Skip to content
cccskills
FreePublished on GitHub

agentdb-cypher

Execute Cypher queries against AgentDB's graph backend. Use when the user wants to write a custom traversal that the standard tools don't cover, or when explaining graph state.

The English summary is being prepared. Showing the original description.

See how to install

Files included (1)

  • SKILL.md1.9 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Cypher Query

Run arbitrary Cypher against the AgentDB graph backend. The native binding accepts standard openCypher; AgentDB also adds a few extensions (uplift, confidence properties on edges).

When to use

  • Need a query the standard agentdb_causal_* tools don't cover
  • Multi-hop traversal with custom filtering
  • Aggregation queries — count nodes by type, find hub nodes, etc.
  • Migration / inspection during debugging

API

agentdb_causal_query(
  cypher: <query string>,
  params?: { ... }
)

Returns: { rows, stats: { execMs, rowsMatched, rowsReturned } }

Example queries

-- Top 10 most-cited skills
MATCH (s:Skill)<-[r:references]-(:Episode)
RETURN s.name, count(r) AS cites
ORDER BY cites DESC LIMIT 10

-- Find hub nodes (high in-degree)
MATCH (n)<-[r]-()
RETURN n.id, n.type, count(r) AS inDegree
ORDER BY inDegree DESC LIMIT 20

-- Walk supersedes chain backwards from current ADR
MATCH p=(current:ADR {id: 'ADR-046'})-[:supersedes*]->(ancestor:ADR)
RETURN [n IN nodes(p) | n.title]

-- Cycle detection (worth a warning)
MATCH p=(n)-[:caused*1..5]->(n)
RETURN p LIMIT 5

Safety

The agentdb_causal_query tool accepts arbitrary Cypher, including writes (CREATE, DELETE, DETACH DELETE). For destructive operations, prefer the typed delete tools (agentdb_causal_node_delete, agentdb_causal_edge_delete) — they enforce cascade semantics and write to the attestation log.

Don't

  • Don't use string interpolation for ids — use params to avoid Cypher injection.
  • Don't run MATCH (n) DETACH DELETE n (deletes everything). Always scope the match.
  • Don't loop a Cypher query in client code when one query with WHERE id IN [...] works — the binding's per-call overhead adds up.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

The English summary is being prepared. Showing the original description.

ruvnet/agentdb94Updated Oct 12, 2026

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

The English summary is being prepared. Showing the original description.

ruvnet/agentdb94Updated Oct 12, 2026

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

The English summary is being prepared. Showing the original description.

ruvnet/agentdb94Updated Oct 12, 2026

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

The English summary is being prepared. Showing the original description.

ruvnet/agentdb94Updated Oct 12, 2026

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

The English summary is being prepared. Showing the original description.

ruvnet/agentdb94Updated Oct 12, 2026

Walk the causal graph in AgentDB to explain why two memories are connected, or trace a root cause. Use when the user asks "why did X happen", "what led to Y", or after an incident.

The English summary is being prepared. Showing the original description.

ruvnet/agentdb94Updated Oct 12, 2026

See all skills by ruvnet

Report a problem with this skill