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NanoResearch

Skill Development Solid
Works inClaude Code
Solid Scanned — metadata only

Reduces time spent on literature review and technical research by automating information gathering and synthesis, allowing engineers to focus on implementati…

End-to-end autonomous AI research engine that takes a research idea through a 9-stage pipeline to a finished LaTeX paper. It actually runs the computational experiments — generating code, executing it locally or on SLURM GPU clusters, collecting real results, and producing figures — so the paper's numbers come from runs, not LLM invention. Includes a Claude Code mode driven by /project:research slash commands.

1,379 starsMIT (commercial OK)FreeQuick setup

Reduces time spent on literature review and technical research by automating information gathering and synthesis, allowing engineers to focus on implementation and architectural decisions.

Engineering teams evaluating emerging technologies or building POCs under tight deadlines.

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https://github.com/OpenRaiser/NanoResearch

By OpenRaiser

How to Get It

Option 1: Claude Desktop App (Code Mode)Click the + button next to the prompt box → PluginsAdd plugin. Search and click Install. Skills work in Claude Code only.
Option 2: Paste into Claude CodeCopy the command below and paste it into your conversation. Claude will install it.
Command
git clone https://github.com/OpenRaiser/NanoResearch.git && cd NanoResearch && pip install -e ".[dev]"

Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.

First thing to try

After installing, paste this into Claude:

Search and summarize recent research papers on a specific technical topic quickly
PrerequisitesPython 3.10+; an OpenAI-compatible API endpoint configured in ~/.nanoresearch/config.json; local GPU or SLURM cluster access for experiment executionCostFree

Trust Signals Auto-scanned

Stars1,379Contributors7Last updated2026-05-26LicenseMIT (OK for commercial use)Known CVEsNone foundSources: GitHub Advisory Database + OSV.dev · Scanned 2026-08-16 · scanner vattempted-no-data

Reviewer notes

Auto-scanned review. These are observations, not a security certification.

catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 1,379 GitHub stars; 7 contributors; last commit 84d ago; license MIT.

Things to check

  • Documentation is primarily in Chinese (an English README is provided). Full pipeline runs require an OpenAI-compatible API endpoint and, for the experiment stages, GPU or SLURM cluster access — results depend on real compute, not just an API key.

How to evaluate tools before deploying →

Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.

Evaluation

Ease of Use
4/5
Versatility
4/5
Reliability
4/5
Security
3/5
Overall score3.85 / 5.00 SolidEvaluatedAug 2026
catalog_hygiene stale-eval refresh: Scored from trust signals (evidence-eval-v1): 1,379 GitHub stars; 7 contributors; last commit 84d ago; license MIT.

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