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-predictor/scripts/forecast_agent.py

@@ -0,0 +1,76 @@
+import json
+from typing import List, Optional, Dict, Any
+from datetime import datetime
+from loguru import logger
+import pandas as pd
+
+from .kronos_predictor import KronosPredictorUtility
+from .utils.database_manager import DatabaseManager
+from .schema.models import ForecastResult, KLinePoint, InvestmentSignal
+
+class ForecastUtils:
+    """
+    预测辅助工具 (ForecastUtils)
+    提供数据准备、基础模型预测等功能。
+    LLM 调整逻辑已移交 Agent 执行 (参考 scripts/prompts/PROMPTS.md)。
+    """
+    
+    def __init__(self, db: DatabaseManager):
+        self.db = db
+        self.predictor_util = KronosPredictorUtility() # Singleton
+
+    def get_base_forecast(
+        self,
+        ticker: str,
+        signals: List[Dict] = None,
+        lookback: int = 20,
+        pred_len: int = 5,
+    ) -> Optional[List[KLinePoint]]:
+        """
+        获取基础预测数据 (技术面 + 新闻模型定量修正)。
+        Agent 应随后使用 PROMPTS.md 中的指令进行定性调整。
+        """
+        logger.info(f"🔮 Generating base forecast for {ticker}...")
+        
+        # 1. 获取历史数据
+        from .stock_tools import StockTools
+        stock_tools = StockTools(self.db, auto_update=False)
+        
+        end_date = datetime.now().strftime("%Y-%m-%d")
+        # 宽放一点时间以确保有足够的交易日
+        start_date = (datetime.now() - pd.Timedelta(days=max(lookback * 4, 90))).strftime("%Y-%m-%d")
+        df = stock_tools.get_stock_price(ticker, start_date=start_date, end_date=end_date)
+
+        if df.empty or len(df) < lookback:
+            # Try force sync
+            df = stock_tools.get_stock_price(ticker, start_date=start_date, end_date=end_date, force_sync=True)
+
+        if df.empty:
+            logger.warning(f"⚠️ No history data for {ticker}")
+            return None
+
+        effective_lookback = lookback
+        if len(df) < lookback:
+            if len(df) < 10:
+                logger.warning(f"⚠️ Insufficient history for {ticker}")
+                return None
+            effective_lookback = len(df)
+
+        # 2. 准备信号上下文
+        signal_lines = []
+        for s in (signals or []):
+            try:
+                title = s.get('title', '') if isinstance(s, dict) else getattr(s, 'title', '')
+                summary = s.get('summary', '') if isinstance(s, dict) else getattr(s, 'summary', '')
+                if title or summary:
+                    signal_lines.append(f"- {title}: {summary}")
+            except Exception:
+                continue
+
+        signals_context = "\n".join(signal_lines).strip()
+        
+        # 3. 模型预测 (News-Adjusted if context exists)
+        if signals_context:
+            return self.predictor_util.get_base_forecast(df, lookback=effective_lookback, pred_len=pred_len, news_text=signals_context)
+        else:
+            return self.predictor_util.get_base_forecast(df, lookback=effective_lookback, pred_len=pred_len, news_text=None)