Pull rich data about people and companies.
Find Social Media Profiles from Email. Cost: 3 credit/successful request See the documentation.
Run any Go code and use any Go package available with a simple import. Refer to the Pipedream Go docs to learn more.
Lookup work email address of a LinkedIn Person Profile. If you provided a webhook in your request parameter, our application will call your webhook with the result once. Cost: 3 credit/successful request See the documentation.
Retrieve Company Metadata from LinkedIn URL. Cost: 1 credit/successful request See the documentation.
The Proxycurl API is a tool for scraping LinkedIn data, enabling users to extract professional information from LinkedIn profiles and company pages. When integrated with Pipedream's serverless platform, Proxycurl can automate the collection of LinkedIn data, which can be used for lead generation, market research, or recruitment. Pipedream's capabilities allow users to create workflows that respond to various triggers, process the data with Proxycurl, and then output it to desired destinations or further manipulate it with other apps and services.
import { axios } from "@pipedream/platform"
export default defineComponent({
props: {
proxycurl: {
type: "app",
app: "proxycurl",
}
},
async run({steps, $}) {
return await axios($, {
url: `https://nubela.co/proxycurl/api/credit-balance`,
headers: {
Authorization: `Bearer ${this.proxycurl.$auth.api_key}`,
},
})
},
})
You can execute custom Go scripts on-demand or in response to various triggers and integrate with thousands of apps supported by Pipedream. Writing with Go on Pipedream enables backend operations like data processing, automation, or invoking other APIs, all within the Pipedream ecosystem. By leveraging Go's performance and efficiency, you can design powerful and fast workflows to streamline complex tasks.
package main
import (
"fmt"
pd "github.com/PipedreamHQ/pipedream-go"
)
func main() {
// Access previous step data using pd.Steps
fmt.Println(pd.Steps)
// Export data using pd.Export
data := make(map[string]interface{})
data["name"] = "Luke"
pd.Export("data", data)
}