from pddun_workbuddy import WorkBuddyClient
# 初始化客户端
client = WorkBuddyClient(api_key="YOUR_API_KEY")
# 调用餐饮门店诊断Skill
result = client.skills.execute(
skill_id="restaurant-diagnosis",
params={
"store_id": "store_12345",
"period": "last_30_days",
"focus_areas": ["revenue", "menu", "cost"]
}
)
# 查看诊断结果
print(f"诊断评分: {result['overall_score']}")
print(f"问题数量: {len(result['issues'])}")
print(f"预期提升: {result['expected_improvement']}")const { WorkBuddyClient } = require('@pddun/workbuddy');
const client = new WorkBuddyClient({ apiKey: 'YOUR_API_KEY' });
async function diagnoseRestaurant() {
const result = await client.skills.execute('restaurant-diagnosis', {
store_id: 'store_12345',
period: 'last_30_days',
focus_areas: ['revenue', 'menu', 'cost']
});
console.log(`诊断评分: ${result.overall_score}`);
console.log(`问题数量: ${result.issues.length}`);
}
diagnoseRestaurant();from pddun_workbuddy import WorkBuddyClient
import time
client = WorkBuddyClient(api_key="YOUR_API_KEY")
# 发现可用智能体
agents = client.mcp.a2a.list_agents(category="data_analysis")
print(f"找到 {len(agents)} 个数据分析智能体")
# 创建任务
task = client.mcp.a2a.create_task(
agent_id=agents[0]['id'],
task_type="data_analysis",
description="分析2026年8月销售数据",
input_data={"data_source": "sales_db", "period": "2026-08"}
)
print(f"任务ID: {task['task_id']}")
# 等待任务完成
while task['status'] == 'processing':
time.sleep(5)
task = client.mcp.a2a.get_task(task['task_id'])
print(f"任务结果: {task['result']}")from pddun_workbuddy import WorkBuddyClient
client = WorkBuddyClient(api_key="YOUR_API_KEY")
# 获取今日订单
orders = client.mcp.store.list_orders(
store_id="store_12345",
date="2026-09-07",
status="paid"
)
print(f"今日订单数: {len(orders)}")
total = sum(o['total_amount'] for o in orders)
print(f"今日营收: ¥{total:.2f}")
# 热销商品排行
ranking = client.mcp.store.get_sales_ranking(
store_id="store_12345",
period="last_7_days",
limit=10
)
print("\n热销商品TOP10:")
for i, item in enumerate(ranking, 1):
print(f"{i}. {item['product_name']} - 销量: {item['quantity']}")