New Project

Why It Matters:

• "Why does this matter? Who should care about the answer?"
• "Is there a policy lever here, or is this more about understanding a mechanism?"

Theoretical Motivation:

• "What's your intuition for why X happens — what's the mechanism?"
• "What would standard theory predict? Do you expect to find something different, and why?"

Data and Setting:

• "Do you have data in mind, or are you open on the data source?"
• "Is there a specific context, time period, country, or institutional setting you're focused on?"

Identification:

• "Is there a natural experiment, policy change, or discontinuity you could exploit?"
• "What's the biggest threat to a causal interpretation — what would a skeptic say?"

Expected Results + Contribution:

• "What would you expect to find? What would genuinely surprise you?"
• "What existing papers are closest to this? What gap does yours fill?"

When to Stop Interviewing

Move to Phase 2 when you have:

• A clear research question (one sentence)
• At least one plausible identification strategy
• Some sense of what data exists or is needed
• The motivation / contribution

If after 3 exchanges the user keeps giving vague answers, move to Phase 2 anyway and flag the open questions.

Phase 2: Research Ideation

Goal: Generate 3–5 structured research questions covering the full range from descriptive to causal.

Announce the transition: "Great — I have enough to generate a structured set of research questions. Let me build that out now."

Then generate 3–5 research questions ordered by type:

For each RQ, develop:

• Hypothesis — testable prediction with expected direction/magnitude
• Identification Strategy:
  • Method (DiD, RDD, IV, synthetic control, etc.)
  • Treatment (what varies, when, where)
  • Control group (comparison units)
  • Key assumption (parallel trends, exclusion restriction, etc.)
  • Main robustness checks (pre-trends test, placebo, etc.)
• Data requirements — what variables, time period, geography, unit of observation
• Key pitfalls — 2 main threats to identification + mitigations
• Related work — 2-3 papers using similar approaches (name only, no fabrication)

Rank the questions by feasibility × contribution:

Phase 3: Save Project Spec

Produce the unified project spec document and save it.

Save to: quality_reports/project_spec_[sanitized_topic].md

# Research Project: [Working Title]

**Date:** [YYYY-MM-DD]
**Researcher:** [from PROJECT_CONTEXT.md if available]

---

## Research Question

[Single clear sentence]

## Motivation

[2–3 paragraphs: why this matters, theoretical context, policy relevance, what the answer would change]

## Research Questions

### RQ1: [Question] — Priority: ★★★ (Feasibility: High / Contribution: High)

**Type:** Causal

**Hypothesis:** [Testable prediction with expected sign]

**Identification Strategy:**
- **Method:** [e.g., Staggered DiD with Sun–Abraham estimator]
- **Treatment:** [What varies and when]
- **Control group:** [Comparison units]
- **Key assumption:** [e.g., Parallel pre-trends conditional on controls]
- **Robustness:** [Pre-trends test, placebo outcomes, alternative control groups]

**Data Requirements:**
- [Dataset or data type needed]
- [Key variables: treatment proxy, outcome, controls]
- [Time period and geography]

**Key Pitfalls:**
1. [Threat + mitigation]
2. [Threat + mitigation]

**Related Work:** [Author (Year)], [Author (Year)]

---

[Repeat for RQ2–RQ5]

---

## Priority Empirical Strategy

[1 paragraph recommending the single highest-priority RQ and why, with the specific identification approach]

## Open Questions

[Issues raised in the interview that need further thought before committing to a strategy]

---

## Suggested Next Steps

1. **`lit-review [topic]`** — Search the literature for related work and citation chains
2. **`data-finder [topic]`** — Find and assess datasets for the priority RQ
3. Once data is secured: **`data-analysis`** to begin analysis

After Saving

Tell the user:

• The spec is saved to quality_reports/project_spec_[topic].md
• Recommended next step: lit-review [topic] to build the literature foundation
• Then: data-finder [topic] to identify and assess data sources