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research-idea-generation

Generate and score ranked LLM research ideas from sparse user goals.

Updated Aug 27, 2026
Search Tags:#research#ideation#llm#benchmarking#literature review#zero-spec
One-click install
npx skills add https://github.com/Aum08Desai/hermes-research-agent --skill research-idea-generation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-idea-generation
Source: https://github.com/Aum08Desai/hermes-research-agent/tree/main/skills/research/research-idea-generation
Command: npx skills add https://github.com/Aum08Desai/hermes-research-agent --skill research-idea-generation

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill addresses the challenge of starting new research projects with vague or open-ended goals by systematically generating and evaluating potential research directions.

Core Features & Use Cases

  • Literature Synthesis: Identifies limitations and gaps in current research.
  • Idea Generation: Proposes novel, testable research directions.
  • Scoring & Prioritization: Ranks ideas based on novelty, feasibility, cost, and evaluation clarity.
  • Use Case: When tasked with exploring "improving LLM reasoning," this skill can propose concrete research avenues like "developing novel attention mechanisms for multi-hop reasoning" or "creating a new benchmark for evaluating causal inference in LLMs."

Quick Start

Use the research-idea-generation skill to propose research directions for the goal of making LLMs more robust to adversarial attacks.

Frequently Asked Questions about research-idea-generation

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

FAQPage Schema
How do I generate testable LLM research ideas from a vague project goal?▼

To generate testable LLM research ideas from a vague goal, synthesize recent literature to identify evaluation gaps and propose ranked directions with theses, benchmarks, and cost assessments.

What is the best way to find evaluation gaps in current LLM literature?▼

Finding evaluation gaps in LLM literature involves synthesizing recent publications to identify limitations and prioritize novel, testable research directions based on feasibility and evaluation clarity.

Can I use this approach for zero-spec projects lacking a concrete dataset or benchmark?▼

Yes, this approach applies to zero-spec projects by proposing plausible, testable directions when a project lacks a concrete benchmark, dataset, or training recipe.

How are generated research ideas scored and prioritized?▼

Generated research ideas are scored and prioritized by ranking candidate directions based on novelty, feasibility, cost, evaluation clarity, and overall risk assessments.

What limitations exist when synthesizing literature to propose novel research directions?▼

A key limitation is that proposed research directions remain plausible but unverified hypotheses, requiring subsequent empirical validation to confirm the identified literature gaps and cost estimates.

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