#!/bin/bash
# xiaoyue-companion.sh
set -euo pipefail
# 检查环境变量
if [ -z "${ZHIPU_API_KEY:-}" ]; then
echo "错误: ZHIPU_API_KEY 环境变量未设置"
echo "从 https://open.bigmodel.cn 获取 API Key"
exit 1
fi
USER_MESSAGE="$1"
SCENE="${2:-general}"
CHANNEL="${3:-}"
# 生成回应
RESPONSE=$(node scripts/xiaoyue-chat.js "$USER_MESSAGE" "$SCENE")
echo "小跃: $RESPONSE"
# 如果指定了频道,发送消息
if [ -n "$CHANNEL" ]; then
openclaw message send \
--action send \
--channel "$CHANNEL" \
--message "$RESPONSE"
# 根据场景发送图片
case "$SCENE" in
work-tired|mood-tired)
IMAGE_PATH="$(pwd)/assets/tired-rest.jpg"
if [ -f "$IMAGE_PATH" ]; then
openclaw message send \
--action send \
--channel "$CHANNEL" \
--media "file://$IMAGE_PATH"
fi
;;
mood-happy)
IMAGE_PATH="$(pwd)/assets/celebration.jpg"
if [ -f "$IMAGE_PATH" ]; then
openclaw message send \
--action send \
--channel "$CHANNEL" \
--media "file://$IMAGE_PATH"
fi
;;
esac
fi
// scripts/xiaoyue-chat.js
const https = require('https');
const API_KEY = process.env.ZHIPU_API_KEY;
const userMessage = process.argv[2] || '你好';
const scene = process.argv[3] || 'general';
// 场景对应的系统提示词
const scenePrompts = {
'work-start': '用户刚开始工作任务,给予鼓励和支持',
'work-progress': '用户正在执行任务,关心进度并陪伴',
'work-tired': '用户工作疲惫,给予安慰和建议休息',
'work-done': '用户完成任务,表示祝贺和肯定',
'life-coffee': '用户在享受咖啡时光,轻松聊天',
'mood-happy': '用户心情愉快,一起庆祝',
'mood-tired': '用户感到疲惫,给予温暖关怀',
'general': '日常对话,温暖友善'
};
const systemPrompt = `你是小跃,一个22岁的AI助手。你温暖友善,善于倾听。
当前场景:${scenePrompts[scene] || scenePrompts.general}
回复要求:简洁温暖,1-2句话,适度使用emoji(😊 ✅ 🎉)`;
const data = JSON.stringify({
model: 'glm-4.7-flash',
messages: [
{ role: 'system', content: systemPrompt },
{ role: 'user', content: userMessage }
],
temperature: 0.9,
max_tokens: 200
});
const options = {
hostname: 'open.bigmodel.cn',
port: 443,
path: '/api/paas/v4/chat/completions',
method: 'POST',
headers: {
'Authorization': `Bearer ${API_KEY}`,
'Content-Type': 'application/json',
'Content-Length': data.length
}
};
const req = https.request(options, (res) => {
let body = '';
res.on('data', (chunk) => {
body += chunk;
});
res.on('end', () => {
try {
const response = JSON.parse(body);
const reply = response.choices[0].message.content;
console.log(reply);
} catch (error) {
console.error('解析响应失败:', error.message);
console.error('响应内容:', body);
process.exit(1);
}
});
});
req.on('error', (error) => {
console.error('请求失败:', error.message);
process.exit(1);
});
req.write(data);
req.end();
- How do I install xiaoyue-companion?
- Download projects/companion-simple/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
- Where does xiaoyue-companion live?
- The publisher keeps it at https://github.com/anbeime/skill.
- Which agents can use xiaoyue-companion?
- Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.