Using Your Project as Context to Set Up .dinorules

Creating effective .dinorules from scratch can be challenging when you don't know what standards to define or how to articulate your team's existing patterns. Rather than starting with a blank file, you can analyze your current dbt™ project to identify established conventions and use those insights to build comprehensive rules.

DinoAI Agent can examine your existing models, YAML files, and project structure to automatically generate .dinorules that capture your team's actual practices.

Example Prompt

Analyze my selected dbt™ project files and create a .dinorules that capture my existing SQL formatting, naming conventions, folder structure, and documentation patterns.

Recommended: Add context by selecting multiple files that represent your best practices. include well-structured models from different folders (staging, marts), your dbt_project.yml, and existing schema files. This gives DinoAI a comprehensive view of your standards.

How to use

To generate a .dinorules file that matches your team standards, do the following:

1. Open DinoAI by clicking the DinoAI icon (🪄) in the right panel

2. Enter prompt - You can use the above example or create your own

3. Add files as context using the "@" icon in the DinoAI panel. Select representative files from different folders.

   1. Examples: staging models, intermediate models, marts models, your dbt_project.yml, and schema.yml files

How It Works

After you enter your prompt:

1. DinoAI analyzes the structure and patterns in your selected files

2. It identifies consistent naming conventions, SQL formatting styles, and organizational patterns

3. It examines your folder structure and materialization strategies

4. It creates a .dinorules file that codifies these discovered patterns

5. The generated rules ensure future AI-generated code matches your established standards

Example Output

DinoAI will generate a comprehensive .dinorules file like this:

Example .dinorules file

Project Context
- Domain: E-commerce analytics
- Data Stack: dbt™ with Snowflake
- Project Structure: source → staging → intermediate → marts hierarchy

SQL Formatting Standards (Based on Analyzed Files)
- Keywords: Always uppercase (SELECT, FROM, WHERE, etc.)
- Indentation: 4 spaces, no tabs
- Comma Style: Trailing commas in SELECT statements
- Aliasing: Always alias tables (use 'u' for users, 'o' for orders)
- CTEs: Prefer CTEs over subqueries for readability
- Comments: Add comments for complex business logic

Naming Conventions (Extracted from Current Models)
- Models: snake_case with prefixes (stg_, int_, dim_, fct_)
- Columns: snake_case, descriptive names
- Tables: Singular nouns (user, not users)
- Primary Keys: Always end with '_id' (user_id, order_id)

Folder Structure and Materializations
- staging/: Views only, basic transformations
- intermediate/: Views, complex business logic
- marts/: Tables or incremental, final business models
- All models tagged by domain and layer

Documentation Requirements
- All models require descriptions
- Business-critical columns need descriptions
- Primary keys need unique and not_null tests
- Foreign keys need relationships tests

Business Logic Patterns
- Always use UTC for timestamps
- Standardize null handling with COALESCE
- Use consistent date formatting (YYYY-MM-DD)
- Apply consistent rounding rules (2 decimal places for currency)

Quality Standards
- All models must compile without warnings
- Use consistent test patterns across similar model types
- Maintain referential integrity through proper testing

Key Benefits

• Consistency: Ensures all future AI-generated code matches your existing standards

• Discovery: Reveals patterns you may not have consciously documented

• Team Alignment: Codifies implicit knowledge that experienced team members understand

• Efficiency: Creates comprehensive rules without starting from scratch

• Accuracy: Based on your actual codebase rather than generic best practices

When to Use This

• When setting up .dinorules for the first time

• When your project has evolved organically and you want to formalize conventions

• Before major refactoring efforts to ensure consistency

• When preparing to scale your team and need documented standards