idea-generation

Original：🇺🇸 English

Translated

1 scriptsChecked / no sensitive code detected

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.

2installs

Sourcelingzhi227/claude-skills

Added on2026-02-20

NPX Install

npx skill4agent add lingzhi227/claude-skills idea-generation

SKILL.md Content

View Translation Comparison →

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:

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

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