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idea-generation

Generate and refine research ideas with literature-backed novelty checks.

Updated Apr 23, 2026
Search Tags:#ideation#generate#experiments#literature-search#research-planning#semantic-scholar#novelty-assessment
One-click install
npx skills add https://github.com/Embers-of-the-Fire/agent-research-skills-opencode --skill idea-generation-embers-of-the-fire
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: idea-generation
Source: https://github.com/Embers-of-the-Fire/agent-research-skills-opencode/tree/main/.opencode/skills/idea-generation
Command: npx skills add https://github.com/Embers-of-the-Fire/agent-research-skills-opencode --skill idea-generation-embers-of-the-fire

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Generates novel research ideas and provides an objective, literature-backed novelty assessment to prevent duplicated efforts and guide early-stage planning.

Core Features & Use Cases

  • Iterative ideation: produce 3-5 candidate ideas with names, titles, and experiment outlines, then refine them through multiple rounds guided by literature feedback.
  • Novelty vetting: run automated novelty checks against major scholarly corpora to surface related work and highlight overlaps.
  • Structured outputs: deliver decision-ready ideas with associated ratings (Interestingness, Feasibility, Novelty) and recommended next steps.

Quick Start

Describe a research area and run the idea-generation workflow to obtain top ideas with novelty scores.

Frequently Asked Questions about idea-generation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate research ideas and check their novelty against existing literature?▼

To generate research ideas and check novelty, you provide a research area description to trigger an iterative workflow that produces 3-5 candidate ideas with experiment outlines and runs automated novelty checks against scholarly corpora to highlight related work overlaps.

What is literature-backed novelty assessment for research planning?▼

Literature-backed novelty assessment is an automated process that compares generated research ideas against major scholarly corpora to surface related work, preventing duplicated efforts and delivering structured novelty ratings to guide early-stage research planning.

How do I use Semantic Scholar for automated novelty vetting in AI research?▼

Automated novelty vetting queries scholarly corpora to surface related work overlaps for candidate ideas. You describe an AI or ML research area, and the workflow integrates literature feedback to iteratively refine ideas and produce decision-ready novelty scores.

Can I get structured experimental outlines for multiple candidate research ideas?▼

Yes, the iterative ideation workflow produces 3-5 candidate research ideas complete with names, titles, and structured experiment outlines. It delivers decision-ready outputs with Interestingness, Feasibility, and Novelty ratings alongside recommended next steps.

Does this ideation workflow support domains outside of machine learning?▼

The ideation and novelty assessment workflow is designed to work across ML, AI, and related domains. You provide a research area description, and it generates candidate ideas with experiment outlines and literature-backed novelty ratings.

What are the limitations of automated novelty checks for research ideation?▼

Automated novelty checks surface related work overlaps from major scholarly corpora to guide early-stage planning, but they serve as an objective assessment aid rather than a definitive guarantee of absolute novelty, requiring human review of highlighted literature overlaps.

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