Our AI co-author, from research problem to final publication.
ResearchStudio is a collection of skills covering the entire research lifecycle — from the moment you have a vague research direction to the moment your paper goes public. These skills are supported by Claude Code and Codex.
Presenting an all-in-one interactive reel.
IdeaSpark runs the full pipeline end-to-end.
- [Preview] ResearchStudio-Reel is coming soon — the post-paper half: turn a finished paper PDF into the artifacts a publication needs — a print-ready poster, a narrated walkthrough video, a bilingual blog post, and an interactive reel viewer.
- [2026-07-03] ResearchStudio-Idea is released — the pre-paper half: turn an under-specified research direction into a reviewer-defensible, implementable idea, grounded in an empirical taxonomy induced from a large-scale papers of ICLR / ICML / NeurIPS submissions.
Option 1 — clone and run install.sh (symlinks the skills from a local clone and scaffolds .env):
git clone https://github.com/microsoft/ResearchStudio.git && cd ResearchStudio
bash install.shOption 2 — interactive npx installer (choose which plugins to install, enter your API keys when prompted, and it writes .env for you):
npx github:microsoft/ResearchStudioWe recommend utilizing these skills with models versions claude-opus-4.6 and gpt-5.5.
If ResearchStudio helps your research workflow, please cite:
@article{xiao2026researchstudioreel,
title = {ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog},
author = {Lingao Xiao and Yalun Dai and Yangyu Huang and Qihao Zhao and Wenshan Wu and Hugo He and Ruishuo Chen and Jin Jiang and Qianli Ma and Jiahuan Zhang and Xin Zhang and Ying Xin and Yang Ou and Yan Xia and Scarlett Li and Longbo Huang and Zhipeng Zhang and Yang He and Yap Kim Hui and Yan Lu},
journal = {arXiv preprint arXiv:2607.04438},
year = {2026},
url = {https://arxiv.org/abs/2607.04438}
}
@article{zhao2026researchstudioidea,
title = {ResearchStudio-Idea: An Evidence-Grounded Research-Ideation Skill Suite from ML Conference Outcomes},
author = {Qihao Zhao and Yangyu Huang and Yalun Dai and Lingao Xiao and Jianjun Gao and Xin Zhang and Wenshan Wu and Scarlett Li and Yang He and Yan Lu and Yap Kim Hui},
journal = {arXiv preprint arXiv:2607.04439},
year = {2026},
url = {https://arxiv.org/abs/2607.04439}
}

