/aris-idea-creator

name: aris-idea-creator
description: Generate and rank research ideas given a broad direction. Use when user says "找idea", "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, mcp__codex__codex, mcp__codex__codex-reply
license: MIT
metadata:
  author: wanshuiyin/ARIS
  version: "1.0.0"

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 `/aris-research-lit`, `/aris-novelty-check`, and `/aris-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., `/aris-idea-creator "topic" — pilot budget: 4h per idea, 20h total`.

Workflow

Phase 0: Load Research Wiki (if active)

**Skip this phase entirely if `research-wiki/` does not exist.**

if research-wiki/query_pack.md exists AND is less than 7 days old:
    Read query_pack.md and use it as initial landscape context:
    - Treat listed gaps as priority search seeds
    - Treat failed ideas as a banlist (do NOT regenerate similar ideas)
    - Treat top papers as known prior work (do not re-search them)
    Still run Phase 1 below for papers from the last 3-6 months (wiki may be stale)
else if research-wiki/ exists but query_pack.md is stale or missing:
    python3 tools/research_wiki.py rebuild_query_pack research-wiki/
    Then read query_pack.md as above

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

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:

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 `/aris-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 `/aris-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