research-ideation

Generate structured research questions and testable hypotheses from topics or datasets.

Updated Jun 27, 2026

Search Tags:#research #data analysis #academic research #hypothesis generation #empirical strategies

Ready to run

npx skills add https://github.com/fredmilhome/laffer_tobacco --skill research-ideation-fredmilhome

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: research-ideation
Source: https://github.com/fredmilhome/laffer_tobacco/tree/main/.claude/skills/research-ideation
Command: npx skills add https://github.com/fredmilhome/laffer_tobacco --skill research-ideation-fredmilhome

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides structured research questions, testable hypotheses, and empirical strategies to guide research projects.

Core Features & Use Cases

Generate Research Questions: Formulate descriptive, correlational, causal, and mechanism research questions based on user-provided topics.
Hypothesis Development: Develop testable predictions with expected sign/magnitude for each question.
Data Requirements Analysis: Identify the necessary data for hypothesis testing, considering availability and structure.
Risk Assessment: Identify potential pitfalls and related literature to strengthen the research proposal.

Quick Start

To get started, run the 'research-ideation' Skill and provide a topic, phenomenon, or dataset description.

Frequently Asked Questions about research-ideation

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

FAQPage Schema

How do I generate testable research hypotheses from a broad topic?▼

To generate testable research hypotheses from a topic, provide a description of your subject or dataset to formulate descriptive, correlational, causal, and mechanism research questions with expected signs and magnitudes. This process structures your initial academic inquiry into actionable predictions.

What is the best way to design an empirical strategy for causal identification?▼

Designing an empirical strategy for causal identification involves generating structured research questions and matching them with testable predictions, while assessing data availability and potential pitfalls to validate the causal mechanism. This ensures your empirical strategy aligns with the available data structure.

Can I use this for academic research in fields like economics and sociology?▼

Yes, you can use this for academic research in fields like economics, politics, and sociology, because it supports social science inquiry by providing structured research questions and literature review guidance tailored to those domains. It handles domain-specific empirical strategies effectively.

How do I check data availability when developing a research proposal?▼

To check data availability during research proposal development, the skill analyzes your hypothesis requirements and identifies the necessary data structure needed for testing. It flags potential data constraints and assesses whether the required empirical data is accessible.

What are the limitations of using automated hypothesis generation for literature reviews?▼

Automated hypothesis generation provides related literature guidance and risk assessment to strengthen your proposal, but it does not replace comprehensive reading or guarantee exhaustive literature coverage. You must still manually validate the identified pitfalls and review the suggested sources.