research-ideation

name: research-ideationdescription: Generate structured research questions, testable hypotheses, and candidate empirical strategies from a topic, phenomenon, or dataset description. Use when user says "give me research ideas on X", "brainstorm questions about Y", "what could I study with this data?", "I'm looking for a paper idea on...", "generate hypotheses for...". One-shot generation, not multi-turn. For idea-refinement use `/interview-me`.argument-hint: "[topic, phenomenon, or dataset description] [--no-verify]"allowed-tools: ["Read", "Grep", "Glob", "Write", "WebSearch", "WebFetch", "Agent", "Task"]

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. Check .claude/rules/ for domain conventions.

2. 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?")

3. Tag each RQ with a likely paper type (drawn from methods-referee.md):

   • reduced-form (DiD, IV, RD, event study, synthetic control)
   • structural (estimation of a fully-specified model)
   • theory+empirics (formal model + empirical test of its predictions)
   • descriptive (measurement, data construction, pattern documentation)
   • formal-theory (pure theory, no empirical test in this paper)
   • survey-experiment (vignette, conjoint, list-experiment)
   • unsure (when multiple types are plausible — the user can pick later via /interview-me)

   Use .claude/references/discipline-cards.md to bias the distribution by field (econ vs poli-sci default frequencies differ — e.g., poli-sci skews more toward survey-experiment and formal-theory than econ does).

4. 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

5. Rank the questions by feasibility and contribution.

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

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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
**Paper type:** reduced-form / structural / theory+empirics / descriptive / formal-theory / survey-experiment / unsure

**Hypothesis:** [Testable prediction]

**Identification Strategy:**
- **Method:** [the identification approach you would defend in a seminar]
- **Treatment:** [What varies and when]
- **Control group:** [Comparison units]
- **Key assumption:** [the assumption the method's validity rests on, stated so it can be attacked]

**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)]

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[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]

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Post-Flight Verification (mandatory, CoVe)

Before returning the ideation report, run the Post-Flight Verification protocol from .claude/rules/post-flight-verification.md. Research ideation is hallucination-prone in three specific ways:

1. Negative-literature claims — "no prior work studies X" is frequently wrong.
2. Dataset structure claims — "The CPS contains field educ_attain" can be confidently wrong about variable names, coverage years, or restricted-access status.
3. Estimator feasibility claims — "this works with panel fixed effects" can misstate an identification assumption.

Steps

1. Extract claims from the draft ideation report: each negative-literature claim, each named dataset with attributed fields, each claimed identification strategy + required data structure.
2. Generate verification questions per claim. Example: "Has Card & Krueger, Autor, or anyone in the last 10 years studied X? Search Google Scholar + NBER working papers." / "Does IPUMS-CPS include the educ_attain variable 1990–2024?"
3. Spawn claim-verifier via the Agent tool with subagent_type=claim-verifier, in a fresh context — a named Agent call, not a conversation fork, which would inherit the draft. Hand it claims + questions + source pointers (WebSearch allowed, NBER/SSRN URLs preferred, dataset codebooks preferred). Do NOT include the draft.
4. Reconcile: PASS → attach green block; PARTIAL → mark uncertain RQs with flags; FAIL → rewrite the affected RQ/hypothesis/strategy.

Skip conditions

• --no-verify flag
• User explicitly says "I'll verify the literature myself"

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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 (FRED, Census, PSID, administrative data, etc.).