Research ideation
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Generate structured research questions, testable hypotheses, and empirical strategies from a topic or dataset
SKILL.md
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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").
Steps
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Understand the input. Read
$ARGUMENTSand any referenced files. Checkmaster_supporting_docs/for related papers. Check.claude/rules/for domain conventions. -
Generate 3-5 research questions ordered from descriptive to causal:
- Descriptive: What are the patterns? (e.g., "How has X evolved over time?")
- Correlational: What factors are associated? (e.g., "Is X correlated with Y after controlling for Z?")
- Causal: What is the effect? (e.g., "What is the causal effect of X on Y?")
- Mechanism: Why does the effect exist? (e.g., "Through what channel does X affect Y?")
- Policy: What are the implications? (e.g., "Would policy X improve outcome Y?")
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For each research question, develop:
- Hypothesis: A testable prediction with expected sign/magnitude
- Identification strategy: How to establish causality (DiD, IV, RDD, synthetic control, etc.)
- Data requirements: What data would be needed? Is it available?
- Key assumptions: What must hold for the strategy to be valid?
- Potential pitfalls: Common threats to identification
- Related literature: 2-3 papers using similar approaches
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Rank the questions by feasibility and contribution.
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Save the output to
quality_reports/research_ideation_[sanitized_topic].md
Output Format
# Research Ideation: [Topic]
**Date:** [YYYY-MM-DD]
**Input:** [Original input]
## Overview
[1-2 paragraphs situating the topic and why it matters]
## Research Questions
### RQ1: [Question] (Feasibility: High/Medium/Low)
**Type:** Descriptive / Correlational / Causal / Mechanism / Policy
**Hypothesis:** [Testable prediction]
**Identification Strategy:**
- **Method:** [e.g., Difference-in-Differences]
- **Treatment:** [What varies and when]
- **Control group:** [Comparison units]
- **Key assumption:** [e.g., Parallel trends]
**Data Requirements:**
- [Dataset 1 — what it provides]
- [Dataset 2 — what it provides]
**Potential Pitfalls:**
1. [Threat 1 and possible mitigation]
2. [Threat 2 and possible mitigation]
**Related Work:** [Author (Year)], [Author (Year)]
---
[Repeat for RQ2-RQ5]
## Ranking
| RQ | Feasibility | Contribution | Priority |
|----|-------------|-------------|----------|
| 1 | High | Medium | ... |
| 2 | Medium | High | ... |
## Suggested Next Steps
1. [Most promising direction and immediate action]
2. [Data to obtain]
3. [Literature to review deeper]
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 (FRED, Census, PSID, administrative data, etc.).