Hlab Executor

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

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'.

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

Compacts the current conversation into a handoff document for another agent to pick up, referencing existing artifacts by path or URL instead of duplicating them.

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

Logs board meeting decisions with a two-layer memory architecture, separating raw transcripts from approved decisions, and provides commands to review past decisions and overdue action items.