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alphaear-sentiment

Analyze finance text sentiment using FinBERT or LLM.

Skill by RKiding

IndexedApache-2.0★ 3,084
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Install

Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/rkiding-alphaear-sentiment

Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.

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About

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.

Use cases: ml, finance

No reviews yet. Installed it? Reviews come from verified installers only. How reviews work

Permissions

What this skill can do and what it asks for, from its SKILL.md and code. Checked Oct 25, 2026.

Tools
No tools found
Secrets
None asked for
Install scripts
None found
Runs as
Python package

Network hosts it talks to 3

  • api.z.ai · found in code
  • codeload.github.com · found in install command
  • dashscope.aliyuncs.com · found in code

Files it touches

  • ~/.claude/skills/alphaear-sentiment

What's inside

Skill 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.
License
Apache-2.0
Instructions
54 lines in SKILL.md · view on GitHub

Before installing

  • Ships executable scripts

Files (8)

  • SKILL.md
  • __init__.py · scripts/
  • database_manager.py · scripts/
  • capability.py · scripts/llm/
  • factory.py · scripts/llm/
  • router.py · scripts/llm/
  • sentiment_tools.py · scripts/
  • test_sentiment.py · tests/

SKILL.md (first lines)

# 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)

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.

```markdown
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.

From RKiding/Awesome-finance-skills, Apache-2.0.

Signals

Stars
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Forks
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Open issues
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Last commit (this folder)
Feb 3, 2026 (8 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
Apache-2.0
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

alphaear-sentiment skill. Agent Mart. https://agentmart.dev/skills/rkiding-alphaear-sentiment (last updated 2026-10-24)