🔎 Scrape social media metrics across social networks
git clone https://github.com/oscarmmv/social-crawler.gitThe social-crawler skill is designed to scrape social media metrics across various social networks, enabling marketers to efficiently gather and analyze data. This intermediate-level Claude Code skill allows users to automate the collection of vital social media statistics, making it easier to track performance and optimize marketing strategies. By leveraging this skill, users can save valuable time that would otherwise be spent manually gathering data from multiple platforms. Key benefits of the social-crawler skill include the ability to streamline data collection processes and enhance decision-making through data-driven insights. Although the time savings are currently unknown, the automation of this task can significantly reduce the workload for marketing teams, allowing them to focus on higher-value activities such as campaign strategy and content creation. The skill's implementation is straightforward, taking approximately 30 minutes, making it accessible even for those with intermediate technical skills. This skill is particularly beneficial for marketers, product managers, and data analysts who need to monitor social media performance and gain insights into audience engagement. By automating the scraping of metrics, users can quickly compile reports and make informed decisions based on real-time data. For instance, a marketing team can use the skill to compare engagement rates across different platforms, identify trends, and adjust their strategies accordingly. The social-crawler skill fits seamlessly into AI-first workflows by enhancing the capabilities of AI agents and enabling them to perform complex data gathering tasks autonomously. With a growing emphasis on AI automation in marketing, this skill represents a significant advancement in workflow automation. As businesses increasingly rely on data-driven strategies, integrating the social-crawler skill into your toolkit can provide a competitive edge in understanding and leveraging social media dynamics.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/oscarmmv/social-crawlerCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Act as a social media analytics tool. Scrape and analyze the latest metrics for [COMPANY] across [PLATFORMS] (e.g., Facebook, Instagram, Twitter, LinkedIn). Provide insights on engagement, follower growth, and content performance. Focus on the last [TIMEFRAME] (e.g., 30 days, 6 months).
# Social Media Metrics Analysis for [COMPANY] ## Engagement Overview - **Total Engagements**: 12,450 (up 15% from last period) - **Average Engagement Rate**: 4.2% (industry benchmark: 3.8%) - **Top Performing Post**: "[POST TITLE]" with 2,100 engagements ## Follower Growth - **New Followers**: 1,200 (net growth of 800 after unfollows) - **Follower Growth Rate**: 5.3% (industry average: 3.1%) - **Top Source of New Followers**: Instagram Stories (35% of new followers) ## Platform Performance - **Facebook**: 6,200 engagements (2.8% engagement rate) - **Instagram**: 4,500 engagements (7.1% engagement rate) - **Twitter**: 1,750 engagements (3.5% engagement rate) - **LinkedIn**: 1,000 engagements (5.8% engagement rate) ## Recommendations - Leverage Instagram Stories for higher engagement - Experiment with LinkedIn content to capitalize on high engagement rate - Analyze top-performing post to replicate successful content strategy
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