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vector-cluster — Claude Code skill: install and use

Clusters code files with graph community detection. Use it to inspect related areas of a project and spot outliers.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓ 
npx skills add ruvnet/ruflo --skill vector-cluster -a claude-code
Open on GitHub

What vector-cluster does

This skill runs [email protected] to create code-graph clusters from supplied files. It calls hooks graph-cluster with spectral or Louvain community detection, and can also run hooks graph-mincut. The output is JSON containing cluster assignments, community labels, and graph edges.

It checks for [email protected] and installs the package with npm when needed. Results can be stored with the memory MCP tool under the vector-clusters namespace. If graph.nodes is not iterable appears, run hooks init before clustering.

The top-level cluster command is the wrong path here because [email protected] reserves it for distributed cluster operations. Its --namespace, --k, and --density flags will not work. Use the hooks commands instead, or run k-means in your own code against vectors in AgentDB.

What it helps with

  • group code files by graph communities
  • find related code areas
  • detect outlier files
  • store cluster assignments in agent memory

Example prompts for the agent

  • Run vector-cluster for `src/*.ts` with `hooks graph-cluster`.
  • Cluster `src/app.ts` and `src/api.ts`, then show the community JSON.
  • Run `hooks init` first, then use vector-cluster on `src/*.ts`.

How to install vector-cluster

1Skills CLI (Vercel)

npx skills add ruvnet/ruflo --skill vector-cluster -a claude-code

2Manually: put the files in place

~/.claude/skills/vector-cluster/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/vector-cluster/

From the same set104

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Questions about vector-cluster

How do I install vector-cluster in Claude Code?

Run npx skills add ruvnet/ruflo --skill vector-cluster -a claude-code, then run npx skills add ruvnet/ruflo --skill vector-cluster -a claude-code again. The skill file is placed at ~/.claude/skills/vector-cluster/SKILL.md.

What does vector-cluster do?

It clusters supplied code files through graph community detection in [email protected]. It returns JSON with cluster assignments, community labels, and edges.

Why does cluster with --k fail?

In [email protected], the top-level cluster command is for distributed cluster operations and does not accept --namespace, --k, or --density. Use hooks graph-cluster or hooks graph-mincut for file clustering.

Source text

SKILL.md · MIT 
---
name: vector-cluster
description: Cluster code by graph community detection via npx [email protected] 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 [email protected] is available**:
   ```bash
   npm ls ruvector 2>/dev/null | grep '0.3.3' || npm install [email protected]
   ```
2. **Run clustering** — in [email protected] 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.
   ```bash
   npx -y [email protected] hooks graph-cluster <files...>
   npx -y [email protected] 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 [email protected] — 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.

© ruvnet · MIT · ruvnet/ruflo

See also

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