Claude Skill

modelscope/Awesome-Vibe-Research

Awesome-Vibe-Research is an open, collaboratively-built repository curating AI agents, skills, workflows, tools, and best practices for the full scientific research lifecycle.

Overview

Stars303
Forks25
LanguagePython
Last pushed2026-07-03
Last synced2026-07-03
View on GitHub

Repository

Ownermodelscope
RepositoryAwesome-Vibe-Research
Full namemodelscope/Awesome-Vibe-Research
Repo ID1,267,423,357

Install this Skill

git clone https://github.com/modelscope/Awesome-Vibe-Research.git

Registry

Typemcp_server
Quality score70/100
Verificationreadme_parsed
Last verified2026-07-01
Platforms
ClaudeMCPOpenClawCodex
Capabilities
memorysearchimageterminalworkflow
Detected files
README.md

Summary

Awesome-Vibe-Research is an open, collaboratively-built repository by modelscope that collects and curates AI agents, skills, workflows, tools, and best practices for the full scientific research lifecycle. It aims to accelerate AI-assisted research across disciplines.

Chinese description

一个面向AI辅助科研的开放共建仓库——收集和沉淀科研全生命周期中的agents、skills、workflows、tools与最佳实践。

Key features

  • Open collaborative curation of AI research resources
  • Covers full research lifecycle from ideation to publication
  • Includes agents, skills, workflows, and tools
  • Community-driven best practices for AI-assisted science

Use cases

  • Accelerating literature review and hypothesis generation
  • Automating experiment design and data analysis
  • Streamlining paper writing and peer review preparation
  • Facilitating interdisciplinary research collaboration

README excerpt

<p align="center"> <img src="assets/cover.png" alt="Awesome Vibe Research" width="800"/> </p> <h1 align="center">🔬 Awesome Vibe Research</h1> <p align="center"> <em>面向 AI 辅助科研的开放共建仓库</em><br/> 收集和沉淀科研全流程中的 agents、skills、workflows、tools 与最佳实践 </p> <p align="center"> <strong>🇨🇳 中文版</strong> | <a href="./README_en.md">🇬🇧 English</a> </p> <p align="center"> <a href="#0--全流程端到端自动科研">🧪 全流程</a> • <a href="#1--方向扫描与问题定义">🔭 方向扫描</a> • <a href="#2--文献研究检索精读综述与引用网络">📚 文献研究</a> • <a href="#3--方法设计">🧩 方法设计</a> • <a href="#4--实验执行与分析">⚗️ 实验执行</a> • <a href="#5--科学可视化论文插图科学绘图与可视化表达">📊 可视化</a> • <a href="#6--论文写作投稿与同行评审">✍️ 写作</a> • <a href="#7--复现发布与归档">📦 复现发布</a> • <a href="#8--传播教学与影响力分析">📡 传播</a> • <a href="#-如何贡献">🤝 贡献</a> </p> --- 我们关注能在科研工作中**反复复用**、能被同行**验证**、能逐步**演化**、表现**最优**的 AI 辅助能力。 本仓库从「与 Agent 共事做研究」系列沙龙出发,希望把 speaker 的实践经验、观众的使用反馈、开源项目与可复用流程沉淀成一个社区可维护的知识库。 --- ## 🗺️ 科研流程地图 下表是我们对科研生命周期的 **9 阶段**拆分。每个阶段列出了"典型问题"和"可沉淀的 AI 辅助组件类型"。正文按阶段展开条目表,收录已知最好的项目、skill、workflow。 > 💡 **如果你觉得某个阶段的条目缺失或可以补充——直接在对应表格中添加一行。** | 阶段 | 典型问题 | 范围 | |:---:|---|---| | 🔄 0.全流程 | 覆盖以下多个环节和问题 | — | | 🔭 1. 方向扫描和问题定义 | 这个领域最近发生了什么?什么问题值得做、可做、能验证? | trend scanner、paper radar、venue tracker;idea generator、novelty checker、hypothesis workflow | | 📚 2. 文献研究 | 相关工作怎么找、读、比、写? | literature review workflow、paper reading skill、citation graph agent | | 🧩 3. 方法设计 | 方案、实验和评价指标如何设计? | experiment design skill、ablation planner、protocol checker | | ⚗️ 4. 实验执行与分析 | 如何写代码、跑实验、记录失败?结果是否可信,误差来自哪里? | experiment runner、statistical analysis skill、failure analysis workflow、robustness checker | | 📊 5. 可视化 | 图表是否讲清楚了科学问题? | figure generation agent、visualization critique skill | | ✍️ 6. 论文写作 | 如何组织论文、引用、补实验? | paper writing workflow、citation verifier、rebuttal assistant | | 📦 7. 复现发布 | 如何让别

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Data from GitHub. Synced on 2026-07-03