/vector-cluster

name: vector-cluster
description: Cluster code by graph community detection via npx ruvector@0.3.13 hooks graph-cluster (spectral / Louvain)
argument-hint: "<namespace> [--k N]"
allowed-tools: Bash Read mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_list

Vector Cluster

Cluster vectors in a namespace by semantic similarity using `ruvector`.

When to use

Use this skill when you have a collection of embeddings and want to discover natural groupings. Clustering reveals themes, identifies outliers, and helps organize large vector collections.

Steps

1. **Ensure ruvector@0.3.13 is available**:

   npm ls ruvector 2>/dev/null | grep '0.3.3' || npm install ruvector@0.3.13

2. **Run clustering** — in ruvector@0.3.13 the only working clustering is via `hooks graph-cluster` (spectral/Louvain over a code graph). The top-level `cluster` command is reserved for distributed cluster ops and is currently "Coming Soon" upstream.

   npx -y ruvector@0.3.13 hooks graph-cluster <files...>
   npx -y ruvector@0.3.13 hooks graph-mincut <files...>

3. **Review output** — JSON with cluster assignments, community labels, and edges. If you see `"graph.nodes is not iterable"`, run `hooks init` first to seed the graph state. 4. **Store results**: `mcp__plugin_ruflo-core_ruflo__memory_store({ key: "clusters-PROJECT-TIMESTAMP", value: "CLUSTER_ASSIGNMENTS", namespace: "vector-clusters" })`

Interpreting results

• **High cohesion** (>0.85): tight, well-defined cluster
• **Medium cohesion** (0.6-0.85): related but diverse content
• **Low cohesion** (<0.6): loose grouping, try higher resolution
• **Outliers**: novel or anomalous files worth investigating

Caveats

• `cluster --namespace ... --k N` and `cluster --density` are **not** valid in ruvector@0.3.13 — those flags fall through to the distributed-cluster command, which only accepts `--status`, `--join`, `--leave`, `--nodes`, `--leader`, `--info`.
• For namespaced k-means over arbitrary embeddings, run k-means in your own code against vectors stored in AgentDB.