# LinkedIn

The **LinkedIn Node** in StackAI enables seamless integration with LinkedIn, the leading professional networking platform. This node allows you to automate LinkedIn-related tasks, such as searching for profiles, extracting professional data, and more, directly within your workflow/

***

**Example of Usage**

Suppose you want to search for professionals in a specific industry and extract their public profile data. You would use the LinkedIn node, select the "Search" action, provide the search keywords as input, and configure any optional filters. The output will be a list of matching LinkedIn profiles with relevant details.

***

**Available Actions**

#### 1. LinkedIn Search

**Description:**\
Search for professionals, companies, or jobs on LinkedIn based on keywords and filters.

**Inputs:**

* **Keywords** (Required): The search terms to find relevant profiles or companies.\
  \&#xNAN;*Example:* `"Data Scientist San Francisco"`
* **Filters** (Optional): Additional filters such as location, industry, or company size.\
  \&#xNAN;*Example:* `{"location": "San Francisco", "industry": "Technology"}`

**Configurations:**

* **Query Filter:** which fields of the JSON to include in the output
* **Top K:** how many results to return
* **LinkedInSearchType:** search generally, or for jobs, companies, or content
* **CountryCode:** the country to search in

**Outputs:**

* **Results** (Always Provided): A list of LinkedIn profiles, companies, or jobs matching the search criteria.\
  \&#xNAN;*Example:*

  ```json
  [
    {
      "name": "Jane Doe",
      "title": "Senior Data Scientist",
      "company": "TechCorp",
      "location": "San Francisco"
    },
    ...
  ]
  ```


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.stackai.com/workflow-builder/apps/linkedin.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.