/generate-idea

name: generate-idea
description: Generate and rank research ideas given a broad direction. Use when user input "brainstorm ideas", "generate research ideas", "what can we work on", or wants to explore a research area for publishable directions.
argument-hint: [research-direction]
allowed-tools: Bash(*), Read, Write, Grep, Glob, WebSearch, WebFetch, Agent

Research Idea Generator

Generate publishable research ideas for: $ARGUMENTS

Overview

Given a broad research direction from the user, systematically generate, validate, and rank concrete research ideas. This skill composes with `/lit-review`, `/novelty-check`, and `/research-review` to form a complete idea discovery pipeline.

Constants

• **PILOT_MAX_HOURS = 2** — Skip any pilot estimated to take > 2 hours per GPU. Flag as "needs manual pilot".
• **PILOT_TIMEOUT_HOURS = 3** — Hard timeout: kill pilots exceeding 3 hours. Collect partial results if available.
• **MAX_PILOT_IDEAS = 3** — Pilot at most 3 ideas in parallel. Additional ideas are validated on paper only.
• **MAX_TOTAL_GPU_HOURS = 8** — Total GPU budget for all pilots combined.
• **REVIEWER_MODEL = `gpt-5.4`** — Model used via Codex MCP for brainstorming and review. Must be an OpenAI model (e.g., `gpt-5.4`, `o3`, `gpt-4o`).

> 💡 Override via argument, e.g., `/generate-idea "topic" — pilot budget: 4h per idea, 20h total`.

Workflow

Phase 1: Get Literature Reivew Results

Map the research area to understand what exists and where the gaps are.

If `output/LIT_REVIEW_REPORT.md` exists (with Synthesis and Gap Analysis sections), read it and skip Phase 1 silently. If not, do the following:

1. **Scan local paper library first**: Check `papers/` in the project directory for existing PDFs. Read first 3 pages of relevant papers to build a baseline understanding before searching online. This avoids re-discovering what the user already knows.

2. **Search recent literature** using WebSearch:

• Top venues in the last 2 years
• Recent arXiv preprints (last 6 months)
• Use 5+ different query formulations
• Read abstracts and introductions of the top 10-15 papers

2. **Build a landscape map**:

• Group papers by sub-direction / approach
• Identify what has been tried and what hasn't
• Note recurring limitations mentioned in "Future Work" sections
• Flag any open problems explicitly stated by multiple papers

3. **Identify structural gaps**:

• Methods that work in domain A but haven't been tried in domain B
• Contradictory findings between papers (opportunity for resolution)
• Assumptions that everyone makes but nobody has tested
• Scaling regimes that haven't been explored
• Diagnostic questions that nobody has asked

Phase 2: Idea Generation (brainstorm with external LLM)

Use the external LLM via Codex MCP for divergent thinking. If external LLM is not configured properly, use subagent with the most powerful model instead.

mcp__codex__codex:
  model: REVIEWER_MODEL
  config: {"model_reasoning_effort": "xhigh"}
  prompt: |
    You are a senior ML researcher brainstorming research ideas.

    Research direction: [user's direction]

    Here is the current landscape:
    [paste landscape map from Phase 1]

    Key gaps identified:
    [paste gaps from Phase 1]

    Generate 8-12 concrete research ideas. For each idea:
    1. One-sentence summary
    2. Core hypothesis (what you expect to find and why)
    3. Minimum viable experiment (what's the cheapest way to test this?)
    4. Expected contribution type: empirical finding / new method / theoretical result / diagnostic
    5. Risk level: LOW (likely works) / MEDIUM (50-50) / HIGH (speculative)
    6. Estimated effort: days / weeks / months

    Prioritize ideas that are:
    - Testable with moderate compute (8x RTX 3090 or less)
    - Likely to produce a clear positive OR negative result (both are publishable)
    - Not "apply X to Y" unless the application reveals genuinely surprising insights
    - Differentiated from the 10-15 papers above

    Be creative but grounded. A great idea is one where the answer matters regardless of which way it goes.

Save the threadId for follow-up.

Phase 3: First-Pass Filtering

For each generated idea, quickly evaluate:

1. **Feasibility check**: Can we actually run this experiment with available resources?

• Compute requirements (estimate GPU-hours)
• Data availability
• Implementation complexity
• Skip ideas requiring > 1 week of GPU time or unavailable datasets

2. **Novelty quick-check**: For each idea, do 2-3 targeted searches to see if it's already been done. Full `/novelty-check` comes later for survivors.

3. **Impact estimation**: Would a reviewer care about the result?

• "So what?" test: if the experiment succeeds, does it change how people think?
• Is the finding actionable or just interesting?

Eliminate ideas that fail any of these. Typically 8-12 ideas reduce to 4-6.

Phase 4: Deep Validation (for top ideas)

For each surviving idea, run a deeper evaluation:

1. **Novelty check**: Use the `/novelty-check` workflow (multi-source search + GPT-5.4 cross-verification) for each idea

2. **Critical review**: Use GPT-5.4 via `mcp__codex__codex-reply` (same thread):

   Here are our top ideas after filtering:
   [paste surviving ideas with novelty check results]

   For each, play devil's advocate:
   - What's the strongest objection a reviewer would raise?
   - What's the most likely failure mode?
   - How would you rank these for a top venue submission?
   - Which 2-3 would you actually work on?

3. **Combine rankings**: Merge your assessment with GPT-5.4's ranking. Select top 2-3 ideas for pilot experiments.

Phase 5: Parallel Pilot Experiments (for top 2-3 ideas)

Before committing to a full research effort, run cheap pilot experiments to get empirical signal. This is the key differentiator from paper-only validation.

**Phase 5.0: Mandatory Local GPU Availability Check (RE