alphaear-sentiment
Analyze finance text sentiment using FinBERT or LLM.
Skill by RKiding
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
Use cases: ml, finance
Reviews
Write a reviewNo 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 codecodeload.github.com· found in install commanddashscope.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.
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Signals
- Stars
- 3,084
- Forks
- 383
- Open issues
- 0
- 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
- Source
- GitHubgithub-graphql
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Copies of this SKILL.md published by others; search shows only one of them.
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Cite this page
alphaear-sentiment skill. Agent Mart. https://agentmart.dev/skills/rkiding-alphaear-sentiment (last updated 2026-10-24)