# Cluster Documents

> Automated content similarity and grouping analysis. Groups related documents by topic, purpose, or content similarity.

- Skill: `majiayu000/cluster-documents` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/cluster-documents`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/cluster-documents/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/cluster-documents

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# Document Clustering Skill

Analyze a repository of documents to group them based on content similarity, topic, or purpose. This skill helps organize large collections, identify redundancies, and discover relationships.

## Inputs

- `PATH` - The repository to analyze (e.g., "/repository")
- `SIMILARITY_THRESHOLD` - (Optional) Float (0.0-1.0), threshold for grouping (default: 0.8)
- `VISUALIZATION` - (Optional) Boolean, whether to generate a visual representation (default: false)

## Workflow

### Step 1: Text Processing

Ingest documents from `PATH`.
- Normalize text (remove stop words, stemming/lemmatization).
- Generate embeddings or TF-IDF vectors for each document.

### Step 2: Clustering Analysis

Apply clustering algorithms (e.g., K-Means, DBSCAN) to the document vectors.
- Group documents that meet the `SIMILARITY_THRESHOLD`.
- Identify outliers or unique documents.

### Step 3: Cluster Labeling

Analyze the centroid or representative terms of each cluster to assign a meaningful label (Topic).

### Step 4: Output Generation

Generate the clustering report.
- If `VISUALIZATION` is true, create a scatter plot or dendrogram data.

## Required Outputs

A `CLUSTERING_REPORT` object containing:
- **Cluster List**: ID, Label, and List of Documents in each cluster.
- **Redundancy Report**: Sets of highly similar documents (potential duplicates).
- **Visualization Data**: (If requested) Coordinates for plotting.

## Quick Reference

- **Purpose**: Organize unstructured content and find duplicates.
- **Techniques**: Text Mining, NLP, Vector Space Models.