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cluster-documents

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

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How to use this skill

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  3. Review the proposed files and risks before you approve installation.
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Source SKILL.md: https://github.com/dandye/ai-runbooks/blob/HEAD/skills/cluster-documents/SKILL.md

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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.