What problem does it solve? AI agents lose all knowledge of the user between conversations, forcing repetitive re-explanation and preventing personalized, continuous assistance. This Skill configures Honcho memory for Hermes so user preferences, facts, and session context persist across conversations and across multiple agent profiles. ## Core Features & Use Cases - Cross-Session User Modeling: Honcho builds a persistent representation of each user, injecting session summaries, user representations, and peer cards into the system prompt automatically. - Multi-Profile Peer Isolation: Each Hermes profile gets its own AI peer while sharing a unified view of the user, so specialized agents develop independent identities without losing shared context. - Tunable Dialectic Reasoning: Three orthogonal knobs (cadence, depth, level) control how often, how many rounds, and how intensely Honcho's reasoning engine synthesizes answers about the user. - Use Case: A developer runs separate Hermes profiles for coding and writing. With Honcho configured, both profiles remember the user's preferences, the coding agent recalls the Rust project discussed last week, and context budgets prevent token blowup in long sessions. ## Quick Start Set up Honcho memory for my Hermes agent by running the interactive setup wizard and verifying the connection with the status command.
