alphaear-sentiment
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.
npx skills add RKiding/Awesome-finance-skills --skill alphaear-sentiment --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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)`: G
What does the alphaear-sentiment skill do?
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.
How do I install it?
Run `npx skills add RKiding/Awesome-finance-skills --skill alphaear-sentiment --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From RKiding/Awesome-finance-skills, a repository with 2,750 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.