Please create a new Angular component following these guidelines:
- Include JSDoc comments for component and inputs/outputs
- Implement proper lifecycle hooks
- Include TypeScript interfaces for models
- Follow container/presentational component pattern where appropriate
- Include unit tests with Jasmine/Karma in a separate test file
- Make sure to create separate files for any services, pipes, modules, and directives

Create a new Next.js page based on the following description.

Create a new Nuxt.js page based on the following description.

Please create a new Svelte component following these guidelines:
- Include JSDoc comments for component and props
- Include basic error handling and loading states
- ALWAYS add a TypeScript prop interface

Create an exploratory data analysis workflow that includes:

Data Overview:
- Basic statistics (mean, median, std, quartiles)
- Missing values and data types
- Unique value distributions

Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices
- Temporal patterns (if applicable)

Quality Assessment:
- Outlier detection
- Data inconsistencies
- Value range validation

Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook

The user has provided the following information:

Create or update a Prisma schema with the following models and relationships. Include necessary fields, relationships, and any relevant enums.

Review this API route for security vulnerabilities. Ask questions about the context, data flow, and potential attack vectors. Be thorough in your investigation.

Generate a data processing pipeline with these requirements:

Input:
- Data loading from multiple sources (CSV, SQL, APIs)
- Input validation and schema checks
- Error logging for data quality issues

Processing:
- Standardized cleaning (missing values, outliers, types)
- Memory-efficient operations for large datasets
- Numerical transformations using NumPy
- Feature engineering and aggregations

Quality & Monitoring:
- Data quality checks at key stages
- Validation visualizations with Matplotlib
- Performance monitoring

Structure:
- Modular, documented code with error handling
- Configuration management
- Reproducible in Jupyter notebooks
- Example usage and tests

The user has provided the following information:

Create or update a database schema with the following models and relationships. Include necessary fields, relationships, and any relevant enums.

Create a client component with the following functionality. If writing this as a server component is not possible, explain why.