| 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.
