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RamanEbrahimi
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cross-pollination-ideation

Generates novel research ideas by combining interdisciplinary techniques and validating them with quality filters.

3|Updated Apr 17, 2026
Search Tags:#idea generation#cross-disciplinary research#AI-driven research#mathematical connections#theorems retrieval
One-click install
npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill cross-pollination-ideation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cross-pollination-ideation
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/cross-pollination-ideation
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill cross-pollination-ideation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill aids researchers in discovering novel research directions by finding cross-disciplinary connections, retrieving obscure theorems, and identifying analogies between different mathematical domains.

Core Features & Use Cases

  • Cross-Disciplinary Ideation: Generate new research ideas by connecting techniques from various fields.
  • Technique Space Mapping: Map the core mathematical structure, proof/solution techniques, and modeling assumptions of a problem.
  • Analogy Searching: Identify parallels between identified structures and Raman's research areas.
  • Parameterization Testing: Apply Raman's methodology for parameterization testing and interpolation of techniques.
  • Obscure Theorem Retrieval: Use AI to restate a problem abstractly and search for relevant theorems from other fields.
  • AI Behavioral Science Framework: Apply AI Behavioral Science's framework for studying AI-human strategic interactions.
  • Quality Filtering: Run novelty, feasibility, and significance checks on proposed ideas.

Quick Start

Activate the skill by running 'cross-pollination-ideation <your-research-problem>' to generate ideas.

Frequently Asked Questions about cross-pollination-ideation

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

FAQPage Schema
How do I generate cross-disciplinary research ideas for mathematical problems?▼

Cross-disciplinary research ideas are generated by mapping a problem's core mathematical structure, identifying proof techniques, and searching for analogies across different fields to unlock novel research directions.

Can AI retrieve obscure theorems from other fields for my research?▼

AI can retrieve obscure theorems by restating your problem abstractly and searching across diverse mathematical domains to find relevant structural matches and applicable theorems.

How does behavioral game theory apply to AI interactions?▼

Behavioral game theory applies to AI interactions by providing a framework to study and analyze strategic AI-human interactions, helping researchers understand and model complex behavioral dynamics.

What is the best way to filter research ideas for novelty and significance?▼

The best way to filter research ideas is to run an automated quality filter that evaluates proposed concepts against novelty, feasibility, and significance criteria to ensure relevant outcomes.

Do I need specific mathematical dependencies to use cross-pollination ideation?▼

No specific dependencies are required to use cross-pollination ideation, allowing researchers to directly input their research problems and generate interdisciplinary connections without setup barriers.

When should I use technique space mapping for problem-solving?▼

Technique space mapping should be used when you need to abstractly define a problem's core mathematical structure, modeling assumptions, and solution techniques to find cross-domain parallels.

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