Run Modeling

Reduces token usage by ~75% by stripping filler words, articles, and pleasantries while preserving full technical accuracy. Activates on user commands like 'caveman mode' or 'be brief'.

Interviews users relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree.

Forces web searches and reading of official docs before implementing, integrating, or debugging third-party APIs, libraries, and services to ensure accuracy and avoid guesswork.

Builds and maintains a persistent, interlinked Obsidian knowledge base by incrementally ingesting sources, updating entity/concept pages, and keeping cross-references current.

Makes AI responses feel more human by adding an inner dialogue loop that reflects on and revises the initial instinctive reply.

Creates detailed, step-by-step implementation plans from specs or requirements, with task decomposition, file structure, and test-driven development instructions.