CtrlK
BlogDocsLog inGet started
Tessl Logo

neo4j-vector-index-skill

Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity function and embedding provider dimensions, and batch-update embeddings. Use when tasks involve CREATE VECTOR INDEX, vector.dimensions, cosine/euclidean search, embedding ingestion pipelines, or semantic nearest-neighbor lookup. Does NOT handle GraphRAG retrieval_query graph traversal — use neo4j-graphrag-skill. Does NOT handle fulltext/keyword indexes (FULLTEXT INDEX, db.index.fulltext) — use neo4j-cypher-skill. Does NOT handle GDS graph embeddings (FastRP, Node2Vec) — use neo4j-gds-skill.

92

1.58x
Quality

88%

Does it follow best practices?

Impact

100%

1.58x

Average score across 3 eval scenarios

SecuritybySnyk

Advisory

Suggest reviewing before use

SKILL.md
Quality
Evals
Security

Evaluation results

100%

25%

Semantic Article Search — Embedding Ingestion Pipeline

Embedding ingestion pipeline with correct ordering and batch pattern

Criteria
Without context
With context

CYPHER 25 prefix

0%

100%

IF NOT EXISTS

100%

100%

Correct dimensions

100%

100%

Cosine similarity

100%

100%

Index before ingest

100%

100%

Poll until ONLINE

80%

100%

FAILED state handled

0%

100%

Batch UNWIND pattern

100%

100%

Batch size 500

0%

100%

Dimension assertion

100%

100%

Expected dim constant

100%

100%

Neo4j driver used

100%

100%

100%

36%

Knowledge Base Semantic Search — Version-Aware Query Layer

Version-aware vector search with SEARCH vs queryNodes branching

Criteria
Without context
With context

Pre-flight version query

100%

100%

SEARCH clause for 2026.01+

0%

100%

queryNodes for 2025.x

100%

100%

Version branch logic

40%

100%

No literal API key

100%

100%

Consistent embedding model

100%

100%

Literal index name

0%

100%

CYPHER 25 prefix

0%

100%

Score returned

100%

100%

Results ordered by score

100%

100%

100%

48%

Product Catalog Semantic Search with Category Filtering

Filterable vector index with WITH declarations and in-index SEARCH WHERE

Criteria
Without context
With context

CYPHER 25 prefix

0%

100%

IF NOT EXISTS

100%

100%

Correct dimensions

100%

100%

Cosine similarity function

100%

100%

WITH property declarations

0%

100%

Only scalar types in WITH

50%

100%

SEARCH clause used

0%

100%

In-index WHERE filters

20%

100%

Literal index name in SEARCH

42%

100%

In-index filter strategy explained

62%

100%

Two constraints documented

100%

100%

Results ordered by score

100%

100%

Repository
neo4j-contrib/neo4j-skills
Evaluated
Agent
Claude Code
Model
Claude Sonnet 4.6

Table of Contents

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.