Clustering Methodology (Grade A) logo

Clustering Methodology (Grade A)Security-tested data-ai skill for Claude AI. Grade A. This skill should be used when the intelligence-cluster agent needs guidance on clustering intelligence cards to themes, detecting new themes, and

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Daniel NikulshynReviewed by Daniel Nikulshyn·Updated July 2026

Overview

This skill is designed to provide guidance for an intelligence-cluster agent in clustering intelligence cards to themes, detecting new themes, and determining card-theme assignments. The algorithm reads card contents and uses a combination of field matching, keyword matching, and entity matching to assign confidence scores to potential themes. The agent can then make decisions based on these confidence scores. New themes are considered when a certain number of cards do not match any existing themes and share similar features. The skill outputs a JSON object containing clustered cards, edge cases, unclustered cards, and suggested new themes, along with their respective confidence scores and reasons. It also includes a quality check to ensure that all cards have been assigned a cluster, that confidence scores are computed correctly, and that new themes have sufficient support. The skill provides specific guidance for different domains, including Threat-Landscape, Emerging-Tech, and Vendor-Intelligence, and can handle multi-theme assignments and edge cases where card topics do not match any existing theme definitions.

Key features

  • Domain matching for theme assignment
  • Keyword and entity extraction for card-theme matching
  • Confidence scoring for assignment decisions
  • New theme detection based on unclustered cards
  • Handling of edge cases and multi-theme assignments

Pricing

Model
Free
Category
Skills
Rating
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Use cases

Threat Landscape Analysis

Clustering intelligence cards related to threat actors, attack types, and industry targets into themes like ransomware threats or APT activities.

Emerging Tech Monitoring

Grouping cards about new technologies, such as AI security, cloud security, or zero-trust architecture, into relevant themes.

Pros & Cons

Pros

  • Improves theme clustering accuracy for intelligence cards
  • Detects new themes based on unclustered cards and their features
  • Provides detailed confidence scores for each card-theme assignment

Cons

  • Requires careful tuning of theme definitions and matching rules
  • May struggle with ambiguous or complex card content
  • Dependent on quality of metadata and core facts in cards

Reviews

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Q&A

What limitations should I be aware of?

The method requires well-defined theme rules and high-quality metadata; it may struggle with ambiguous or complex card content and needs careful tuning to avoid misclassification.

Asked by Ines Fernandes · May 20, 2026

What happens to cards that don’t fit any theme?

Such cards are listed as unclustered, and edge cases are reported. They may prompt the creation of new themes or indicate a need to refine existing theme definitions.

Asked by Ravi Chandrasekaran · Mar 8, 2026

Can the skill detect entirely new themes?

Yes, if a sufficient number of cards fail to match existing themes and share similar features, the algorithm suggests new themes with associated confidence scores and reasons.

Asked by Umar Farooq · Mar 6, 2026

How does the Clustering Methodology (Grade A) determine card-theme assignments?

It analyzes each card’s content using field matching, keyword matching, and entity extraction to calculate confidence scores for potential themes. The highest-scoring theme is assigned unless the score falls below a threshold.

Asked by Elias Hedström · Jan 31, 2026

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