Agent Skills › ninehills/skills › alphaear-sentiment

alphaear-sentiment

GitHub

提供金融文本情感分析能力，支持FinBERT本地模型和LLM两种模式。用于判断金融新闻或文本的情绪极性（正/负/中性）及评分，并可将结果存入数据库，辅助市场情绪研判。

alphaear-sentiment/SKILL.md ninehills/skills

Trigger Scenarios

需要分析金融文本或新闻的情感倾向获取金融数据的情绪分数和标签

Install

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

More Options

Non-standard path

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

Use without installing

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.