research-ideation

name	research-ideation
description	Use when the researcher has no specific research question yet — when they need to discover what to study, not how to study it. Triggers on keywords like 'what should I research', 'research ideas', 'what can I do with this dataset', or when a dataset/paper is provided without a formed question.

Research Ideation

Overview

Generate concrete research ideas from loose inputs — keywords, datasets, papers — and present them with actionable metadata. This skill is divergent: it expands possibilities. Its downstream partner research-brainstorming is convergent: it narrows one idea into a rigorous study design.

Core principle: You cannot refine a question you haven't asked yet. Ideation comes before design.

When to Use

Use this skill when:

• The user has no formed research question — only keywords, a dataset, or vague interest
• The user says "what should I research?", "what can I do with this data?", "research ideas for [topic]"
• A dataset or paper is provided without a specific question attached
• whats-next diagnoses the state as Pre-ideation

Do NOT use this skill when:

• The user already has a research question (e.g., "Does X cause Y?") → use research-brainstorming
• The user has a hypothesis to test → use hypothesis-first
• The user is stuck mid-project → use whats-next

Discriminating rule: If the user can state their interest as "Does/Is/Can [X] [verb] [Y]?", they have a formed question → skip to research-brainstorming. If they cannot, they are in ideation territory.

Checklist (Step Order Fixed)

You MUST create a task for each of these items and complete them in order:

1. Collect inputs — gather keywords/datasets/papers from conversation or by asking
2. Explore sources — analyze what's available
3. Generate ideas — 3-5 by default; 5-10 if scope is broad
4. Present ideas — each with title + description + metadata table
5. Recommend and propose — highlight one + suggest research-brainstorming handoff

Step 1: Collect Inputs

The skill accepts three types of input. All optional, but at least one must be present:

1. Keywords / interest area — e.g., "attention mechanism", "neuroimaging", "time-series forecasting"
2. Dataset — files in the project (csv, npy, etc.) whose structure the agent explores
3. Papers / literature — user-provided PDFs, URLs, or web search for recent trends

Collection rules:

• Check what the user already provided in conversation. Skip what's already known.
• If nothing is provided, ask once: "Are there datasets or papers I should look at? Or just throw me some keywords."
• Minimum requirement: one keyword. The skill can start from a single keyword alone.
• Do NOT ask multiple questions. One prompt, then start working.

Step 2: Explore Sources

Adapt exploration to the input types available:

Input type	What to do
Dataset	Read the file(s). Analyze columns, shape, dtypes, descriptive stats (N, mean, std, NaN rate, label distribution). Note what variables are available and what relationships could be studied.
Paper	Read abstract + methodology + limitations. Extract the research question, key findings, and identified gaps. Note what the authors suggest for future work.
Keywords	Web-search for recent trends, open problems, and active debates in the area. Look for survey papers, workshop topics, and recent preprints.

When multiple input types are present, look for cross-pollination — where dataset characteristics meet literature gaps or keyword trends.

Step 3: Generate Ideas

Default: 3-5 ideas. If the agent judges the scope is broad (multiple fields, large dataset with many variables, or diverse literature), expand to 5-10 ideas without asking.

Each idea should be:

• Concrete — specific enough that research-brainstorming could start refining it
• Distinct — ideas should not be minor variations of each other
• Grounded — connected to the actual inputs (data available, literature gap identified, trend observed)

Step 4: Present Ideas

Each idea follows this template:

N. [Idea Title]

[2-3 sentences: why this is interesting, what gap it fills]

Item	Detail
Difficulty	High / Medium / Low
Data needed	Already available / Additional collection required / Public dataset available
Estimated duration	~2 weeks / ~1 month / ~3 months (one researcher, full-time, excluding data collection)
Core methodology	e.g., "transformer + EEG temporal features"

Step 5: Recommend and Propose

After presenting all ideas, recommend one:

"I find #N most promising — [one-sentence reason]. Want to start research-brainstorming to shape this into a rigorous study design?"

The user may:

• Pick the recommended idea → invoke research-brainstorming
• Pick a different idea → invoke research-brainstorming with that idea
• Ask for more ideas → generate additional ideas
• End the session → suggest journaling (see Session End below)

All are valid outcomes. The handoff is a suggestion, never a forced transition.

Session End Behavior

If the user ends the session without picking an idea:

"Want me to save these ideas before we wrap up? research-journal can capture them so you don't lose them."

This is a gentle suggestion. Ideas are ephemeral by default — saved only if the user journals or picks one to develop.

Anti-Patterns

What you might do wrong	What to do instead
Start designing a study for one of the ideas	Stop. That's research-brainstorming's job. Present ideas, don't refine them.
Generate only one idea	Always generate at least 3. The user needs options.
Ask 5 questions before generating	Collect inputs in one prompt, then work.
Force the user to pick an idea	Present, recommend, wait. The user decides.
Generate ideas unrelated to the inputs	Every idea must connect to at least one input (keyword, dataset feature, or literature gap).
Skip exploring the dataset	If a dataset is provided, you MUST look at its structure. Don't ideate in the abstract.

Integration

• Called by: eureka:using-eureka (when no formed question exists), eureka:whats-next (Pre-ideation diagnosis)
• Hands off to: eureka:research-brainstorming (when user selects an idea — suggestion only)
• Does NOT invoke: hypothesis-first, experiment-design, or any execution/review skill

Skill Type

FLEXIBLE — The step order (collect → explore → generate → present → recommend) is fixed and must not be skipped. But how each step executes adapts to context:

• Dataset-heavy input → more structural analysis, fewer web searches
• Keyword-only input → heavier web search for trends and gaps
• Broad field → 5-10 ideas; narrow niche → 3-5 ideas

No scientific integrity is at stake at the ideation phase. The downstream rigid skills (hypothesis-first, claims-audit) enforce that discipline later.