/discover

name: discover
description: Discovery phase combining research interviews, literature search, data discovery, and ideation. Routes to appropriate agents based on arguments. Replaces /interview-me, /lit-review, /find-data, /research-ideation.
argument-hint: "[mode: interview | lit | data | ideate] [topic or query]"
allowed-tools: Read,Grep,Glob,Write,Edit,WebSearch,WebFetch,Task

Discover

Launch the Discovery phase of research. Routes to the appropriate agents based on the mode specified.

**Input:** `$ARGUMENTS` — a mode keyword followed by a topic or query.

---

Modes

Default (no mode specified)

If no mode keyword is given, start with an interactive interview to build the research specification.

`/discover interview [topic]` — Research Interview

Conduct a structured conversational interview to formalize a research idea.

**This is conversational.** Ask questions directly in your text responses, one or two at a time. Wait for the user to respond before continuing. Do NOT use AskUserQuestion.

**Agents:** Direct conversation (no agent dispatch) **Output:** Research specification + domain profile

Interview structure: 1. **Big Picture** (1-2 questions): "What phenomenon are you trying to understand?" "Why does this matter?" 2. **Theoretical Motivation** (1-2 questions): "What's your intuition for why X happens?" "What would standard theory predict?" 3. **Data and Setting** (1-2 questions): "What data do you have access to?" "Is there a specific institutional setting?" 4. **Identification** (1-2 questions): "Is there a natural experiment or policy change you can exploit?" "What's the biggest threat to causal interpretation?" 5. **Expected Results** (1-2 questions): "What would you expect to find?" "What would surprise you?" 6. **Contribution** (1 question): "How does this differ from what's been done? What gap are you filling?"

Interview style:

• **Be curious, not prescriptive.** Draw out the researcher's thinking, don't impose your own ideas.
• **Probe weak spots gently.** "What would a skeptic say about...?" not "This won't work because..."
• **Build on answers.** Each question should follow from the previous response.
• **Know when to stop.** If the researcher has a clear vision after 4-5 exchanges, move to the specification.

After interview (5-8 exchanges), produce:

**Output 1: Research Specification** → `quality_reports/research_spec_[topic].md`

# Research Specification: [Title]
## Research Question — [one sentence]
## Motivation — [why this matters, theoretical context, policy relevance]
## Hypothesis — [testable prediction with expected direction]
## Empirical Strategy — [method, treatment, control, identifying assumption, robustness]
## Data — [primary dataset, key variables, sample, unit of observation]
## Expected Results — [what the researcher expects and why]
## Contribution — [how this advances the literature]
## Open Questions — [issues needing further thought]

**Output 2: Domain Profile** → `.claude/references/domain-profile.md` (if still template) Fill in field, target journals, common data sources, identification strategies, field conventions, seminal references, and referee concerns based on the interview.

`/discover lit [topic]` — Literature Review

Search and synthesize academic literature.

**Agents:** Librarian (collector) → librarian-critic (reviewer) **Output:** Annotated bibliography + BibTeX entries + frontier map

Workflow: 1. Read `.claude/references/domain-profile.md` for field journals and seminal references 2. Check `master_supporting_docs/` for uploaded papers 3. Read `bibliography_base.bib` for papers already in the project 4. Dispatch Librarian to search:

• Top-5 journals (AER, Econometrica, QJE, JPE, REStud)
• Field journals from domain-profile.md
• NBER/SSRN/IZA working papers
• **Citation chains** — forward and backward citation tracking from key papers. Follow: (a) backward citations (what do the key papers cite?), and (b) forward citations (who cites the key papers?). This is often the most productive search vector.

5. Assign **proximity scores** to each paper:

• **1** — Directly competes (same question, similar method)
• **2** — Closely related (same question, different method or setting)
• **3** — Related (overlapping topic, different angle)
• **4** — Background (provides theory, method, or context)
• **5** — Tangentially related (useful framing only)

6. Dispatch librarian-critic to check coverage, gaps, recency, scope 7. If gaps found, re-dispatch Librarian for targeted search (max 1 round) 8. Save to `quality_reports/lit_review_[topic].md`

**Unverified citations:** If you cannot verify a citation, mark the BibTeX entry with `% UNVERIFIED`. Do NOT fabricate or guess citation details. Note when working papers have been published — cite the published version.

Output format for each paper:

### [Author (Year)] — [Short Title]
- **Journal:** [venue]
- **Proximity:** [1-5 score]
- **Main contribution:** [1-2 sentences]
- **Identification strategy:** [DiD / IV / RDD / SC / descriptive]
- **Key finding:** [result with effect size]
- **Relevance:** [why it matters for our research]

`/discover data [requirements]` — Data Discovery

Find and assess datasets for the research question.

**Agents:** Explorer (finder) → explorer-critic (assessor) **Output:** Ranked data sources with feasibility grades

Workflow: 1. Read research spec and strategy memo if they exist 2. Read `.claude/references/domain-profile.md` for common data sources in the field 3. Understand what variables are needed: treatment, outcome, controls, time period, geography 4. Dispatch Explorer to search across source categories:

• Public microdata (CPS, ACS, NHIS, MEPS, etc.)
• Administrative data (Medicare claims, tax records, court records)
• Survey data (RAND HRS, PSID, Add Health, NLSY)
• International (World Bank, OECD, Eurostat)
• Novel/alternative (satellite imagery, web scrapin