# Hero LinkedIn Evidence Analyzer (`shashghosh/hero-linkedin-analyzer`) Actor

Analyze supplied LinkedIn or social evidence for thought-leadership, customer-story, and proof signals. This is not a live LinkedIn scraper.

- **URL**: https://apify.com/shashghosh/hero-linkedin-analyzer.md
- **Developed by:** [Shashwat Ghosh](https://apify.com/shashghosh) (community)
- **Categories:** Social media, Automation, Lead generation
- **Stats:** 10 total users, 0 monthly users, 100.0% runs succeeded, 1 bookmarks
- **User rating**: No ratings yet

## Pricing

$3.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

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 examples already wired to this Actor's own input schema, see the [API](#api) section below.

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`).

# README

## Hero LinkedIn Evidence Analyzer — Analyze Supplied LinkedIn/Post Evidence

Hero LinkedIn Evidence Analyzer reviews supplied LinkedIn or social evidence and turns it into structured customer-story intelligence.

### What it does

Paste a LinkedIn post, profile text, company-page excerpt, or exported social evidence. The Actor extracts thought-leadership signals, customer-proof angles, transformation themes, persona relevance, and recommended content/story actions.

### Important disclosure

This Actor is an evidence analyzer, not a live LinkedIn scraper. Provide the text or exported evidence you are allowed to analyze.

### Best for

- Founders and executives analyzing their thought leadership
- Customer marketing teams extracting story signals
- Sales leaders turning social proof into outreach angles
- Agencies reviewing executive/company social evidence

### Example input

```json
{
  "companyName": "Freshworks",
  "linkedinUrl": "https://www.linkedin.com/company/freshworks-inc/",
  "evidenceText": "Freshworks customer teams share stories about faster support resolution and measurable CX outcomes.",
  "analysisType": "all"
}
```

### Output

Rows include `recordType`, `companyName`, `sourceType`, `title`, `snippet`, `heroScore`, `confidence`, `persona`, `heartDimensions`, `status`, and `recommendation`.

### Limitations

Quality depends on the evidence supplied. The Actor does not claim to bypass LinkedIn restrictions or scrape private/social pages directly.

# Actor input Schema

## `companyName` (type: `string`):

Company being analyzed.

## `linkedinUrl` (type: `string`):

Optional reference URL. Used for context only.

## `evidenceText` (type: `string`):

Paste profile summaries, company page text, or LinkedIn post exports to analyze.

## `posts` (type: `array`):

Optional array of post/snippet objects with text, author, url, engagement fields.

## `analysisType` (type: `string`):

Choose which type of LinkedIn/user-provided evidence analysis to perform.

## `testMode` (type: `boolean`):

Run a fast deterministic smoke test using sample evidence.

## Actor input object example

```json
{
  "companyName": "Freshworks",
  "linkedinUrl": "",
  "evidenceText": "CEO shared a customer success story about reducing support resolution time by 40% and improving customer satisfaction.",
  "posts": [],
  "analysisType": "evidence",
  "testMode": false
}
```

# Actor output Schema

## `results` (type: `string`):

Structured canonical rows produced by the actor.

## `summary` (type: `string`):

Human/agent-readable run summary stored in the default key-value store.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "companyName": "Freshworks",
    "evidenceText": "CEO shared a customer success story about reducing support resolution time by 40% and improving customer satisfaction."
};

// Run the Actor and wait for it to finish
const run = await client.actor("shashghosh/hero-linkedin-analyzer").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "companyName": "Freshworks",
    "evidenceText": "CEO shared a customer success story about reducing support resolution time by 40% and improving customer satisfaction.",
}

# Run the Actor and wait for it to finish
run = client.actor("shashghosh/hero-linkedin-analyzer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "companyName": "Freshworks",
  "evidenceText": "CEO shared a customer success story about reducing support resolution time by 40% and improving customer satisfaction."
}' |
apify call shashghosh/hero-linkedin-analyzer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,shashghosh/hero-linkedin-analyzer"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/K2dS94TfCnwzYLDfW/builds/zPCnpmB58TTE8MxZD/openapi.json