idea-generation

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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.

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NPX Install

npx skill4agent add lingzhi227/agent-research-skills idea-generation

SKILL.md Content

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