Break large coding jobs into focused subagent missions with AB Method

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.

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

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

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.

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