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DataPython

AlphaEar Sentiment

by RKiding

AlphaEar Sentiment is a Data skill for Claude Code, published by RKiding in Awesome-finance-skills.

2.8K stars362 forkson RKiding/Awesome-finance-skillsAdded 2026/08/17Repository updated 2026/03/29
agentagent-skillsfinancesfintech
Install in seconds
Install AlphaEar Sentiment
Copy AlphaEar Sentiment into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.
terminal
npx degit https://github.com/RKiding/Awesome-finance-skills/tree/main/skills/alphaear-sentiment ~/.claude/skills/alphaear-sentiment

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/RKiding/Awesome-finance-skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file
skills/alphaear-sentiment/SKILL.md in RKiding/Awesome-finance-skills
Installs to
~/.claude/skills/alphaear-sentiment
Collection
One of 10 skills cataloged from this repository
Category
Data668 skills

What AlphaEar Sentiment does

AlphaEar Sentiment analyzes financial text and news to determine sentiment polarity and score using local FinBERT or an LLM prompt. Use it when you need positive, negative, or neutral labels with scores for market-related content.

AlphaEar Sentiment is cataloged under Data on DirSkills. AlphaEar Sentiment comes from a repository tagged agent, agent-skills, finances and fintech.

Documentation

README

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)

This is the opening of the README. Read the full README on GitHub.

Frequently asked about AlphaEar Sentiment

  • What else does RKiding publish alongside AlphaEar Sentiment?

    AlphaEar Sentiment is one of 10 skills that DirSkills catalogs from RKiding/Awesome-finance-skills, the repository it ships in. Its siblings there include AlphaEar DeepEar Lite, AlphaEar Logic Visualizer and AlphaEar News. Each one is a separate skill with its own page in this directory, installs the same way AlphaEar Sentiment does, and is maintained by RKiding in that same repository. The rest of the collection is listed on the RKiding/Awesome-finance-skills page.

  • How does AlphaEar Sentiment compare to other Data skills?

    AlphaEar Sentiment ranks #292 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of AlphaEar Sentiment against them. Open each page to compare what they document and how they install.

More from RKiding/Awesome-finance-skills

AlphaEar Sentiment is one of 10 skills cataloged on DirSkills from RKiding/Awesome-finance-skills.

See all 10 skills