idea-generation（想法产生）

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