Divergent Thinking Scoring

Score divergent thinking responses across fluency, flexibility, originality, elaboration, and semantic distance.

34|5|Updated Feb 28, 2026
One-click install
npx skills add https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills --skill divergent-thinking-scoring
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Divergent Thinking Scoring
Source: https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/tree/main/skills/divergent-thinking-scoring
Command: npx skills add https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills --skill divergent-thinking-scoring

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Divergent Thinking Scoring provides a structured, domain-validated framework to score divergent thinking responses across multiple dimensions—fluency, flexibility, originality, elaboration, and semantic distance—allowing researchers to obtain reliable creativity metrics in cognitive science studies.

Core Features & Use Cases

  • Scoring dimensions: fluency, flexibility, originality, elaboration, semantic distance
  • Methods: statistical rarity, subjective originality ratings, and automated semantic-distance scoring
  • Guidance for inter-rater reliability, data normalization, and transparent reporting
  • Use Case: evaluate AUT or Unusual Uses Task responses with consistent scoring and clear documentation

Quick Start

Test the scoring workflow by providing a small AUT response set and following the prompts to apply multi-dimensional scoring.

Frequently Asked Questions about Divergent Thinking Scoring

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I score divergent thinking responses across multiple dimensions?▼

Divergent thinking scoring evaluates responses across fluency, flexibility, originality, elaboration, and semantic distance dimensions. It applies statistical rarity, subjective ratings, and automated semantic-distance methods to generate reliable creativity metrics.

What is the best way to calculate inter-rater reliability for AUT responses?▼

Inter-rater reliability for AUT responses is established by applying standardized scoring guidelines across multiple raters. This ensures consistent subjective originality ratings and transparent reporting for cognitive psychology research.

Can I use automated semantic distance scoring for the Unusual Uses Task?▼

Yes, automated semantic-distance scoring is supported for the Unusual Uses Task. It measures semantic relationships in responses to generate creativity metrics, providing an alternative to human-rated subjective originality scoring.

Does divergent thinking scoring support data normalization for cognitive psychology research?▼

Yes, divergent thinking scoring includes guidance for data normalization in cognitive psychology research. It provides methods to normalize multi-dimensional scores, ensuring transparent reporting and reliable creativity metrics across study conditions.

When should I use statistical rarity versus subjective originality ratings?▼

Statistical rarity provides objective frequency-based scoring for divergent thinking responses, while subjective originality ratings offer human-rated qualitative assessment. Both methods require inter-rater reliability guidelines to produce reliable creativity metrics.