Optimizes task execution by selecting the most efficient AI model and parallelizing subtasks based on complexity.
Strategically plan and execute complex tasks by breaking them down into manageable sub-steps. This skill provides a framework for scoring task complexity to select the most appropriate model (cost-effective Haiku, balanced Sonnet, or powerful Opus) and identifies opportunities to run independent tasks in parallel, significantly improving speed and efficiency. Use this at the start of any multi-step project to create an optimal execution plan, avoiding common pitfalls like model overkill or premature parallelization.
Key Features
01Complexity-based model selection (Haiku, Sonnet, Opus)
02Cost-benefit analysis for different AI models
03Pre-flight checklist for task planning and scoping
040 GitHub stars
05Intelligent parallelization heuristics for independent subtasks
06Common orchestration patterns for development workflows (e.g., research, implementation, testing)
Use Cases
01Planning a multi-step software development feature from research to completion.
02Optimizing the cost and speed of a complex coding request.
03Refactoring a large codebase by breaking it into parallelizable chunks.
