nox-preference-learner

To make an AI agent less verbose through conversation, provide direct feedback like asking it to be more concise. The skill captures this conversational feedback, logs it for reinforcement, and applies a lightweight RLHF loop to shorten future responses automatically.

Conversational feedback shapes an AI agent's behavior through a lightweight Reinforcement Learning from Human Feedback mechanism. It logs user feedback signals, updates a JSON-based preference store with decay mechanisms, and injects learned preferences into the agent's context to adapt responses.

You can control six behavioral dimensions by teaching an AI agent preferences: autonomy, verbosity, proactivity, formality, technical depth, and confirmation seeking. The agent adapts its responses across these dimensions based on your ongoing conversational feedback.

No, this preference learning approach does not require any external dependencies. It operates entirely through scripts to manage a JSON-based preference store and inject learned behavioral patterns directly into the AI agent's context.

The agent handles outdated behavioral preferences using built-in decay mechanisms within its JSON-based preference store. This ensures that older conversational feedback signals gradually lose influence, allowing the agent to adapt to more recent behavioral preferences.