| 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:
- 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
