idea-generation

SKILL.md

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:[