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Semi-automated assistant for research, coding, experiments, and publication.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "claude-scholar" yet — see the docs or source repo.
Please outline research ideas for “multimodal models in medical imaging,” including possible research questions, related directions, and next experimental steps.
A list of research questions, direction breakdowns, and actionable experiment ideas.
I want to build a Python prototype for a paper experiment. Please break down the implementation steps and suggest a module design.
Implementation steps, module breakdown, and development suggestions.
Based on the research points below, please turn them into an abstract and introduction outline: …
A draft abstract and an outline for the introduction.
Useful for researchers or students at the start of a project to organize directions, problem statements, and experiment ideas. It also helps turn scattered thoughts into a clearer research plan.
Useful for developers turning research ideas into runnable prototypes, helping with module breakdown, implementation planning, and experiment execution.
Useful during writing to organize abstracts, introductions, and paper structure, making experimental results and research narratives clearer.
It is a semi-automated research assistant for academic research and software development, covering ideation, coding, experiments, writing, and publication. It works with Claude Code, Codex CLI, Kimi Code CLI, and OpenCode.
The description explicitly supports Claude Code, Codex CLI, Kimi Code CLI, and OpenCode. No other compatibility details are provided.
The provided information only says it works with those CLI tools; installation, configuration, or key requirements are not specified.
Collaborate across AI models for C# development with memory and brainstorming support.
Provides AI workflows for literature review, research design, writing, and delegation.
Discover Claude Code tools and resources to speed up developer workflows.
Understand your codebase in terminal and accelerate coding with natural language.
Equip any AI model with open-source research and engineering skills.
Search local Markdown notes offline with hybrid semantic and keyword retrieval.