behavioral-modes

AI behavior modes switch by invoking a specific mode explicitly, such as instructing the system to switch to IMPLEMENT mode. Mode definitions in the frontmatter guide mode switching and format responses for tasks like brainstorming or debugging.

The best way to structure an AI workflow is to use the mode library sequentially: plan a feature in BRAINSTORM mode, IMPLEMENT the solution, DEBUG any issues, and finally SHIP the code. This ensures contextual reasoning at each stage.

Yes, you can adapt AI response formatting using mode definitions. Output conventions defined in the frontmatter adjust questioning style and prioritization, tailoring the response format to fit the specific project stage or task type.

Yes, this mode-switching approach works for both code review and teaching. The mode library includes distinct REVIEW and TEACH modes, which adjust the AI's contextual reasoning and output conventions to suit each specific task type.

No, you do not need any dependencies to use AI mode switching. The skill operates independently without external components, relying solely on frontmatter definitions to guide adaptive behavior across various software engineering projects.

Guided modes provide structured mode switching and consistent interaction patterns that standard prompts lack. Defining a library of modes like IMPLEMENT ensures the AI applies contextual reasoning and appropriate output conventions for the specific task.