brainstorm

name: brainstorm description: Use when starting a new research project, exploring a research idea, deciding whether a question is viable, or before touching code or data for a new paper. Runs a research-focused brainstorm that clarifies research question, identification strategy, data feasibility, and contribution before any implementation.

Overview

This is the first step in the superpapers pipeline for any new research project. It starts by invoking academic-baseline as the standing policy layer for the session, then mirrors the Superpowers brainstorming philosophy — Socratic questions, proposed approaches, incremental design approval — but asks research-specific questions. The terminal state is invoking write-plan. No implementation, data collection, or literature review beyond gap-verification happens until the design spec is written and approved by the user.

When to Use

"I have an idea for a paper"
"I want to study X"
"Can you help me think through this research question?"
Start of any new empirical research project
Before any data collection or analysis begins
Revisiting a stalled project with a new angle

When NOT to Use

Mid-project troubleshooting — use statistical-modeling or the relevant domain skill
The research question and design are already clear and stable
Formatting, writing, or submission tasks — use journal-guidelines or other late-stage skills

Hard Gate

Do NOT invoke write-plan, execute-plan, data collection, analysis, or any literature search beyond gap-verification until the design spec is written and the user has explicitly approved it. This applies to every project regardless of apparent simplicity.

Mandatory Steps

Invoke academic-baseline and replication-driven-research first. academic-baseline resolves CLAUDE.superpapers.md via the walk-up Read (current working directory, then parent directories) and carries its settings into the session; its nine principles apply from the first question onward. replication-driven-research anchors the design as end-to-end reproducible — data to scripts to outputs to paper, with fixed seeds. Both skills stay active for the entire brainstorm.
Explore project context. Inspect existing data/, paper/, .bib files, and git history. Settings from CLAUDE.superpapers.md are already loaded via step 1. Learn what already exists before asking questions.
Detect research field and paper language. From project context when possible; otherwise ask the user. The field shapes which databases, methods, and journals will be relevant. The paper language determines later user-facing output.
Ask Socratic questions one at a time. Do not batch. Use multiple choice where possible. Cover these topics in roughly this order:
- Research question: What is the question? Can it be rejected by data?
- Exploratory vs confirmatory: This shapes every downstream decision — specification rigidity, multiple testing corrections, framing.
- Causal vs descriptive: What would a causal answer require — and is that answerable with the available data?
- Identification strategy: Candidate — DiD, RD, IV, SC, time-series, none (descriptive)? Invoke statistical-modeling to apply its guidance on specification, assumptions, and diagnostics for the chosen strategy.
- Data: What would you need? Does it exist? Is it accessible? At what cost? Invoke data-collection for source-discovery guidance only; the hard gate above still blocks actual collection.
- Contribution: What's the contribution over existing literature? Invoke literature-search in gap-check mode: one or two targeted queries, 5-8 results maximum, no bibliography output. This is NOT the full literature review. The full review runs in the plan's Literature phase, where literature-search is invoked in full mode with all its Mandatory Steps. Do not conflate the two invocations.
- Statistical power: Order-of-magnitude check — is the sample large enough to detect plausible effect sizes? Invoke statistical-modeling for its power-calculation and effect-size guidance.
- Publication tier: Target journal tier — and is the design consistent with that tier's expectations? Invoke journal-selection to match the paper to candidate outlets given field, method, and contribution.
Propose 2-3 empirical approaches with trade-offs. Always recommend one and explain why. Present options conversationally, not as a menu.
Present the research design section by section, getting approval after each section. Sections: research question, data strategy, identification strategy, estimation plan, expected outputs (tables/figures), robustness plan, submission target. When presenting the submission target section, journal-selection must already have been invoked in Step 4 — use its recommendation as the basis for this section.
Scale each section to its complexity. A simple descriptive study may need one paragraph per section. A novel identification strategy may need several.
Write the spec to docs/superpapers/specs/YYYY-MM-DD-<topic>-design.md in English. The spec is a plugin artifact, not paper content — English keeps it consistent across projects.
Self-review the spec for placeholders, internal contradictions, scope problems, and ambiguous requirements. Fix inline.
Ask the user to review the written spec. Wait for explicit approval before proceeding.
Transition to write-plan. This is the only terminal state. Do not invoke execute-plan or any implementation skill directly.

Guardrails

Invoke academic-baseline principles throughout — especially the causal-versus-correlational distinction.
Never commit to a result framing in advance. The brainstorm ends with a design, not with conclusions.
Questions asked in the user's conversation language. Spec documents written in English (plugin artifact). User paper content respects the paper language later.

Anti-Patterns

Batching multiple questions in one message
Jumping to implementation before a design is approved
Skipping the exploratory-versus-confirmatory distinction
Proposing only one approach instead of 2-3
Writing the spec without user approval
Letting the user drift into "it's too simple to need a design" — every project gets a design, even if short
Assuming a causal answer is feasible without asking about identification
Starting literature search, data collection, or analysis during the brainstorm itself (beyond minimal gap verification)
Discussing target journal tier without invoking journal-selection
Discussing identification strategy or statistical power without invoking statistical-modeling

Verification Before Completion

Research field detected and confirmed
Paper language established (even if default English)
academic-baseline and replication-driven-research invoked first and applied throughout the brainstorm
Research question is falsifiable and explicit
Exploratory-versus-confirmatory distinction made
Identification strategy identified (or "none, this is descriptive" made explicit)
statistical-modeling invoked for the identification-strategy and statistical-power questions
Data feasibility confirmed
Contribution over literature established
2-3 approaches presented with a recommendation
journal-selection invoked for the Publication tier question and its recommendation used in the submission-target section
Design presented section by section with approval
Spec written to docs/superpapers/specs/ in English
User approved the spec
Next step (write-plan) announced, not executed