Harbor Boost provides a collection of modular behaviors that modify how an LLM reasons, explains, or plays with user input, such as prompting multiple answers (3T), multi-step analysis (AMBI), analogy-first responses (ANALOGICAL), and automatic temperature adjustment (AUTOTEMP).

Other modules expand or perturb context or reasoning patterns, including context expansion (CEX), recursive clarification (CLARITY), concept-graph generation (CONCEPT), multi-expert debate (CRYSTAL), dynamic chain-of-thought and self-reflection (G1), formula-like rewriting (FML), “grug” simplification (GRUG), and character-level random rewrites to boost creativity (KLMBR). Additional modules focus on retrieval or stylistic/novel behavior, such as URL content ingestion (DISCUSSURL), ELI5-style simplification, joke or game-like mishearing and dice-roll behaviors (DEAF, DND), cellular automata noise (CEA), and beam-style validation of choices (CSSV).