A professional tool for analyzing crypto market sentiment and generating trading signals from news and social media.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install news-sentiment
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install news-sentiment using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
The News Sentiment Analyzer is a powerful AI-driven tool designed to scan multiple information streams, including Twitter, Reddit, and global news sites, to gauge market sentiment. It leverages large language models via Dashscope to process complex text and provide actionable insights for cryptocurrency traders.
By integrating this skill into your workflow using Openclaw Skills, you can automate the process of filtering noise from high-value market data. This skill helps users identify whether specific coins or the general market are trending bullish, bearish, or neutral, allowing for data-driven decision-making in fast-paced financial environments.
To get started with this skill, ensure you have python3 and curl installed on your system. You will also need a valid Dashscope API key.
# Set your API key environment variable
export DASHSCOPE_API_KEY='your_api_key_here'
# Analyze sentiment for a specific coin
/news-sentiment analyze BTC
# View general market sentiment
/news-sentiment market
The skill organizes sentiment data using a structured format to provide clarity on market positions:
| Field | Type | Description |
|---|---|---|
coin |
String | The cryptocurrency ticker symbol (e.g., BTC, ETH) |
sentiment_score |
Float | Value from 0.0 to 1.0 indicating sentiment strength |
signal |
String | The resulting trading signal (Bullish, Bearish, or Neutral) |
sources |
Array | List of specific data sources analyzed during the request |
timestamp |
ISO8601 | The exact time the sentiment analysis was performed |
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