LinkedIn-Post-Interaktionsdaten-Extraktor---Vorlage

• •Keine besonderen Voraussetzungen, sofort nach Import nutzbar

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        "prompt": "You are looking at a LinkedIn post.\nYour job is to extract engagement data from the post.\n\nExtract the information of the people that have commented and reacted to this post. I want their name, their job title and their profile_url.\n\nI also want the total number of reactions, comments and reposts. For each of these values, if you are unable to find it, set it as -1.",
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        "content": "## Extract engagmenet data\nWith airtop and a simple prompt, the agent is able to extract information from a LinkedIn post like:\n- Amount of comments on the post\n- Amount of reactions on the post\n- Amount of reposts\n- A list of commenters, with their name, job title and profile url"
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        "content": "README\n\n# LinkedIn Post Engagement Data Extractor\n\n## Use Case\nThis automation is designed to extract key engagement metrics and audience data from a LinkedIn post. It's useful for analyzing the impact of content and identifying engaged users for lead generation, marketing, or research purposes.\n\n## What It Does\nGiven a LinkedIn post URL and an Airtop profile, this automation extracts:\n\n- Total number of reactions\n- Total number of comments\n- Total number of reposts\n- A list of users who reacted or commented, including:\n  - Their full name\n  - Their job title\n  - A link to their LinkedIn profile\n\n## Input Parameters\n\n| Name             | Description                                                | Required |\n|------------------|------------------------------------------------------------|----------|\n| airtop_profile   | The name of an [Airtop Profile](https://portal.airtop.ai/browser-profiles) that's logged into LinkedIn | Yes      |\n| linkedin_post_url| The full URL of the LinkedIn post you want to analyze     | Yes      |\n\n## How It Works\n\n1. The workflow starts when triggered manually or from another workflow/form.\n2. It maps input fields for Airtop profile and post URL.\n3. Airtop opens a browser session and loads the LinkedIn post.\n4. An AI agent is instructed to extract engagement data via prompt-based analysis.\n5. The response is parsed and output in a structured format.\n\n## Output Format\n\nThe output will be a structured JSON object with the following fields:\n\n```json\n{\n  \"interactors\": [\n    {\n      \"name\": \"Jane Doe\",\n      \"job_title\": \"Marketing Director at ExampleCorp\",\n      \"profile_url\": \"https://linkedin.com/in/janedoe\"\n    }\n    // ... more interactors\n  ],\n  \"reactions_count\": 153,\n  \"comments_count\": 21,\n  \"reposts_count\": 8\n}\n"
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