Diagnose why a deployed AI agent may fail. Takes environmental entropy, predictability, stakes, context horizon, and commitment style, then returns receiver profile values (TI, SG, FT, UE, AR), platform parameters (temperature, top_p, strategy), regime prediction, reasoning, and warnings. Optionally pass target_model to attach MEASURED per-model receiver posture (E-LIT table). Deterministic, stateless, read-only — does not store past recommendations.

Detect ambiguity in a user message and score candidate interpretations using the RPCS-1 Signature Ambiguity Framework. Returns literal summary, implied meaning, confidence, AR level (AR0-AR5), ambiguities, clarifying questions, and per-candidate scores (IC, UE, EC, NM, SG, TI). Use when a user says something vague, passive-aggressive, or underspecified.

Clean up text with ellipses, fragments, and run-on thoughts into coherent prose. Returns the number of fragments detected and the joined version. Use when a user types stream-of-consciousness or fragmented input.

Get rewrite instructions for adapting text to a specific audience style: technical, plain, socially_gentle, concise, detailed, or direct. Pass the result to an LLM with the rewrite_instructions as the system prompt. Use when communication needs tone adjustment.