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Connect live heart-rate and conversation data to AI agents for coaching.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Pulse" yet — see the docs or source repo.
Using this session's live heart-rate data and conversation transcript, identify likely stress spikes, changes in speech signals, and provide brief coaching advice.
A result highlighting stress periods, summarizing relevant transcript moments, and giving actionable communication coaching.
Continuously observe the current session's heart-rate and transcript; when stress rises or speech patterns become abnormal, prompt me to adjust pace, pauses, or response style.
Ongoing session monitoring with real-time coaching prompts during the conversation.
Based on this session's physiological signals and conversation record, create a review: when I seemed more stressed, which expressions may have triggered it, and what I can try next time.
A post-session review combining stress cues, speech signals, and suggested improvements.
Developers or researchers can connect live Wear OS heart-rate data and conversation transcripts to an AI agent so it can observe state changes and provide coaching during a session.
When you need to understand tension or expression changes in a conversation, this tool feeds physiological and transcript signals to AI to derive stress and speech-related cues.
Product managers or researchers can use session observations to have AI generate coaching suggestions about communication style, pacing, or response strategy.
Pulse is an MCP tool that connects live Wear OS heart-rate data and conversation transcripts to AI agents. This lets agents observe sessions, derive stress and speech signals, and provide coaching responses.
Based on the provided information, you at least need Wear OS heart-rate data, conversation transcripts, and an AI agent environment that supports MCP. For exact setup and runtime requirements, see the source repository.
Its distinguishing feature is that it feeds both live heart-rate data and conversation transcripts into AI agents, rather than exposing only one data source. The goal is to let agents combine session observation, stress signals, and speech cues to provide coaching.
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