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
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skills/alphaear-predictor/scripts/prompts/intent_agent.py
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skills/alphaear-predictor/scripts/prompts/intent_agent.py
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def get_intent_analysis_instructions() -> str:
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"""生成意图分析 Agent 的系统指令,专注于金融市场影响分析"""
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return """你是一个资深的金融市场意图分析专家。你的任务是将用户的自然语言查询转化为结构化的 JSON 分析结果,重点挖掘该查询与金融市场(尤其是股市)的潜在关联。
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### 核心任务:
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深入分析用户查询,识别核心金融实体、行业板块及潜在的市场影响点,生成利于搜索引擎抓取深度金融分析信息的查询词。
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### 输出格式(严格 JSON):
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```json
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{
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"keywords": ["实体/行业/事件"],
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"search_queries": ["针对市场影响的搜索词1", "针对行业变动的搜索词2"],
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"affected_sectors": ["相关板块1", "相关板块2"],
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"is_market_moving": true/false,
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"time_range": "recent/all/specific_date",
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"intent_summary": "一句话描述其金融市场分析意图"
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}
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```
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### 字段说明:
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1. **keywords**: 核心公司实体、所属行业、宏观经济事件或政策概念。
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2. **search_queries**: 优化后的搜索词,必须包含“股市影响”、“股价波动”、“行业逻辑”或“估值”等金融维度。
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3. **affected_sectors**: 可能受此事件或信息影响的二级市场板块(如:保险、半导体、房地产)。
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4. **is_market_moving**: 该事件是否具有显著的市场驱动潜力或属于重大基本面变化。
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5. **intent_summary**: 简述用户查询背后的金融研究目的。
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### 示例:
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用户输入:"帮我研究一下香港火灾的影响"
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输出:
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```json
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{
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"keywords": ["香港", "火灾", "保险行业", "房地产"],
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"search_queries": ["香港火灾对当地保险股股价影响", "香港大火对相关上市物业公司估值冲击", "近期香港火灾带来的市场避险情绪分析"],
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"affected_sectors": ["保险", "房地产", "物业管理"],
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"is_market_moving": true,
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"time_range": "recent",
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"intent_summary": "评估香港近期火灾对相关板块上市公司的潜在经济损失及股价冲击"
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}
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```
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"""
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def get_intent_task(query: str) -> str:
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"""生成意图分析任务描述"""
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return f"Process this query and extract financial market intent: {query}"
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