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from $0.01 / 1,000 results
Extract people who engaged with any LinkedIn post. Get likers, commenters, and reposters as warm leads.
Pricing
from $0.01 / 1,000 results
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0.0
(0)
Developer
John Rippy
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0
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2
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1
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3 days ago
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You can access the LinkedIn Post Engagers - Likers, Commenters, Reposters programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
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Go to Sales Navigator > Search > People, set your filters, then copy the URL from your browser." }, "savedListUrl": { "title": "Saved List URL", "type": "string", "description": "URL of your saved lead list in Sales Navigator. Go to Lists > Lead Lists > click on your list, then copy the URL." }, "accountListUrl": { "title": "Account List URL", "type": "string", "description": "URL of your account list in Sales Navigator. Go to Lists > Account Lists > click on your list, then copy the URL." }, "cookies": { "title": "LinkedIn Cookies (Required)", "type": "string", "description": "JSON array of cookies exported from your browser while logged into Sales Navigator. Use the Cookie-Editor or EditThisCookie browser extension. IMPORTANT: You must have an active Sales Navigator subscription." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 2500, "type": "integer", "description": "Maximum number of leads/accounts to export", "default": 100 }, "includeCompanyDetails": { "title": "Include Company Details", "type": "boolean", "description": "Extract detailed company information for each lead (industry, size, headquarters). Slightly slower.", "default": false }, "includeEngagementSignals": { "title": "Include Engagement Signals", "type": "boolean", "description": "Detect engagement signals like recent activity, job changes, and company growth indicators.", "default": false }, "proxy": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings (recommended: RESIDENTIAL for Sales Navigator)" }, "demoMode": { "title": "Demo Mode", "type": "boolean", "description": "Return mock data for testing without real scraping. Useful to preview output format.", "default": true }, "webhookUrl": { "title": "Webhook URL", "type": "string", "description": "URL to POST results when scraping completes (Zapier, Make, n8n, or custom endpoint)" }, "webhookPlatform": { "title": "Webhook Platform", "enum": [ "zapier", "make", "n8n", "custom" ], "type": "string", "description": "Platform type for proper payload formatting", "default": "custom" }, "webhookHeaders": { "title": "Custom Webhook Headers", "type": "object", "description": "Additional HTTP headers to send with webhook (e.g., Authorization)" } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } }}OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.
OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.
By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.
You can download the OpenAPI definitions for LinkedIn Post Engagers - Likers, Commenters, Reposters from the options below:
If you’d like to learn more about how OpenAPI powers GPTs, read our blog post.
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