Expanso Sentiment Score

What This Skill Does

The Expanso Sentiment Score skill, developed by aronchick, provides a robust mechanism for analyzing the emotional tone of text-based data. By leveraging the Expanso Edge runtime, this skill processes input strings and assigns a numerical value ranging from -1 (highly negative) to +1 (highly positive). This transformation of qualitative language into quantitative data allows OpenClaw agents to interpret user feedback, social media discourse, or customer support logs programmatically. It acts as a lightweight sentiment engine that can be integrated directly into CLI workflows or served via an MCP pipeline, ensuring that sentiment analysis is portable, containerized, and highly performant.

Installation

To begin using this skill, ensure you have the expanso-edge binary installed and available in your system PATH. Once the prerequisite is met, install the skill directly through the ClawHub ecosystem using the following command:

clawhub install openclaw/skills/skills/aronchick/expanso-sentiment-score

After installation, you can verify the setup by running the pipeline file via expanso-edge. For advanced users, the skill can also be deployed to the Expanso Cloud by utilizing the expanso-cli to point directly at the pipeline-cli.yaml configuration file.

Use Cases

This skill is ideal for teams looking to automate sentiment monitoring across various text streams. Common use cases include:

• Customer Experience: Automatically scoring incoming support tickets to prioritize negative sentiment feedback for human escalation.
• Social Listening: Processing tweet or forum data to gauge public perception of brand announcements.
• Product Development: Analyzing internal Slack channel discussions to identify friction points or morale shifts within project teams.
• Content Filtering: Automatically tagging user-generated content that falls below a certain sentiment threshold for moderation.

Example Prompts

1. "Analyze the sentiment of the following customer review: 'The product is intuitive, but the setup process was incredibly frustrating.' and store the score in the project log."
2. "Review the text file at data/comments.txt, calculate the average sentiment score, and generate a summary report."
3. "If the sentiment score of the latest incoming feedback is below -0.5, alert the team in the #critical-issues channel."

Tips & Limitations

The Sentiment Score skill operates best on short-to-medium length text blocks. While it provides a precise numerical output, it may struggle with highly sarcastic language, complex metaphors, or domain-specific jargon where sentiment is context-dependent. For the best accuracy, sanitize your inputs to remove URLs or extraneous boilerplate code before sending them to the pipeline. Because the skill relies on the Expanso Edge environment, ensure your network allows for edge-based process execution if deploying via the cloud or an MCP server.