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Query Neo4j knowledge graph for entities, relationships, and graph analysis.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Basierend auf der SOC-Berufsklassifikation
Build and deploy the realtime-api Docker container with full verification that deployed code matches local source.
Speak text aloud using the Magpie TTS container in the realtime-api Docker stack. Zero external dependencies — just curl + aplay.
Run TTS round-trip tests — pure-logic unit tests and optional integration tests against live TTS/STT services.
Unified memory management for notes, knowledge graph, RAG search, and file analysis. Use when working with: (1) Core memory — protected identity, projects, relationships, and system facts that should never be forgotten, (2) Working notes — per-session ephemeral notes organized by section, (3) MongoDB RAG — vector-search-enabled notes with importance scoring, decay, deduplication, and archival, (4) Neo4j knowledge graph — entities, relationships, merge duplicates, reinforce mentions, Cypher queries, (5) File analysis — deep file reading that extracts knowledge into all memory layers, (6) Service initialization — health-check, start/stop MongoDB, Neo4j, TEI embeddings via docker-compose with partial setup support.
Query MongoDB notes store for memory analysis and statistics.
Calls qq agent from cli.
| name | neo4j-query |
| description | Query Neo4j knowledge graph for entities, relationships, and graph analysis. |
| triggers | ["neo4j","knowledge graph","entities","relationships","graph query","cypher"] |
Query the Neo4j knowledge graph to investigate entities, relationships, and graph structure.
bolt://localhost:7687 (or NEO4J_URI env var)neo4j (or NEO4J_USER env var)refinerypass (or NEO4J_PASSWORD env var)from qq.knowledge.neo4j_client import Neo4jClient
# Initialize client
client = Neo4jClient()
# Execute raw Cypher query
results = client.execute("MATCH (n) RETURN n LIMIT 10")
# Get graph summary (entity and relationship counts by type)
summary = client.get_graph_summary()
# Get entity by name
entity = client.get_entity("entity_name")
# Get related entities (up to N hops)
related = client.get_related_entities("entity_name", depth=2, limit=20)
# Close when done
client.close()
-- Total entities
MATCH (n) RETURN count(n) as total_entities
-- Entities by type
MATCH (n) RETURN labels(n)[0] as type, count(n) as count ORDER BY count DESC
-- Total relationships
MATCH ()-[r]->() RETURN count(r) as total_relationships
-- Relationships by type
MATCH ()-[r]->() RETURN type(r) as type, count(r) as count ORDER BY count DESC
-- Entities with no incoming or outgoing relationships
MATCH (n) WHERE NOT (n)--() RETURN n.name, labels(n)[0] as type
-- Entities with most connections
MATCH (n)-[r]-() RETURN n.name, labels(n)[0] as type, count(r) as connections ORDER BY connections DESC LIMIT 10
-- Distribution of relationship counts per entity
MATCH (n)
OPTIONAL MATCH (n)-[r]-()
WITH n, count(r) as rel_count
RETURN rel_count, count(n) as entities_with_this_count ORDER BY rel_count
-- Sample entities with their properties
MATCH (n) RETURN n.name, labels(n)[0] as type, n.description LIMIT 20
-- Sample relationships with endpoints
MATCH (a)-[r]->(b) RETURN a.name, type(r), b.name LIMIT 20
# Use cypher-shell directly
docker exec -it qq-neo4j-1 cypher-shell -u neo4j -p refinerypass "MATCH (n) RETURN count(n)"