idea-generation

Name: idea-generation
Author: lingzhi227

Generate novel research ideas with iterative refinement and novelty checking against literature. Score ideas on Interestingness, Feasibility, and Novelty. Use…

INSTALLATION

npx skills add https://github.com/lingzhi227/agent-research-skills --skill idea-generation

Run in your project or agent environment. Adjust flags if your CLI version differs.

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