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自动追踪用户情绪变化，在合适的时机关心用户。检测对话情绪、记忆历史、主动关心、周报生成。
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Based on SOC occupation classification
| name | emotion-memory-assistant |
| slug | emotion-memory-assistant |
| version | 1.0.0 |
| description | 自动追踪用户情绪变化，在合适的时机关心用户。检测对话情绪、记忆历史、主动关心、周报生成。 |
| homepage | https://github.com/786793119/miya-skills |
| metadata | {"openclaw":{"emoji":"💕","requires":{"bins":["python3"]},"os":["linux","darwin","win32"]}} |
自动追踪用户情绪变化，在合适的时机关心用户。
detect_emotion - 检测对话中的情绪关键词recall_emotion_history - 查询历史情绪记录send_care_message - 发送关心消息给用户generate_weekly_report - 生成每周情绪报告正向情绪: 开心、高兴、愉快、兴奋、满意、舒服、快乐、幸福
负向情绪: 难过、伤心、焦虑、担心、害怕、沮丧、低落、郁闷、烦、生气、失望
中性状态: 忙、累、困、无聊
当检测到用户负面情绪，且距离上次关心超过30分钟，自动发送关心消息。
from emotion_memory import detect_emotion, recall_emotion_history, generate_weekly_report
# 检测情绪
result = detect_emotion("今天基金亏了，心情不好...")
print(result)
# 查询历史
history = recall_emotion_history(days=7)
# 生成周报
report = generate_weekly_report()
~/.memory/emotions/history.jsonBy Miya - 2026
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