经 AI Skill Hub 精选评估,零纸AI研究工作流 获评「推荐使用」。这款Agent工作流在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 7.0 分,适合有一定技术背景的用户使用。
零纸AI研究工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
零纸AI研究工作流 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。
# 方式一:pip 安装(推荐)
pip install zeropaper
# 方式二:虚拟环境安装(推荐生产环境)
python -m venv .venv
source .venv/bin/activate # Windows: .venv\Scripts\activate
pip install zeropaper
# 方式三:从源码安装(获取最新功能)
git clone https://github.com/alejandroll10/zeropaper
cd zeropaper
pip install -e .
# 验证安装
python -c "import zeropaper; print('安装成功')"
# 命令行使用
zeropaper --help
# 基本用法
zeropaper input_file -o output_file
# Python 代码中调用
import zeropaper
# 示例
result = zeropaper.process("input")
print(result)
# zeropaper 配置文件示例(config.yml) app: name: "zeropaper" debug: false log_level: "INFO" # 运行时指定配置文件 zeropaper --config config.yml # 或通过环境变量配置 export ZEROPAPER_API_KEY="your-key" export ZEROPAPER_OUTPUT_DIR="./output"
Autonomous research paper generator. Set up a project, launch Claude Code, Codex, or Gemini CLI, walk away. The system discovers a problem, generates a theory, verifies it adversarially, and writes a publication-ready paper.
```bash
If you already have Claude Code installed, open it in any empty folder and paste this in:
Set up an autonomous finance research project in this folder.
1. Clone https://github.com/alejandroll10/zeropaper into a temp location
2. From there, run ./setup.sh my-paper --variant finance
(or --variant finance --ext empirical if I want CRSP/Compustat data)
3. Move the resulting my-paper/ folder here
4. Check that I have the prerequisites installed (python3, uv, git; bubblewrap on Linux).
If anything is missing, walk me through installing it on my machine (Mac or Linux).
5. When setup is done, tell me to cd into my-paper and say "Run the pipeline."
Claude Code will handle the clone, setup, and prereq checks for you. Works on Mac and Linux.
git config --global user.email "you@example.com" git config --global user.name "Your Name" ```
nano .env ```
| Extension | Credentials needed |
|---|---|
--ext empirical | FRED_API_KEY (free, from [FRED](https://fred.stlouisfed.org/docs/api/api_key.html)), WRDS_USER + WRDS_PASS (from [WRDS](https://wrds-www.wharton.upenn.edu/)) |
--ext theory_llm | UF_API_KEY (from [UF NaviGator](https://api.ai.it.ufl.edu)) |
my-paper/
├── CLAUDE.md # Claude Code orchestration (assembled by setup.sh)
├── AGENTS.md # Codex orchestration (assembled by setup.sh)
├── GEMINI.md # Gemini CLI orchestration (assembled by setup.sh)
├── .env # API keys (gitignored)
├── dashboard.html # Live progress dashboard
├── .claude/
│ ├── settings.json # Sandbox config
│ ├── agents/ # Claude subagents (.md)
│ └── skills/ # Claude skills
├── .codex/
│ └── agents/ # Codex custom agents (.toml)
├── .gemini/
│ ├── settings.json # Gemini config
│ └── agents/ # Gemini subagents (.md)
├── .agents/
│ └── skills/ # Shared skills (Codex + Gemini)
├── output/ # Pipeline outputs by stage
├── paper/ # LaTeX paper
│ ├── main.tex
│ ├── sections/
│ └── simulated_referee_reports/
├── code/
│ ├── analysis/ # Analysis and verification scripts
│ ├── download/ # Data download helpers
│ ├── explore/ # Exploration scripts and diagnostics
│ ├── tmp/ # Scratch files
│ └── utils/ # Utility scripts (including codex-math; more with extensions)
└── process_log/
├── pipeline_state.json # Current stage, scores, history
└── history.md
```bash cd my-paper
curl -LsSf https://astral.sh/uv/install.sh | sh
./setup.sh my-paper --variant finance --ext empirical --ext theory_llm
./setup.sh my-toolkit --manual
| Extension | Flag | What it adds |
|---|---|---|
| **empirical** | --ext empirical | Stage 3a: empirical analysis with real data (CRSP, Compustat, FRED, Ken French, Chen-Zimmerman, WRDS) |
| **theory_llm** | --ext theory_llm | Stage 3b: test predictions via LLM experiments using gpt-oss models (UF NaviGator) |
Extensions are additive and combinable — they inject extra agents and skills without changing the core pipeline. Use multiple --ext flags to combine them.
Stage 0: Problem Discovery → Gate 0: Problem Viability
Stage 1: Idea Generation → Gate 1: Idea Review (iterates)
Gate 1b: Novelty Check on idea
Gate 1c: Idea Prototype (tractability)
Stage 2: Theory Development → Gate 2: Math Audit (structured + free-form)
Gate 3: Novelty Check on theory
Stage 2b: Theory Exploration (compute, verify, plot)
Gate 3a-feasibility: Empirical Feasibility (optional)
Stage 3: Implications
Stage 3a: Full Empirical Analysis (optional, if --ext empirical)
Stage 3b: LLM Experiments (optional, if --ext theory_llm)
Stage 4: Self-Attack → Gate 4: Scorer Decision
Stage 5: Paper Writing
Stage 6: Referee Simulation → Gate 5: Referee Decision
Stage 7: Style Check
Stage 8: Bibliography Verify
Stage 9: Polish → Done (eight parallel polish agents — consistency,
formula, numerics, institutions, equilibria,
identification, bibliography, prose — triaged
+ applied; max 2 rounds)
Each gate is adversarial. Failed theories get revised, reworked, or abandoned. The system loops until it produces a paper that passes simulated referee review.
创新的AI工作流框架,代理能力设计合理。代码质量和文档完整性有待提升,适合早期采用者和二次开发集成。
该工具使用 NOASSERTION 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。
AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。
建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。
📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。
AI Skill Hub 点评:零纸AI研究工作流 的核心功能完整,质量良好。对于自动化工程师和运维人员来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 原始名称 | zeropaper |
| 原始描述 | 开源AI工作流:Enhanced AI-assisted research template with agentic capabilities。⭐50 · Python |
| Topics | AI工作流代理系统研究工具Python开源 |
| GitHub | https://github.com/alejandroll10/zeropaper |
| License | NOASSERTION |
| 语言 | Python |
收录时间:2026-05-25 · 更新时间:2026-05-30 · License:NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。
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