Maintain a perpetual conversational loop with ARCHE-based premise tracing.
npx skills add https://github.com/gg686-jkl/listener-skill --skill listener
Please help me install this Agent Skill. Skill: listener Source: https://github.com/gg686-jkl/listener-skill/tree/main/claude-code/listener Command: npx skills add https://github.com/gg686-jkl/listener-skill --skill listener
Maintains an endless, productive dialogue by keeping the AI in a LISTEN state and using ARCHE-based Trace/Push to surface hidden premises and drive forward reasoning.
Enter the loop by sending /listener to begin and return to LISTEN after each turn.
High-intent search queries and answers about installing and using this skill.
Stateful conversation management maintains a perpetual loop that keeps AI agents in a LISTEN state, surfacing unstated premises and driving forward reasoning for long-running dialogue scenarios. It ensures continuous, productive interaction without losing context.
You trace first principles in conversational AI by applying ARCHE-based reasoning to surface hidden premises and identify contradictions. This stateful decision engine pushes reasoning forward, ensuring clarity controls during research, coaching, or decision-support tasks.
Yes, you can run a continuous reasoning loop with built-in safety gates by initiating the stateful conversation with a command. The loop remains active and endless, returning to a LISTEN state after each turn until explicitly stopped by the user.
The best way to surface hidden premises in AI coaching sessions is using ARCHE-driven reasoning within a perpetual conversational loop. This stateful engine applies comprehension and clarity controls to identify unstated assumptions and propose concrete directions.
To stop a perpetual conversational loop during long-running dialogue, you must explicitly issue the dedicated stop command. The stateful decision engine only exits the continuous LISTEN cycle and safety gates when it receives this specific exit instruction.
Yes, the stateful decision engine works for research and decision support. It applies first-principles tracing and ARCHE-based reasoning to long-running dialogue scenarios, maintaining comprehension and clarity controls to ensure safe, continuous interaction across these domains.
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