research-ideation

name	research-ideation
description	Generate structured research questions, testable hypotheses, and empirical strategies from a topic or dataset
disable-model-invocation	true
argument-hint	[topic, phenomenon, or dataset description]
allowed-tools	["Read","Grep","Glob","Write"]

Research Ideation

Generate structured research questions, testable hypotheses, and empirical strategies from a topic, phenomenon, or dataset.

Input: $ARGUMENTS -- a topic (e.g., "minimum wage effects on employment"), a phenomenon (e.g., "why do firms cluster geographically?"), or a dataset description (e.g., "panel of US counties with pollution and health outcomes, 2000-2020").

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Steps

1. Understand the input. Read $ARGUMENTS and any referenced files. Check master_supporting_docs/ for related papers.

2. Generate 3-5 research questions ordered from descriptive to causal:

   • Descriptive: What are the patterns?
   • Correlational: What factors are associated?
   • Causal: What is the effect?
   • Mechanism: Why does the effect exist?
   • Policy: What are the implications?

3. For each research question, develop:

   • Hypothesis: A testable prediction with expected sign/magnitude
   • Identification strategy: How to establish causality
   • Data requirements: What data would be needed?
   • Key assumptions: What must hold?
   • Potential pitfalls: Common threats to identification
   • Related literature: 2-3 papers using similar approaches

4. Rank the questions by feasibility and contribution.

5. Save the output to quality_reports/research_ideation_[sanitized_topic].md

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Principles

• Be creative but grounded. Push beyond obvious questions, but every suggestion must be empirically feasible.
• Think like a referee. For each causal question, immediately identify the identification challenge.
• Consider data availability. A brilliant question with no available data is not actionable.
• Suggest specific datasets where possible.