Agent Skills › ninehills/skills › alphaear-sentiment

alphaear-sentiment

GitHub

提供针对金融文本的情感分析能力，支持使用FinBERT本地模型或LLM进行情绪极性判断与评分。

alphaear-sentiment/SKILL.md ninehills/skills

触发场景

需要分析金融新闻或文本的情绪倾向获取金融数据的情感得分和理由

安装

npx skills add ninehills/skills --skill alphaear-sentiment -g -y

更多选项

非标准路径

npx skills add https://github.com/ninehills/skills/tree/main/alphaear-sentiment -g -y

不安装直接使用

npx skills use ninehills/skills@alphaear-sentiment

指定 Agent (Claude Code)

npx skills add ninehills/skills --skill alphaear-sentiment -a claude-code -g -y

安装 repo 全部 skill

npx skills add ninehills/skills --all -g -y

预览 repo 内 skill

npx skills add ninehills/skills --list

SKILL.md

Frontmatter

{
    "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

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.

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.