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/SKILL.md

@@ -0,0 +1,57 @@
+---
+name: alphaear-sentiment
+description: Analyze finance text sentiment using FinBERT or LLM. Use when the user needs to determine the sentiment (positive/negative/neutral) and score of financial text markets.
+---
+
+# AlphaEar Sentiment Skill
+
+## Overview
+
+This skill provides sentiment analysis capabilities tailored for financial texts, supporting both FinBERT (local model) and LLM-based analysis modes.
+
+## Capabilities
+
+## Capabilities
+
+### 1. Analyze Sentiment (FinBERT / Local)
+
+Use `scripts/sentiment_tools.py` for high-speed, local sentiment analysis using FinBERT.
+
+**Key Methods:**
+
+-   `analyze_sentiment(text)`: Get sentiment score and label using localized FinBERT model.
+    -   **Returns**: `{'score': float, 'label': str, 'reason': str}`.
+    -   **Score Range**: -1.0 (Negative) to 1.0 (Positive).
+-   `batch_update_news_sentiment(source, limit)`: Batch process unanalyzed news in the database (FinBERT only).
+
+### 2. Analyze Sentiment (LLM / Agentic)
+
+For higher accuracy or reasoning capabilities, **YOU (the Agent)** should perform the analysis using the Prompt below, calling the LLM directly, and then update the database if necessary.
+
+#### Sentiment Analysis Prompt
+
+Use this prompt to analyze financial texts if the local tool is insufficient or if reasoning is required.
+
+```markdown
+请分析以下金融/新闻文本的情绪极性。
+返回严格的 JSON 格式:
+{"score": <float: -1.0到1.0>, "label": "<positive/negative/neutral>", "reason": "<简短理由>"}
+
+文本: {text}
+```
+
+**Scoring Guide:**
+- **Positive (0.1 to 1.0)**: Optimistic news, profit growth, policy support, etc.
+- **Negative (-1.0 to -0.1)**: Losses, sanctions, price drops, pessimism.
+- **Neutral (-0.1 to 0.1)**: Factual reporting, sideways movement, ambiguous impact.
+
+#### Helper Methods
+- `update_single_news_sentiment(id, score, reason)`: Use this to save your manual analysis to the database.
+
+## Dependencies
+
+-   `torch` (for FinBERT)
+-   `transformers` (for FinBERT)
+-   `sqlite3` (built-in)
+
+Ensure `DatabaseManager` is initialized correctly.