Agent skill

nanoresearch-ideation

Search academic literature and generate research hypotheses

OpenRaisergithub.com/OpenRaiserGitHub ↗
claude-codeMIT
Install
npx skills add OpenRaiser/NanoResearch --skill nanoresearch-ideation --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 1
SKILL.md size: 1 KB
Bundled scripts: none
Version: 0.1.0
Path: skills/nanoresearch-ideation/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,480
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# 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

What's inside
Steps it walks through
  1. Purpose
  2. Tools Required
  3. Input
  4. Process
  5. Output
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About this skill
What does the nanoresearch-ideation skill do?

Search academic literature and generate research hypotheses

How do I install it?

Run `npx skills add OpenRaiser/NanoResearch --skill nanoresearch-ideation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From OpenRaiser/NanoResearch, a repository with 1,480 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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