# Idea Creator

> 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.

- Skill: `raja21068/idea-creator-2` (Agent Skill)
- Install (CLI): `npx skillmds@latest add raja21068/idea-creator-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/raja21068/idea-creator-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- Author: raja21068 (https://skillmd.com/u/raja21068)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/raja21068/idea-creator-2

---


# 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

- **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 a secondary Codex agent for brainstorming and review. Must be an OpenAI model (e.g., `gpt-5.4`, `o3`, `gpt-4o`).
- **REVIEWER_BACKEND = `codex`** — Default: Codex xhigh reviewer through `spawn_agent` / `send_input`. Use `--reviewer: oracle-pro` only when explicitly requested; if Oracle is unavailable, warn and fall back to Codex xhigh.
- **OUTPUT_DIR = `idea-stage/`** — All idea-stage outputs go here. Create the directory if it doesn't exist.

> 💡 Override via argument, e.g., `/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.

Resolve the wiki helper using the Codex-side canonical chain (see
`../shared-references/wiki-helper-resolution.md`):

```bash
ARIS_REPO="${ARIS_REPO:-$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null)}"
WIKI_SCRIPT=""
[ -n "$ARIS_REPO" ] && [ -f "$ARIS_REPO/tools/research_wiki.py" ] && WIKI_SCRIPT="$ARIS_REPO/tools/research_wiki.py"
[ -z "$WIKI_SCRIPT" ] && [ -f tools/research_wiki.py ] && WIKI_SCRIPT="tools/research_wiki.py"
[ -z "$WIKI_SCRIPT" ] && [ -f ~/.codex/skills/research-wiki/research_wiki.py ] && WIKI_SCRIPT="$HOME/.codex/skills/research-wiki/research_wiki.py"
```

If `research-wiki/query_pack.md` exists and is less than 7 days old, read it as initial landscape context:

- treat listed gaps as priority search seeds
- treat failed ideas as a banlist
- treat top papers as known prior work
- still run Phase 1 for papers from the last 3-6 months because the wiki may be stale

If `research-wiki/` exists but `query_pack.md` is stale or missing, rebuild it only when `WIKI_SCRIPT` is available. If the helper is unavailable, continue without rebuilding and report that wiki refresh was skipped.

### 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 a secondary Codex agent for divergent thinking:

```
spawn_agent:
  model: REVIEWER_MODEL
  reasoning_effort: xhigh
  message: |
    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 agent id for follow-up.

Save a Review Tracing record for this `spawn_agent` call following `../shared-references/review-tracing.md`, including the landscape summary, prompt summary, raw idea list path, reviewer route, and saved agent id.

### 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 `send_input` (same agent):
   ```text
   send_input:
     target: [saved reviewer id from the earlier idea review]
     message: |
       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.

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-stage/IDEA_REPORT.md`:

```markdown
# 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 /auto-review-loop for full iteration
```

## Phase 7: Write Ideas to Research Wiki (if active)

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

This is critical for spiral learning: without it, `ideas/` stays empty and re-ideation has no memory.

For each recommended and eliminated idea:

1. Create or update `research-wiki/ideas/<idea_id>.md`.
2. Include `node_id`, `stage`, `outcome`, `based_on`, `target_gaps`, hypothesis, proposed method, expected outcome, and pilot results when available.
3. If `WIKI_SCRIPT` is available, add edges from idea to source papers and target gaps, then rebuild `query_pack.md`.
4. If `WIKI_SCRIPT` is unavailable, write the idea pages and report that graph edges/query-pack rebuild require ARIS `research_wiki.py`.

Required edge semantics when helper support exists:

```text
idea:<id> --inspired_by--> paper:<slug>
idea:<id> --addresses_gap--> gap:<id>
```

Log the update as: `idea-creator wrote N ideas (M recommended, K eliminated)`.

## Output Protocols

> Follow these shared protocols for all output files:
> - **[Output Versioning Protocol](../../shared-references/output-versioning.md)** — write timestamped file first, then copy to fixed name
> - **[Output Manifest Protocol](../../shared-references/output-manifest.md)** — log every output to MANIFEST.md
> - **[Output Language Protocol](../../shared-references/output-language.md)** — respect the project's language setting

## Key Rules

- **Large file handling**: If the Write tool fails due to file size, immediately retry using Bash (`cat << 'EOF' > file`) to write in chunks. Do NOT ask the user for permission — just do it silently.

- 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 (e.g., "NLP", "computer vision", "reinforcement learning"), STOP and ask them to narrow it.** A good direction is 1-2 sentences specifying the problem, domain, and constraint — e.g., "factorized gap in discrete diffusion LMs" or "sample efficiency of offline RL with image observations". Without sufficient specificity, generated ideas will be too vague to run experiments on.

## 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
/auto-review-loop             → iterate until submission-ready
```

## Review Tracing

After each `spawn_agent` or `send_input` reviewer call, save the trace following `../shared-references/review-tracing.md`. Include the reviewer route, saved agent id, prompt summary, raw output path, selected ideas, and rejected ideas.