idea-generation

name	idea-generation
description	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.
argument-hint	["research-area"]

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

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

{
  "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, deep-research
• Downstream: research-planning, experiment-design
• See also: novelty-assessment