AgentSkillsCN

cluster-documents

自动化内容相似度与归类分析。按主题、用途或内容相似性对相关文档进行分组。

SKILL.md
--- frontmatter
name: cluster-documents
description: Automated content similarity and grouping analysis. Groups related documents by topic, purpose, or content similarity.
required_roles:
  scribe: roles/scribe.viewer
personas: [information-architect, data-analyst, researcher]

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.