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vector-cluster
Cluster code by graph community detection via npx ruvector@0.2.25 hooks graph-cluster (spectral / Louvain)
DeepseekModel
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Quality Excellent · 90
v1.0.0
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name vector-cluster description Cluster code by graph community detection via npx ruvector@0.2.25 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 Ensure ruvector@0.2.25 is available : npm ls ruvector 2>/dev/null | grep '0.2.25' || npm install ruvector@0.2.25 Run clustering — in ruvector@0.2.25 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@0.2.25 hooks graph-cluster <files...> npx -y ruvector@0.2.25 hooks graph-mincut <files...> 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. 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@0.2.25 — 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.
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| Field | Description |
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| format | Format tag (skill/v1) |
| skill_id | Unique skill ID |
| name | Skill name |
| version | Version |
| description | Description |
| category | Categories (array) |
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