social-sentiment

What This Skill Does

The social-sentiment skill empowers users to conduct comprehensive brand monitoring and public opinion research across major social platforms including Twitter, Reddit, and Instagram. By leveraging the Xpoz MCP integration, this skill allows OpenClaw to search over 1.5 billion indexed posts to extract actionable insights. Unlike superficial social monitoring, this tool is designed for scale; it facilitates the bulk extraction of up to 64,000 posts per query, allowing you to perform deep-dive analysis using integrated Python and pandas workflows. You can identify sentiment trends, detect potential PR crises before they escalate, analyze competitor positioning, and synthesize vast amounts of user feedback into clear, objective data reports.

Installation

To begin, ensure you have the Xpoz MCP configured by running mcporter call xpoz.checkAccessKeyStatus. Once verified, install the skill via the OpenClaw repository: clawhub install openclaw/skills/skills/atyachin/social-analytics Ensure your environment is ready to handle file outputs, as this skill generates CSV files for data processing.

Use Cases

• Brand Reputation Management: Monitor real-time reaction to new product launches or corporate announcements.
• Competitive Intelligence: Compare your brand's sentiment metrics directly against competitors to identify market gaps.
• Crisis Detection: Configure alerts for high-volume negative sentiment patterns to detect viral complaints early.
• Feature Research: Scrape user discussions to extract common pain points or praise regarding specific product features.
• Market Trend Analysis: Track general sentiment around industry-specific topics or keywords.

Example Prompts

1. "Analyze sentiment for Notion, focusing on the latest update, and provide a breakdown of positive versus negative themes."
2. "Compare public sentiment for Figma versus Canva over the last quarter, highlighting top complaints for each."
3. "Is there an emerging PR issue regarding the new iPhone launch? Check Twitter and Reddit for viral negative mentions."

Tips & Limitations

To get the best results, structure your searches to include both broad and narrow queries. Use specific sentiment filters (e.g., combining brand names with 'frustrating' or 'love') to bucket your data effectively. Note that this skill requires the Xpoz MCP to be active; it will not function if authentication is missing. Always ensure your environment can handle large CSV datasets, as performing analysis on full-scale data is computationally intensive. When exporting, verify your file paths to ensure the Python analysis module can access the collected post logs correctly.