Idea Generation Skill for Claude
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.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/lingzhi227/agent-research-skills --skill idea-generationThe instruction itself
13 sections, as written by the authorIdea 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.pyor 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
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How to use it
Take lingzhi227/idea-generation from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.