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AI Agentic Data Stack Framework - Community Edition. Open source data engineering framework with 4 core agents, essential templates, and 3-dimensional quality validation.
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# AI Agentic Data Stack Framework Knowledge Base
## Overview
The AI Agentic Data Stack Framework (ADSF) is an advanced multi-agent orchestration system that transforms how data teams work by providing specialized AI agents for each role in the data engineering workflow.
### Key Features
- **Multi-Agent System**: Specialized AI agents for each data role (Data Engineer, Analyst, Product Manager, Quality Engineer)
- **Advanced Elicitation**: 12+ sophisticated methods for requirements gathering and refinement
- **Progressive Disclosure**: Step-by-step document creation with interactive refinement
- **Document Management**: Automatic sharding and knowledge base integration
- **Workflow Orchestration**: Complex multi-agent workflows with handoffs and validation
### When to Use ADSF
- **Data Pipeline Development**: Building ETL/ELT pipelines with best practices
- **Data Analysis Projects**: Comprehensive analysis with business insights
- **Data Quality Initiatives**: Implementing robust quality checks and monitoring
- **Team Collaboration**: Multiple roles working together on data projects
- **Documentation**: Creating PRDs, architecture docs, and data contracts
## How ADSF Works
### The Core Method
ADSF enables you to direct a team of specialized AI agents through structured workflows:
1. **You Direct, AI Executes**: You provide vision and requirements; agents handle implementation details
2. **Specialized Agents**: Each agent masters one data role (Engineer, Analyst, Product Manager, Quality Engineer)
3. **Structured Workflows**: Proven patterns guide you from requirements to deployed pipelines
4. **Quality Validation**: Built-in 3-dimensional validation ensures high-quality outputs
### Agent Roles
#### Data Engineer (@data-engineer)
- Builds data pipelines and infrastructure
- Implements ETL/ELT processes
- Sets up monitoring and alerting
- Handles data modeling and optimization
#### Data Analyst (@data-analyst)
- Performs data analysis and exploration
- Creates dashboards and visualizations
- Generates business insights
- Conducts segmentation and cohort analysis
#### Data Product Manager (@data-product-manager)
- Gathers requirements from stakeholders
- Creates data contracts and specifications
- Defines metrics and KPIs
- Manages data product roadmap
#### Data Quality Engineer (@data-quality-engineer)
- Implements quality checks and validation
- Profiles data for anomalies
- Sets up monitoring frameworks
- Ensures data reliability
## Getting Started
### Quick Start
1. **Activate an Agent**:
```
@data-engineer
```
2. **View Available Commands**:
```
*help
```
3. **Execute a Task**:
```
*task build-pipeline
```
4. **Create Documentation**:
```
*create-doc data-contract
```
### Common Workflows
#### Building a Data Pipeline
1. Start with requirements gathering:
```
@data-product-manager
*task gather-requirements
```
2. Create data contract:
```
*create-doc data-contract
```
3. Build the pipeline:
```
@data-engineer
*task build-pipeline
```
4. Implement quality checks:
```
@data-quality-engineer
*task implement-quality-checks
```
#### Data Analysis Project
1. Understand the data:
```
@data-quality-engineer
*task profile-data
```
2. Perform analysis:
```
@data-analyst
*task analyze-data
```
3. Create visualizations:
```
*task create-dashboard
```
## Advanced Features
### Elicitation Methods
The framework includes 12+ advanced elicitation methods:
- **Tree of Thoughts**: Explore multiple reasoning paths
- **Stakeholder Roundtable**: Consider multiple perspectives
- **Progressive Disclosure**: Build understanding layer by layer
- **Comparative Analysis**: Evaluate alternatives
- **Scenario-Based**: Test under different conditions
- **Risk Assessment**: Identify and mitigate risks
### Document Management
- **Automatic Sharding**: Split large documents by sections
- **Knowledge Base Integration**: Searchable documentation
- **Version Control**: Track document changes
- **Cross-References**: Link related documents
### Workflow Orchestration
- **Multi-Agent Handoffs**: Seamless context passing
- **Quality Gates**: Validation at each step
- **Progress Tracking**: Real-time status updates
- **Error Recovery**: Graceful handling of failures
## Best Practices
### Agent Selection
- Use specialized agents for focused tasks
- Start with the appropriate agent for your workflow stage
- Leverage agent expertise for better results
### Document Creation
- Begin with templates for consistency
- Use elicitation for comprehensive content
- Review and refine iteratively
- Maintain standard naming conventions
### Workflow Management
- Complete one task before starting the next
- Use clean context windows between agents
- Validate outputs at each stage
- Document decisions and rationale
### Quality Assurance
- Run quality checks at every stage
- Use validation frameworks consistently
- Review agent outputs before proceeding
- Maintain high standards throughout
## Configuration
### Technical Preferences
Store your preferences for consistent project setup:
- Frontend frameworks
- Backend languages
- Database choices
- Testing frameworks
- Deployment platforms
### Workflow Customization
Adapt workflows to your team's needs:
- Define custom task sequences
- Create specialized templates
- Set quality thresholds
- Configure validation rules
## Tips for Success
1. **Start Small**: Begin with simple tasks to understand the system
2. **Use Templates**: Leverage existing templates for consistency
3. **Interactive Refinement**: Use elicitation methods for better content
4. **Progressive Approach**: Build complex workflows incrementally
5. **Document Everything**: Maintain comprehensive documentation
6. **Review Regularly**: Validate outputs at each stage
7. **Learn from History**: Use session persistence to resume work
8. **Collaborate Effectively**: Leverage multi-agent capabilities