# LinkedIn Sales Prep - Latest Posts Before a Call

**Use case:** 

Pre-call research on a target company: 20 most recent LinkedIn posts with author info (name, headline, industry, followers). Know who you're calling, who to name-drop, and what topics they care about right now. One company, one minute, under $0.15 per run. Ideal for BDRs and AEs running discovery calls or ABM plays.

## Input

```json
{
  "companyUrls": [
    "https://www.linkedin.com/company/openai/"
  ],
  "max_posts": 20,
  "date_filter": "month",
  "sort_by": "newest",
  "posted_after_date": "",
  "posted_before_date": "",
  "content_contains": [],
  "include_quote_posts": true,
  "include_reposts": true,
  "skip_empty_rows": false,
  "include_author_profile": true,
  "include_post_extras": false,
  "include_comments": false,
  "max_comments_per_post": 0,
  "include_company_report": false,
  "include_strategy_summary": false
}
```

## Output

```json
{
  "post_url": {
    "label": "Post URL",
    "format": "link"
  },
  "content": {
    "label": "Content",
    "format": "text"
  },
  "author_name": {
    "label": "Author",
    "format": "text"
  },
  "author_public_id": {
    "label": "Author public id",
    "format": "text"
  },
  "author_headline": {
    "label": "Author headline",
    "format": "text"
  },
  "author_industry": {
    "label": "Author industry",
    "format": "text"
  },
  "author_location": {
    "label": "Author location",
    "format": "text"
  },
  "author_followers": {
    "label": "Author followers",
    "format": "number"
  },
  "posted_at": {
    "label": "Posted at",
    "format": "date"
  },
  "likes": {
    "label": "Likes",
    "format": "number"
  },
  "comments": {
    "label": "Comments",
    "format": "number"
  },
  "shares": {
    "label": "Shares",
    "format": "number"
  },
  "reactions": {
    "label": "Reactions",
    "format": "text"
  },
  "hashtags": {
    "label": "Hashtags",
    "format": "array"
  },
  "mentions": {
    "label": "Mentions",
    "format": "array"
  },
  "engagement_rate": {
    "label": "Engagement rate"
  },
  "post_performance_percentile": {
    "label": "Post performance percentile"
  },
  "posted_day_of_week": {
    "label": "Posted day of week"
  },
  "posted_hour_utc": {
    "label": "Posted hour utc"
  },
  "top_comments_count": {
    "label": "Top comments count"
  },
  "company_url": {
    "label": "Company",
    "format": "link"
  },
  "scraped_at": {
    "label": "Scraped at",
    "format": "date"
  },
  "error": {
    "label": "Error"
  }
}
```

## About this Actor

This example demonstrates how to use [LinkedIn Company Posts Scraper - Bulk Export [NO LOGIN] ✅](https://apify.com/unseenuser/company-posts.md) with a specific input configuration. Visit the [Actor detail page](https://apify.com/unseenuser/company-posts.md) to learn more, explore other use cases, and run it yourself.


## How to integrate an Actor?

This Task's input is already configured above. Use it as-is rather than inventing a new one.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For full API examples (JavaScript, Python, CLI, MCP, OpenAPI), see this Task's Actor page: https://apify.com/unseenuser/company-posts.md

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).