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Nanoresearch Ideation

openraiser/nanoresearch

You give it a research topic and it fans out across arXiv and Semantic Scholar with multiple query angles, pulls back relevant papers, then does gap analysis to suggest novel hypotheses. The output is a JSON file with everything from paper metadata to a ranked hypothesis with justification. It's useful when you're starting a research project and need to map the landscape quickly, though the hypothesis quality will depend heavily on how well you frame the initial topic. The multi-query approach is smart since academic search can be finicky about phrasing. Think of it as a research assistant that reads widely and connects dots, not a replacement for your own domain expertise.

GitHub stars
1.5K
last updated
May 26, 2026
View on GitHub

Install

$ npx -y skills add openraiser/nanoresearch --skill nanoresearch-ideation --agent claude-code

Installs into .claude/skills of the current project.

Files

Ideation Skill

Purpose

Search arXiv and Semantic Scholar for papers related to a research topic, perform gap analysis, and generate novel hypotheses.

Tools Required

  • search_arxiv: Search arXiv for papers
  • search_semantic_scholar: Search Semantic Scholar for papers and citations

Input

  • topic: The research topic or question to investigate

Process

  1. Generate 5-8 diverse search queries from the topic
  2. Search arXiv and Semantic Scholar using each query
  3. Deduplicate and rank papers by relevance
  4. Analyze the collected papers to identify research gaps
  5. Generate 2-4 novel hypotheses that address the identified gaps
  6. Select the most promising hypothesis with justification

Output

Produces papers/ideation_output.json containing:

  • Retrieved papers with metadata
  • Survey summary
  • Gap analysis
  • Generated hypotheses
  • Selected hypothesis with rationale
SKILL.md on GitHub

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Details

Repository
github.com/openraiser/nanoresearch
Category
Data Science & ML
License
MIT
First seen
Jun 11, 2026

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