idea-creator

Research Idea Creator

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 /research-lit, /novelty-check, and /research-review to form a complete idea discovery pipeline.

Constants

All constants (PILOT_MAX_HOURS, PILOT_TIMEOUT_HOURS, MAX_PILOT_IDEAS, MAX_TOTAL_GPU_HOURS, REVIEWER_MODEL) are defined in the project's CLAUDE.md. Read them from there before proceeding.

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

Workflow

Phase 1: Landscape Survey (5-10 min)

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

1. Scan local paper library first: Check papers/ and literature/ 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 (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.)
   • Recent arXiv preprints (last 6 months)
   • Use 5+ different query formulations
   • Read abstracts and introductions of the top 10-15 papers

3. 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

4. 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:

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 + REVIEWER_MODEL cross-verification) for each idea

2. Critical review: Use REVIEWER_MODEL 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 REVIEWER_MODEL'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.

1. Design pilots: For each top idea, define the minimal experiment that would give a positive or negative signal:

   • Single seed, small scale (e.g., small dataset subset, fewer epochs)
   • Target: 30 min - PILOT_MAX_HOURS per pilot on 1 GPU
   • Estimate GPU-hours BEFORE launching. If estimated time > PILOT_MAX_HOURS, reduce scale (fewer epochs, smaller subset) or flag as "needs manual pilot"
   • Clear success metric defined upfront (e.g., "if metric improves by > 1%, signal is positive")

2. Deploy in parallel: Use /run-experiment to launch pilots on different GPUs simultaneously:

   GPU 0: Pilot for Idea 1
   GPU 1: Pilot for Idea 2
   GPU 2: Pilot for Idea 3
   

   Use run_in_background: true to launch all at once.

3. Collect results: Use /monitor-experiment to check progress. If any pilot exceeds PILOT_TIMEOUT_HOURS, kill it and collect partial results. Once all pilots complete (or timeout), compare:

   • Which ideas showed positive signal?
   • Which showed null/negative results? (eliminate or deprioritize)
   • Any surprising findings that suggest a pivot?
   • Total GPU-hours consumed (track against MAX_TOTAL_GPU_HOURS budget)

4. Re-rank based on empirical evidence: Update the idea ranking using pilot results. An idea with strong pilot signal jumps ahead of a theoretically appealing but untested idea.

Note: Skip this phase if the ideas are purely theoretical or if no GPU is available. Flag skipped ideas as "needs pilot validation" in the report.

Phase 6: Output — Ranked Idea Report

Write a structured report to IDEA_REPORT.md. When invoked from a workflow command (e.g., /autor.idea-discovery), save to OUTPUT_DIR/IDEA_REPORT.md. When invoked standalone, save to the project root:

# Research Idea Report

**Direction**: [user's research direction]
**Generated**: [date]
**Ideas evaluated**: X generated → Y survived filtering → Z piloted → W recommended

## Landscape Summary
[3-5 paragraphs on the current state of the field]

## Recommended Ideas (ranked)

### Idea 1: [title]
- **Hypothesis**: [one sentence]
- **Minimum experiment**: [concrete description]
- **Expected outcome**: [what success/failure looks like]
- **Novelty**: X/10 — closest work: [paper]
- **Feasibility**: [compute, data, implementation estimates]
- **Risk**: LOW/MEDIUM/HIGH
- **Contribution type**: empirical / method / theory / diagnostic
- **Pilot result**: [POSITIVE: metric +X% / NEGATIVE: no signal / SKIPPED: needs GPU]
- **Reviewer's likely objection**: [strongest counterargument]
- **Why we should do this**: [1-2 sentences]

### Idea 2: [title]
...

## Eliminated Ideas (for reference)
| Idea | Reason eliminated |
|------|-------------------|
| ... | Already done by [paper] |
| ... | Requires > 1 week GPU time |
| ... | Result wouldn't be interesting either way |

## Pilot Experiment Results
| Idea | GPU | Time | Key Metric | Signal |
|------|-----|------|------------|--------|
| Idea 1 | GPU 0 | 45 min | +2.3% CE | POSITIVE |
| Idea 2 | GPU 1 | 30 min | -0.1% CE | NEGATIVE |
| Idea 3 | GPU 2 | 1.5 hr | +0.8% CE | WEAK POSITIVE |

## Suggested Execution Order
1. Start with Idea 1 (positive pilot signal, lowest risk)
2. Idea 3 as backup (weak signal, may need larger scale to confirm)
3. Idea 2 eliminated by pilot — negative result documented

## Next Steps
- [ ] Scale up Idea 1 to full experiment (multi-seed, full dataset)
- [ ] If confirmed, invoke /autor.auto-review-loop for full iteration

Key Rules

• The user provides a DIRECTION, not an idea. Your job is to generate the ideas.
• Quantity first, quality second: brainstorm broadly, then filter ruthlessly.
• A good negative result is just as publishable as a positive one. Prioritize ideas where the answer matters regardless of direction.
• Don't fall in love with any idea before validating it. Be willing to kill ideas.
• Always estimate compute cost. An idea that needs 1000 GPU-hours is not actionable for most researchers.
• "Apply X to Y" is the lowest form of research idea. Push for deeper questions.
• Include eliminated ideas in the report — they save future time by documenting dead ends.
• If the user's direction is too broad, ask them to narrow it before proceeding.

Composing with Other Skills

After this skill produces the ranked report:

/idea-creator "direction"     → ranked ideas
/novelty-check "top idea"     → deep novelty verification (already done in Phase 4, but user can re-run)
/research-review "top idea"   → external critical feedback
implement                     → write code
/run-experiment               → deploy to GPU
/autor.auto-review-loop             → iterate until submission-ready