Cluster
/install cluster
Cluster — Data Clustering Analysis Tool
Cluster is a command-line data clustering analysis tool that supports k-means and hierarchical clustering algorithms. It reads numerical data from CSV/JSONL sources, performs clustering, evaluates cluster quality, and exports results.
Data is stored in ~/.cluster/data.jsonl as JSONL records. Each record represents a clustering run with its parameters, assignments, centroids, and evaluation metrics.
Prerequisites
- Python 3.8+ with standard library (no external packages required for basic operations)
bashshell
Commands
run
Run a clustering algorithm on input data.
Environment Variables:
INPUT(required) — Path to input CSV/JSONL file with numerical dataK— Number of clusters (default: 3)ALGORITHM— Algorithm to use:kmeansorhierarchical(default: kmeans)MAX_ITER— Maximum iterations for k-means (default: 100)SEED— Random seed for reproducibility
Example:
INPUT=/path/to/data.csv K=5 ALGORITHM=kmeans bash scripts/script.sh run
assign
Assign new data points to existing clusters from a previous run.
Environment Variables:
RUN_ID(required) — ID of the clustering run to useINPUT(required) — Path to new data points (CSV/JSONL)
Example:
RUN_ID=abc123 INPUT=/path/to/new_data.csv bash scripts/script.sh assign
centroids
Display or export centroid coordinates for a clustering run.
Environment Variables:
RUN_ID(required) — ID of the clustering runFORMAT— Output format:table,json,csv(default: table)
evaluate
Evaluate clustering quality with silhouette score, inertia, and Davies-Bouldin index.
Environment Variables:
RUN_ID(required) — ID of the clustering run to evaluate
visualize
Generate a text-based or ASCII visualization of cluster assignments.
Environment Variables:
RUN_ID(required) — ID of the clustering runDIMS— Dimensions to plot, comma-separated (default: first two)
export
Export clustering results to a file.
Environment Variables:
RUN_ID(required) — ID of the run to exportOUTPUT— Output file path (default: stdout)FORMAT— Export format:json,csv,jsonl(default: json)
import
Import a previously exported clustering run.
Environment Variables:
INPUT(required) — Path to the file to import
config
View or update configuration settings.
Environment Variables:
KEY— Configuration key to setVALUE— Configuration value
list
List all stored clustering runs with summary info.
Environment Variables:
LIMIT— Maximum runs to display (default: 20)SORT— Sort field:date,k,score(default: date)
stats
Show aggregate statistics across all clustering runs.
help
Display usage information and available commands.
version
Display the current version of the cluster tool.
Data Storage
All clustering runs are stored in ~/.cluster/data.jsonl. Each line is a JSON object with fields:
id— Unique run identifiertimestamp— ISO 8601 creation timealgorithm— Algorithm usedk— Number of clusterscentroids— List of centroid coordinatesassignments— Mapping of data point indices to cluster IDsmetrics— Evaluation metrics (silhouette, inertia, etc.)input_file— Source data file pathnum_points— Number of data points clustered
Configuration
Config is stored in ~/.cluster/config.json. Available keys:
default_k— Default number of clusters (default: 3)default_algorithm— Default algorithm (default: kmeans)max_iterations— Default max iterations (default: 100)random_seed— Default random seed (default: 42)
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- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install cluster - 安装完成后,直接呼叫该 Skill 的名称或使用
/cluster触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Cluster 是什么?
Perform data clustering analysis using k-means and hierarchical algorithms. Use when you need to group, classify, or segment datasets. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 236 次。
如何安装 Cluster?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install cluster」即可一键安装,无需额外配置。
Cluster 是免费的吗?
是的,Cluster 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Cluster 支持哪些平台?
Cluster 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Cluster?
由 BytesAgain2(@ckchzh)开发并维护,当前版本 v1.0.0。