ninehills/alphaear-sentiment
alphaear-sentiment/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add ninehills/skills alphaear-sentimentInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 11, 2026, 10:56 PM4.3s7 files scanned
SKILL.md
- 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).
- Returns:
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.
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.
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