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/schema/models.py

@@ -0,0 +1,100 @@
+from pydantic import BaseModel, Field
+from typing import List, Optional, Dict, Any
+from datetime import datetime
+
+class TransmissionNode(BaseModel):
+    node_name: str = Field(..., description="产业链节点名称")
+    impact_type: str = Field(..., description="利好/利空/中性")
+    logic: str = Field(..., description="该节点的传导逻辑")
+
+class IntentAnalysis(BaseModel):
+    keywords: List[str] = Field(..., description="核心实体、事件或概念关键词")
+    search_queries: List[str] = Field(..., description="优化后的搜索引擎查询词")
+    is_specific_event: bool = Field(..., description="是否查询特定突发事件")
+    time_range: str = Field(..., description="时间范围 (recent/all/specific_date)")
+    intent_summary: str = Field(..., description="一句话意图描述")
+
+class FilterResult(BaseModel):
+    """LLM 筛选结果 - 快速判断是否有有效信号"""
+    has_valid_signals: bool = Field(..., description="列表中是否包含有效的金融信号")
+    selected_ids: List[int] = Field(default_factory=list, description="筛选出的有效信号 ID 列表")
+    themes: List[str] = Field(default_factory=list, description="信号涉及的主题")
+    reason: Optional[str] = Field(default=None, description="如果无有效信号，说明原因")
+
+class InvestmentSignal(BaseModel):
+    # 核心元数据
+    signal_id: str = Field(default="unknown_sig", description="唯一信号 ID")
+    title: str = Field(..., description="信号标题")
+    summary: str = Field(default="暂无摘要分析", description="100 字核心观点快报")
+    reasoning: str = Field(default="", description="详细的推演逻辑和理由")
+    
+    # 逻辑传导 (ISQ Key 1)
+    transmission_chain: List[TransmissionNode] = Field(default_factory=list, description="产业链传导逻辑链条")
+    
+    # 信号质量 (ISQ Key 2) - 来自 isq_template.DEFAULT_ISQ_TEMPLATE
+    # 参考: src/schema/isq_template.py 的 DEFAULT_ISQ_TEMPLATE 定义
+    sentiment_score: float = Field(default=0.0, description="[ISQ] 情绪/走势 (-1.0=极度看空 ~ 0.0=中性 ~ 1.0=极度看多)")
+    confidence: float = Field(default=0.5, description="[ISQ] 确定性 (0.0=不可信 ~ 1.0=完全确定)")
+    intensity: int = Field(default=3, description="[ISQ] 强度/影响量级 (1=微弱 ~ 5=极强)")
+    expectation_gap: float = Field(default=0.5, description="[ISQ] 预期差/博弈空间 (0.0=充分定价 ~ 1.0=巨大预期差)")
+    timeliness: float = Field(default=0.8, description="[ISQ] 时效性 (0.0=长期 ~ 1.0=超短期)")
+    
+    # 预测与博弈 (ISQ Key 3)
+    expected_horizon: str = Field(default="T+N", description="预期的反应时窗 (如: T+0, T+3, Long-term)")
+    price_in_status: str = Field(default="未知", description="市场预期消化程度 (未定价/部分定价/充分定价)")
+    
+    # 关联实体
+    impact_tickers: List[Dict[str, Any]] = Field(default_factory=list, description="受影响的代码列表及其权重")
+    industry_tags: List[str] = Field(default_factory=list, description="关联行业标签")
+    
+    # 溯源
+    sources: List[Dict[str, str]] = Field(default_factory=list, description="来源详情 (包含 title, url, source_name)")
+
+class ResearchContext(BaseModel):
+    """研究员搜集的背景信息结构"""
+    raw_signal: str = Field(..., description="原始信号内容")
+    tickers_found: List[Dict[str, Any]] = Field(default_factory=list, description="找到的相关标的及其基本面/股价信息")
+    industry_background: str = Field(..., description="行业背景及产业链现状")
+    latest_developments: List[str] = Field(default_factory=list, description="相关事件的最新进展")
+    key_risks: List[str] = Field(default_factory=list, description="潜在风险点")
+    search_results_summary: str = Field(..., description="搜索结果的综合摘要")
+
+class ScanContext(BaseModel):
+    """扫描员搜集的原始数据结构"""
+    hot_topics: List[str] = Field(..., description="当前市场热点话题")
+    news_summaries: List[Dict[str, Any]] = Field(..., description="关键新闻摘要列表")
+    market_data: Dict[str, Any] = Field(default_factory=dict, description="相关的市场行情数据")
+    sentiment_overview: str = Field(..., description="整体市场情绪概览")
+    raw_data_summary: str = Field(..., description="原始数据的综合摘要")
+
+class SignalCluster(BaseModel):
+    theme_title: str = Field(..., description="主题名称")
+    signal_ids: List[int] = Field(..., description="包含的信号 ID 列表")
+    rationale: str = Field(..., description="聚类理由")
+
+class ClusterContext(BaseModel):
+    """信号聚类结果结构"""
+    clusters: List[SignalCluster] = Field(..., description="聚类列表")
+
+class KLinePoint(BaseModel):
+    date: str = Field(..., description="日期")
+    open: float = Field(..., description="开盘价")
+    high: float = Field(..., description="最高价")
+    low: float = Field(..., description="最低价")
+    close: float = Field(..., description="收盘价")
+    volume: float = Field(..., description="成交量")
+
+class ForecastResult(BaseModel):
+    ticker: str = Field(..., description="股票代码")
+    base_forecast: List[KLinePoint] = Field(default_factory=list, description="Kronos 模型原始预测")
+    adjusted_forecast: List[KLinePoint] = Field(default_factory=list, description="LLM 调整后的预测")
+    rationale: str = Field(default="", description="预测调整理由及逻辑说明")
+    timestamp: str = Field(default_factory=lambda: datetime.now().strftime("%Y-%m-%d %H:%M:%S"), description="生成时间")
+
+class InvestmentReport(BaseModel):
+    overall_sentiment: str = Field(..., description="整体市场情绪评价")
+    market_entropy: float = Field(..., description="市场分歧度 (0-1, 1代表极高分歧)")
+    signals: List[InvestmentSignal] = Field(..., description="深度解析的投资信号列表")
+    forecasts: List[ForecastResult] = Field(default_factory=list, description="相关标的的预测结果")
+    timestamp: str = Field(..., description="报告生成时间")
+    meta_info: Optional[Dict[str, Any]] = Field(default_factory=dict, description="其他元数据")