ARIA - Automated Research Intelligence Assistant

🚀 Transform months of research into minutes of insight

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ARIA is an automated research assistant framework for scientific data analysis, visualization, and report generation.

Project Structure

aria/
├── .claude/commands/           # Claude AI command files
│   ├── academic/               # Academic workflow commands
│   ├── git/                    # Git operation commands
│   └── python/                 # Python environment commands
├── data/
│   ├── raw/                   # Original data
│   ├── processed/              # Preprocessed data
│   └── output/                 # Experiment outputs
│       ├── models/             # Trained models
│       ├── results/            # Experimental results
│       ├── figures/            # Visualization charts
│       └── logs/               # Execution logs
├── docs/                       # Project documentation
├── src/                        # Source code
├── scripts/                    # Execution scripts
├── GETTING_STARTED.md          # Quick start guide
├── INSTALL.md                  # Installation guide
└── README.md                   # This file

Workflow

1. Data Preparation

• Place raw data in the data/raw/ folder
• Manually create docs/01-basic-information.md to describe project background, objectives, and data overview

2. Data Analysis

• Raw Data Analysis: Use @raw-data-analysis.md command to analyze raw data, generating docs/02-raw-data-analysis.md
• Data Preprocessing: Use @preprocess.md command to design preprocessing plan (docs/03-preprocess-plan.md), execute preprocessing, analyze processed data (docs/04-processed-data-analysis.md)

3. Research Design

• Research Plan: Use @research-plan.md command to develop research plan including feature engineering, model selection, evaluation metrics, generating docs/05-research-plan.md

4. Code Implementation

• Code Development: Use @code-implementation.md command to implement research plan, creating necessary Python modules
• Generate docs/06-implementation-docs.md (implementation documentation) and docs/07-execution-instructions.md (execution guide)
• Code quality check: Use mypy and ruff to ensure code quality

5. Experiment Execution

• Run Experiments: Use @run-experiments.md command to execute experiment scripts
• Outputs saved to corresponding subfolders in output/ directory

6. Results Analysis

• Results Analysis: Use @experiment-analysis.md command to analyze experiment outputs
• Generate individual analyses in docs/08-experiment-results/ directory
• Generate docs/09-experiment-report.md comprehensive experiment report

7. Paper Writing

• Academic Paper: Use @research-report.md command to generate high-impact journal format paper
• Generate docs/10-manuscript.md (main text), docs/10-manuscript-supplement.md (supplementary materials), docs/10-cover-letter.md (cover letter)

8. Model Deployment (Optional)

• Gradio Interface: If models are trained, use @gradio-app.md command to create model inference interface
• Generate docs/11-model-deployment-guide.md deployment guide

Claude AI Commands

Academic Commands

All academic command files are located in .claude/commands/academic/ directory:

• @raw-data-analysis.md - Analyze raw data
• @preprocess.md - Data preprocessing
• @research-plan.md - Research plan design
• @code-implementation.md - Code implementation
• @run-experiments.md - Experiment execution
• @experiment-analysis.md - Results analysis
• @research-report.md - Academic paper generation
• @gradio-app.md - Model deployment interface

Python Environment Commands

Python command files are located in .claude/commands/python/ directory:

• @setup-environment.md - Automated environment setup
  • Checks and installs Git, Python 3.12+, and UV package manager
  • Sets up project dependencies with uv sync
  • Includes Tsinghua mirror fallback for network issues
  • Provides complete environment verification

Git Commands

Git command files are located in .claude/commands/git/ directory:

• @git-commit.md - Intelligent Git commits
  • Automatically creates structured commit messages
  • When there are many modified files, automatically commits in batches (max 10 files per commit)
  • Generates meaningful commit descriptions based on code changes

Example Projects

Complete example projects demonstrating ARIA workflow across different research tasks:

• 🏠 aria-example-buston - Boston housing price prediction (regression) | OpenML Dataset
• 💎 aria-example-diamonds - Diamond price prediction (regression) | OpenML Dataset
• 🐛 aria-example-kc1 - Software defect prediction (classification) | OpenML Dataset
• 🧩 aria-example-sat11 - SAT solver performance prediction (regression) | OpenML Dataset

All datasets are from OpenML, an open machine learning platform. Each project includes complete documentation, production-ready code, trained models, and academic manuscripts.

Quick Start

New to ARIA? Start here: Getting Started Guide 📖

The complete guide covers:

• Installing Git and AI code editor (Cursor/VSCode/Lingma IDE)
• Automated environment setup with @setup-environment.md
• Step-by-step workflow from data to paper
• Troubleshooting and best practices

For Experienced Users

1. Setup Environment: Use @setup-environment.md to configure Python and dependencies
2. Prepare Data: Place raw data in data/raw/
3. Create Project Description: Write docs/01-basic-information.md
4. Execute Workflow: Use Claude AI to execute academic commands in sequence
5. Version Control: Use @git-commit.md for intelligent commits

📚 Full Documentation: See GETTING_STARTED.md and INSTALL.md

Dependency Management

Using UV for Python dependency management:

uv add <package-name>  # Add new dependency
uv sync                # Synchronize dependencies

Code Quality

• Type checking: mypy src/
• Code style: ruff check src/
• Code formatting: ruff format src/

Features

• 📊 Data-Driven: Complete workflow from raw data to academic paper
• 🤖 AI-Assisted: Claude AI commands automate each research phase
• 📝 Academic Standards: Generate Nature/Science standard papers
• 🎯 Quality Assurance: Integrated code quality checking tools
• 🚀 Model Deployment: Support for Gradio interface rapid deployment
• 📦 Version Control: Intelligent Git commit management

License

This project is dual-licensed:

• GNU AGPL-3.0 - for open source use (personal, research, education, open source projects)
• Commercial License - for proprietary/commercial use (contact for licensing)

See the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit Issues and Pull Requests.

Contact

For questions, suggestions, or collaboration opportunities:

• GitHub Issues: Create an issue
• Email: [email protected]
• Project: ARIA on GitHub

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