Research Ideation

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]

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 Task with subagent_type=claim-verifier and context=fork. 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"

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.).