# Idea Generation

> Generate novel research ideas with iterative refinement and novelty checking against literature. Score ideas on Interestingness, Feasibility, and Novelty. Use when brainstorming research directions or validating idea novelty.

- Skill: `om-scogo/idea-generation` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds add om-scogo/idea-generation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/om-scogo/idea-generation/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: om-scogo (https://skillmd.com/u/om-scogo)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/om-scogo/idea-generation

---


# Idea Generation

Generate and refine novel research ideas with literature-backed novelty assessment.

## Input

- `$0` — Research area, task description, or existing codebase context
- `$1` — Optional: additional context (e.g., "for NeurIPS", constraints)

## Scripts

### Novelty check against Semantic Scholar
```bash
python ~/.claude/skills/idea-generation/scripts/novelty_check.py \
  --idea "Adaptive attention head pruning via gradient-guided importance" \
  --max-rounds 5
```

Performs iterative literature search to assess if an idea is novel.

## References

- Ideation prompts (generation, reflection, novelty): `~/.claude/skills/idea-generation/references/ideation-prompts.md`

## Workflow

### Step 1: Generate Ideas
Given a research area and optional code/paper context:
1. Generate 3-5 diverse research ideas
2. For each idea, provide: Name, Title, Experiment plan, and ratings
3. Use the ideation prompt templates from references

### Step 2: Iterative Refinement (up to 5 rounds per idea)
For each idea:
1. Critically evaluate quality, novelty, and feasibility
2. Refine the idea while preserving its core spirit
3. Stop when converged ("I am done") or max rounds reached

### Step 3: Novelty Assessment
For each promising idea:
1. Run `novelty_check.py` or manually search Semantic Scholar / arXiv
2. Use the novelty checking prompts from references
3. Multi-round search: generate queries, review results, decide
4. Binary decision: Novel / Not Novel with justification

### Step 4: Rank and Select
- Score each idea on three dimensions (1-10): Interestingness, Feasibility, Novelty
- Be cautious and realistic on ratings
- Select the top idea(s) for development

## Output Format

```json
{
  "Name": "adaptive_attention_pruning",
  "Title": "Adaptive Attention Head Pruning via Gradient-Guided Importance Scoring",
  "Experiment": "Detailed implementation plan...",
  "Interestingness": 8,
  "Feasibility": 7,
  "Novelty": 9,
  "novel": true,
  "most_similar_papers": ["paper1", "paper2"]
}
```

## Rules

- Ideas must be feasible with available resources (no requiring new datasets or massive compute)
- Do not overfit ideas to a specific dataset or model — aim for wider significance
- Be a harsh critic for novelty — ensure sufficient contribution for a conference paper
- Each idea should stem from a simple, elegant question or hypothesis
- Always check novelty before committing to an idea

## Related Skills
- Upstream: [literature-search](../literature-search/), [deep-research](../deep-research/)
- Downstream: [research-planning](../research-planning/), [experiment-design](../experiment-design/)
- See also: [novelty-assessment](../novelty-assessment/)