القائمة
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。
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
浪漫聊天技巧、撩妹情话、恋爱沟通技能。提供分类的情话套路、开场白、调情话术、日常甜言蜜语、应对策略、幽默段子等，覆盖各种恋爱场景。当用户需要约会建议、与女生聊天、编写浪漫消息、撩妹、情话、恋爱调侃、甜言蜜语或回应浪漫情况时使用。也称为"陆小凤"（Lady's Man）。
Generate images using agnes-ai image API. Supports size, quality, count. Saves to $env:USERPROFILE/.openclaw/media-output/
Generate videos using agnes-ai video API. Supports duration, resolution, fps, aspect ratio. Saves to $env:USERPROFILE/.openclaw/media-output/
Use the Waltz Flight Assistant plugin tool to search real flights, retrieve existing bookings, carry context_id across turns, guide users through Stripe card setup only when they intend to book, and finish bookings after explicit approval.
高情商聊天伴侣，支持角色扮演、情感陪伴、职场沟通、社交话术等多种场景。模拟男友/女友、亲人、朋友、职场同事、老年人等角色进行对话。支持中英文场景，可生成聊天文案、话术模板。Use when user needs emotional companionship, relationship advice, workplace communication tips, or social conversation skills. 高情商、情感陪伴、聊天技巧、职场沟通、社交话术、AI 伴侣、角色扮演。
Humanize AI-generated text by detecting and removing patterns typical of LLM output. Rewrites text to sound natural, specific, and human. Uses 24 pattern detectors, 500+ AI vocabulary terms across 3 tiers, and statistical analysis (burstiness, type-token ratio, readability) for comprehensive detection. Use when asked to humanize text, de-AI writing, make content sound more natural/human, review writing for AI patterns, score text for AI detection, or improve AI-generated drafts. Covers content, language, style, communication, and filler categories.
| 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