIndex(int) - Parent document: metadata only (title, url, createdAt)
- Vector index on
c.embeddingonly - Chunk size 200–500 tokens with 20% overlap is production default [field]
- Do NOT put embedding on
:Document— makes the node too large and pollutes traversal
Anti-Patterns Table
| Anti-pattern | Problem | Fix | |---|---|---| | Generic labels :Entity, :Node | No filtering benefit; all nodes scan | Use domain labels :Person, :Product | | Generic rel types :RELATED_TO, :HAS | Can't filter by relationship type | Use semantic types :PURCHASED, :AUTHORED | | Low-cardinality value as node | Supernode (:Status {name:"active"} → millions of edges) | Use label :Active instead | | Property as label (n.type = 'VIP' + :VIP label both exist) | Inconsistency, duplication | Pick one; prefer label if used in traversal | | Storing embeddings on business node | Node bloat, slow traversal | Dedicated :Chunk node | | MERGE without uniqueness constraint | Duplicate nodes silently created | Add constraint before any MERGE | | Missing relationship direction meaning | Arbitrary direction; confusing model | Direction = semantic flow of action | | Junction table modeled as bare property | Loses history and extensibility | Intermediate node with its own properties | | id as property name | id(n) is a deprecated Cypher function (use elementId(n)); bare id is fine as a property name in practice, but domain-qualified names (personId, movieId) are clearer and avoid any future ambiguity | Prefer personId, movieId, tmdbId where it aids readability | | All dates as strings | No range queries; no temporal operators | Use Neo4j date() or datetime() type |
Output Format — Schema Assessment
When reviewing an existing model:
## Schema Assessment
### Compliant
- [constraint / pattern that is correct]
### Issues Found
#### [Title] — Severity: ERROR / WARNING / INFO
- **Current**: what the model does
- **Problem**: why it is an issue
- **Fix**: specific Cypher DDL or model change
## Recommended Schema
### Node Labels
- :Label {key: TYPE, prop: TYPE, ...} → constraints: [list]
### Relationships
- (:LabelA)-[:TYPE {prop: TYPE}]->(:LabelB)
### Constraints to Create
[CREATE CONSTRAINT ... statements]
### Indexes to Create
[CREATE INDEX ... statements]
Severity semantics:
| Severity | Meaning | Action | |---|---|---| | ERROR | Model correctness failure (duplicates possible, data loss risk) | Stop; fix before proceeding | | WARNING | Performance or extensibility risk | Report; ask user before proceeding | | INFO | Style or convention deviation | Surface; continue |
Provenance Labels
[official]— stated directly in Neo4j docs[derived]— follows from documented behavior[field]— community heuristic; treat as default but validate
Checklist
- [ ] Use cases (≥5 queries) defined before modeling
- [ ] Schema inspected on existing database before changes proposed
- [ ] Every MERGE-target node label has a uniqueness constraint
- [ ] No generic labels (
:Entity,:Node,:Thing) - [ ] No generic relationship types (
:RELATED_TO,:HAS,:CONNECTED_TO) - [ ] Relationship direction encodes semantic meaning
- [ ] N-ary or propertied relationships use intermediate nodes
- [ ] High-cardinality values stored as properties, not nodes
- [ ] Low-cardinality categoricals used as labels, not property nodes
- [ ] Embeddings on dedicated
:Chunknodes, not business nodes - [ ] Supernode candidates identified and mitigated
- [ ] All DDL uses
IF NOT EXISTS - [ ] Indexes polled to ONLINE before use
- [ ] Assessment output follows the structured format above
- [ ] Every prohibition paired with a concrete fix
References
Load on demand:
- [references/modeling-patterns.md](references/modeling-patterns.md) — time-series, versioning, multi-tenancy, linked list, access control patterns
- Neo4j Data Modeling Guide
- Neo4j Modeling Tips
- GraphAcademy: Graph Data Modeling Fundamentals
- Super Nodes — All About Super Nodes (David Allen)
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: neo4j-contrib
- Source: neo4j-contrib/neo4j-skills
- License: MIT
- Homepage: https://neo4j.com/llms.txt
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.