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Summary
This excerpt shows a prompt template for multi-agent code generation and task splitting. It matters because it asks a model to write a goal, spawn agents in parallel, and merge their outputs into a finished software artifact.
Problem
- The prompt tries to turn a vague build request into a concrete software task with feature, behavior, and output-format details.
- It targets the coordination problem in agentic coding: how to split work, run parts at the same time, and combine the results.
Approach
- Rewrite the user's request into a structured build prompt with placeholders for thing, technology, features, interaction, mood, visuals, environment, effects, and output format.
- Create a new goal for the model instead of only following the original request.
- Spawn multiple agents in parallel, assign each one a dedicated goal, and split the work into independent pieces.
- Dispatch the pieces concurrently and synthesize the partial results as they return.
Results
- The excerpt does not report quantitative results, benchmarks, or dataset comparisons.
- Its concrete claim is that parallel agents can do the task “better and faster” when work is split into independent parts and merged afterward.
- It also claims the prompt can produce more detailed software outputs by forcing explicit requirements for features, interaction, visual style, and file type.