Import 9 alphaear finance skills

- alphaear-deepear-lite: DeepEar Lite API integration
- alphaear-logic-visualizer: Draw.io XML finance diagrams
- alphaear-news: Real-time finance news (10+ sources)
- alphaear-predictor: Kronos time-series forecasting
- alphaear-reporter: Professional financial reports
- alphaear-search: Web search + local RAG
- alphaear-sentiment: FinBERT/LLM sentiment analysis
- alphaear-signal-tracker: Signal evolution tracking
- alphaear-stock: A-Share/HK/US stock data

Updates:
- All scripts updated to use universal .env path
- Added JINA_API_KEY, LLM_*, DEEPSEEK_API_KEY to .env.example
- Updated load_dotenv() to use ~/.config/opencode/.env

skills/alphaear-sentiment/scripts/llm/capability.py

@@ -0,0 +1,85 @@
+import os
+from typing import Optional, List, Dict, Any
+from agno.agent import Agent
+from agno.models.base import Model
+from loguru import logger
+from .llm.factory import get_model
+
+
+def test_tool_call_support(model: Model) -> bool:
+    """
+    测试模型是否支持原生的 Tool Call (Function Calling)。
+    通过尝试执行一个简单的加法工具来验证。
+    """
+
+    def get_current_weather(location: str):
+        """获取指定地点的天气"""
+        return f"{location} 的天气是晴天，25度。"
+
+    test_agent = Agent(
+        model=model,
+        tools=[get_current_weather],
+        instructions="请调用工具查询北京的天气，并直接返回工具的输出结果。",
+    )
+
+    try:
+        # 运行一个简单的任务，观察是否触发了 tool_call
+        response = test_agent.run("北京天气怎么样？")
+
+        # 检查 response 中是否包含 tool_calls
+        # Agno 的 RunResponse 对象通常包含 messages，我们可以检查最后几条消息
+        has_tool_call = False
+        for msg in response.messages:
+            if hasattr(msg, "tool_calls") and msg.tool_calls:
+                has_tool_call = True
+                break
+
+        if has_tool_call:
+            logger.info(f"✅ Model {model.id} supports native tool calling.")
+            return True
+        else:
+            # 如果没有 tool_calls 但返回了正确答案，可能是模型通过纯文本模拟了工具调用（ReAct）
+            # 或者根本没用工具。对于原生支持的判断，我们坚持要求有 tool_calls 结构。
+            logger.warning(
+                f"⚠️ Model {model.id} did NOT use native tool calling structure."
+            )
+            return False
+
+    except Exception as e:
+        logger.error(f"❌ Error testing tool call for {model.id}: {e}")
+        return False
+
+
+class ModelCapabilityRegistry:
+    """
+    模型能力注册表，用于缓存和管理不同模型的能力测试结果。
+    """
+
+    _cache = {}
+
+    @classmethod
+    def get_capabilities(
+        cls, provider: str, model_id: str, **kwargs
+    ) -> Dict[str, bool]:
+        key = f"{provider}:{model_id}"
+        if key not in cls._cache:
+            logger.info(f"🔍 Testing capabilities for {key}...")
+            model = get_model(provider, model_id, **kwargs)
+            supports_tool_call = test_tool_call_support(model)
+            cls._cache[key] = {"supports_tool_call": supports_tool_call}
+        return cls._cache[key]
+
+
+if __name__ == "__main__":
+    import os
+    from dotenv import load_dotenv
+
+    load_dotenv(os.path.expanduser("~/.config/opencode/.env"))
+
+    # 测试当前配置的模型
+    p = os.getenv("LLM_PROVIDER", "ust")
+    m = os.getenv("LLM_MODEL", "Qwen")
+
+    print(f"Testing {p}/{m}...")
+    res = ModelCapabilityRegistry.get_capabilities(p, m)
+    print(f"Result: {res}")