ai-spark-coder

Automating feature implementation from database suggestions involves querying specific database tables for planned sparks and routing tasks to specialized skills. This Skill processes sparks marked for AI creation, consolidating them into a coherent plan for user approval before execution.

The workflow for processing AI sparks includes identifying sparks via SQL queries, consolidating parent and child sparks into an actionable plan, securing user approval, and routing tasks to specialized skills. It manages status updates and reporting throughout the execution lifecycle.

Yes, you can use Supabase to manage planned feature sparks and suggestions. This Skill integrates directly with Supabase, executing SQL queries to retrieve spark information, manage data records, and update the implementation status of feature suggestions.

Routing coding tasks to specialized AI skills happens automatically after consolidating parent and child sparks into a coherent plan and securing user approval. The system then delegates the necessary code generation, database migrations, or UI component creation to the appropriate skill.

Yes, user approval is required before executing automated code generation. The Skill consolidates parent and child sparks into a coherent implementation plan, which must be reviewed and approved by the user before any tasks are routed to specialized skills for execution.

The system handles parent and child sparks by consolidating them into a single, coherent implementation plan before any code generation begins. This ensures related sparks are processed together logically, reducing manual development overhead and streamlining automated execution.