Generate novel research ideas with iterative refinement and novelty checking against literature. Score ideas on Interestingness, Feasibility, and Novelty. Use…
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:
Generate 3-5 diverse research ideas
For each idea, provide: Name, Title, Experiment plan, and ratings
Use the ideation prompt templates from references
Step 2: Iterative Refinement (up to 5 rounds per idea)
For each idea:
Critically evaluate quality, novelty, and feasibility
Refine the idea while preserving its core spirit
Stop when converged ("I am done") or max rounds reached
Step 3: Novelty Assessment
For each promising idea:
Run novelty_check.py or manually search Semantic Scholar / arXiv
Use the novelty checking prompts from references
Multi-round search: generate queries, review results, decide
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
26:[don't have the plugin yet? install it then click "run inline in claude" again.