Supercharge Claude Code with a specialized team of AI agents for data analysis, machine learning, visualization, and data science workflows.
This project is experimental and token-intensive. The data science agents are designed for complex analytical workflows and may consume significant tokens during data exploration and modeling phases.
- Claude Code CLI installed and authenticated
- Claude subscription - required for intensive analytical workflows
- Active project directory with your data
- Python environment (3.8+) with essential data science libraries
Quick Setup:
# Option 1: Install with pip
pip install pandas numpy matplotlib seaborn scikit-learn plotly jupyter
# Option 2: Use our complete requirements.txt
pip install -r examples/requirements.txt
# Option 3: Use conda environment
conda env create -f examples/environment.yml
conda activate data-science-agentsgit clone https://github.com/StanleyChanH/awesome-claude-data-agents.gitmacOS/Linux:
# Create agents directory if it doesn't exist
mkdir -p ~/.claude/agents
# Symlink the data science agents collection
ln -sf "$(pwd)/awesome-claude-data-agents/agents/" ~/.claude/agents/awesome-claude-data-agentsWindows (PowerShell):
# Create agents directory
New-Item -Path "$env:USERPROFILE\.claude\agents" -ItemType Directory -Force
# Create symlink
cmd /c mklink /D "$env:USERPROFILE\.claude\agents\awesome-claude-data-agents" "$(Get-Location)\awesome-claude-data-agents\agents"# Create agents directory if it doesn't exist
mkdir -p ~/.claude/agents
# Copy all agents
cp -r awesome-claude-data-agents/agents ~/.claude/agents/awesome-claude-data-agentsclaude /agents
# Should show all data science agents.Navigate to your project directory and run:
claude "use @data-team-configurator and analyze my project to set up the optimal data science team."claude "use @data-science-orchestrator and analyze this dataset to build a predictive model"Your AI data science team will automatically detect your data and use the right specialists!
- Stay Updated: Get notified of new agents, features, and improvements
- Show Support: Help us grow the AI data science community
- Community Trust: Stars indicate trust and encourage others to use this project
- Motivation: Your stars motivate us to keep building amazing AI agents!
If you find this project helpful, please consider giving it a β - it only takes a second but makes a huge difference!
The @data-team-configurator automatically sets up your optimal AI data science team. When invoked, it:
- Locates project structure - Finds existing configuration and preserves your custom content
- Detects Data Environment - Inspects requirements.txt, pyproject.toml, data files, and notebooks
- Discovers Available Agents - Scans for data science specialized agents
- Selects Specialists - Prefers domain-specific agents over universal ones
- Updates configuration - Creates optimal agent mappings
- Provides Usage Guidance - Shows detected data types and sample commands
- Data Science Orchestrator - Senior data scientist who coordinates complex analytical projects and multi-step workflows
- Data Analyst - Data exploration and statistical analysis specialist
- Team Configurator - AI team setup expert for data science projects
- Statistical Analyst - Statistical tests, hypothesis testing, and experimental design
- Data Cleaner - Data preprocessing, missing values, and data quality
- Feature Engineer - Feature selection, creation, and transformation
- Time Series Analyst - Time series analysis, forecasting, and temporal patterns
- Data Explorer - Exploratory data analysis and pattern discovery
- SQL Analyst - Database queries and data extraction optimization
- ML Engineer - End-to-end machine learning pipeline development (includes hyperparameter tuning)
- Model Validator - Model evaluation, cross-validation, and performance metrics
Future ML agents: Deep Learning, NLP, Computer Vision, Ensemble Methods, MLOps
- Data Visualizer - General data visualization and chart creation (includes reporting and dashboards)
Future visualization agents: Interactive Dashboards, Statistical Plots, Report Design
- Code Reviewer (Data Science) - Data science code quality and best practices
Future core agents: Data Archaeologist, Documentation Specialist
Total: 13 specialized data science agents working together to analyze your data!
Note: This is the initial release with core agents. Additional specialized agents (deep learning, NLP, computer vision, etc.) will be added in future releases.
- Domain Expertise: Each agent masters specific Python libraries and analytical techniques
- Methodical Approach: Agents follow Python data science best practices and statistical principles
- Comprehensive Analysis: Multiple specialists cover all aspects of the Python data science lifecycle
- Quality Assurance: Built-in validation and review processes ensure robust, reproducible Python code
- Deeper Insights - Discover patterns and relationships you might miss
- Robust Models - Build machine learning models that perform reliably
- Faster Analysis - Complete analytical workflows in minutes, not days
- Better Decisions - Make data-driven decisions with confidence
- Creating Custom Data Science Agents - Build specialists for your analytical needs
- Data Science Best Practices - Get the most from your AI data team
- Agent Relationships and Orchestration - Understanding agent coordination
- Customer Churn Analysis Example - Complete Python workflow example
- Python Environment Setup - Complete Python dependencies
- Conda Environment - Alternative conda setup
- β Give this repo a star - It helps more people discover the project!
- π Report issues - Found a bug? Let us know!
- π‘ Share ideas - Have suggestions? We'd love to hear them!
- π Success stories - Show us what you've built!
Star History: Every star counts and helps us reach more developers who need AI-powered data science tools!
MIT License - Use freely in your projects!
Transform Claude Code into an AI data science team that delivers analytical insights
Specialized expertise. Comprehensive analysis. Actionable insights.
GitHub β’ Documentation β’ Community