# LinkedPulse – LinkedIn Activity Intelligence \[No Cookies] (`saasydb/linkedpulse-linkedin-activity-intelligence`) Actor

Check whether someone is actually active on LinkedIn before adding them to outbound — no cookies required. See 7/30/90-day activity signals, outreach readiness, and the topics they engage with.

- **URL**: https://apify.com/saasydb/linkedpulse-linkedin-activity-intelligence.md
- **Developed by:** [Stuart](https://apify.com/saasydb) (community)
- **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $30.00 / 1,000 activity checks

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

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

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.
Actors are written with capital "A".

## 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.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## LinkedPulse — LinkedIn Activity Intelligence

**Find out whether a prospect is actually active on LinkedIn before putting them into your outbound workflow.**

LinkedPulse analyzes observable LinkedIn activity for a profile and turns it into simple, GTM-ready signals.

Instead of treating every LinkedIn profile as equally valuable for LinkedIn outreach, LinkedPulse helps answer two questions:

1. **Is this person actually active on LinkedIn?**
2. **What kinds of topics are they engaging with?**

Use it to qualify prospect lists, prioritize LinkedIn outreach, enrich GTM data, or add LinkedIn activity signals to your own applications and automations.

### Two analysis modes

#### Activity Check — $0.03 per profile

A fast, inexpensive check designed for list qualification and enrichment.

For each LinkedIn profile, LinkedPulse returns:

- Activity observed within the last 7 days
- Activity observed within the last 30 days
- Activity observed within the last 90 days
- Activity status
- Activity level
- Activity confidence
- Recent activity evidence
- Observable post/comment evidence when available
- LinkedIn outreach readiness
- Activity evidence counts
- Data-quality warnings

A typical result looks like:

```json
{
  "activity_status": "active",
  "activity_level": "moderate",
  "activity_confidence": "high",
  "active_7d": false,
  "active_30d": true,
  "active_90d": true,
  "recent_activity_evidence_at": "2026-09-02T11:39:50+00:00",
  "reaction_evidence_count": 100,
  "recent_reaction_evidence_count_7d": 0,
  "recent_reaction_evidence_count_30d": 6,
  "recent_reaction_evidence_count_90d": 17,
  "linkedin_outreach_ready": true
}
```

**Price: $0.03/profile ($30 per 1,000 profiles)**

### Activity Intelligence — $0.10 per profile

Activity Intelligence includes the Activity Check plus analysis of the subjects and themes appearing in the person's observable LinkedIn engagement.

It adds:

- `engagement_topics`
- `engagement_summary`
- `outreach_signals`

Example:

```json
{
  "engagement_topics": [
    "Ecommerce UX and conversion optimization",
    "Career progression and leadership development",
    "Professional learning and certifications",
    "Events, conferences, and industry networking",
    "Branding, design, and creative leadership"
  ],
  "engagement_summary": "The profile demonstrates consistent interest in ecommerce optimization, leadership and professional growth, ongoing learning, industry events, and branding/design topics.",
  "outreach_signals": [
    "Interest in ecommerce optimization and Shopify-related content",
    "Engagement with leadership development and career advancement posts",
    "Affinity for professional certifications and badges",
    "Active interest in industry events and conferences",
    "Engagement with branding, design, and creative leadership themes"
  ]
}
```

**Price: $0.10/profile ($100 per 1,000 profiles)**

### Why LinkedIn activity matters

A LinkedIn profile existing doesn't mean the person actually uses LinkedIn.

That distinction matters when you're:

- Building prospect lists
- Running LinkedIn outbound
- Prioritizing SDR outreach
- Enriching CRM records
- Building GTM workflows
- Deciding which prospects deserve manual attention
- Personalizing outreach based on demonstrated interests

Someone showing recent observable activity is much more interesting for LinkedIn outreach than a profile with no recent activity evidence.

LinkedPulse turns that into structured data you can use programmatically.

### Input

Provide one or more LinkedIn profile URLs:

```text
https://www.linkedin.com/in/example-person
```

Then choose:

- **Activity Check** — activity and outreach-readiness signals
- **Activity Intelligence** — activity signals plus engagement topics and outreach intelligence

You can process a single profile or submit a list of profiles in one Actor run.

### Example Activity Intelligence output

```json
{
  "success": true,
  "analysis_type": "activity_intelligence",
  "activity_status": "active",
  "activity_level": "moderate",
  "activity_confidence": "high",
  "active_7d": false,
  "active_30d": true,
  "active_90d": true,
  "recent_activity_evidence_at": "2026-09-02T11:39:50+00:00",
  "most_recent_post_at": "2026-01-27T03:49:16+00:00",
  "most_recent_comment_at": null,
  "reaction_evidence_count": 100,
  "recent_reaction_evidence_count_7d": 0,
  "recent_reaction_evidence_count_30d": 6,
  "recent_reaction_evidence_count_90d": 17,
  "linkedin_outreach_ready": true,
  "data_quality_warning": false,
  "linkedin_url": "https://www.linkedin.com/in/example-person",
  "username": "example-person",
  "engagement_topics": [
    "Ecommerce",
    "Leadership development",
    "Professional learning"
  ],
  "engagement_summary": "The profile shows recent observable activity and recurring engagement with ecommerce, leadership, and professional-development content.",
  "outreach_signals": [
    "Interest in ecommerce optimization",
    "Engagement with leadership content",
    "Interest in professional development"
  ],
  "status_code": 200
}
```

### Use LinkedPulse through the API

LinkedPulse can be run programmatically through the Apify API, making it easy to incorporate into your own GTM systems.

Common workflows include:

```text
Prospect list
    ↓
LinkedPulse Activity Check
    ↓
Active in last 30 days?
    ↓
Yes → prioritize for LinkedIn outreach
No  → deprioritize or use another channel
```

Or:

```text
Prospect
    ↓
LinkedPulse Activity Intelligence
    ↓
Activity + engagement topics
    ↓
CRM / enrichment workflow / outbound system
```

Apify provides ready-to-use integrations for Python, JavaScript, HTTP, OpenAPI, CLI, and MCP.

### Understanding the activity data

LinkedPulse measures **observable LinkedIn activity**.

Recent activity evidence can establish that a person has been active within a particular time window, but it should not be interpreted as LinkedIn's private "last login" timestamp.

For example, engagement with recently published content provides evidence that the profile was active after that content became available.

Because private behavior such as reading the feed or sending direct messages is not observable, a lack of recent evidence does not prove that someone has not logged into LinkedIn.

LinkedPulse is designed as a **prospecting and prioritization signal**, not an account-login tracker.

### Activity statuses

LinkedPulse classifies profiles into useful activity states:

- **Active** — recent activity evidence within 30 days
- **Stale** — activity observed within 90 days, but not within 30 days
- **Inactive** — activity evidence exists, but none was observed within 90 days
- **No activity observed** — no usable activity evidence was returned

Activity levels and confidence provide additional context for ranking prospects.

### Built for GTM workflows

LinkedPulse is useful for:

- GTM engineers
- RevOps teams
- Lead generation agencies
- List builders
- SDR teams
- LinkedIn automation platforms
- Enrichment providers
- Sales intelligence products
- AI agents and automated prospecting workflows

The goal is simple:

**Don't just ask whether someone has a LinkedIn profile. Ask whether LinkedIn is actually a useful channel for reaching them.**

# Actor input Schema

## `linkedin_urls` (type: `array`):

Enter one or more LinkedIn profile URLs to analyze.

## `analysis_type` (type: `string`):

Activity Check determines whether the person is active. Activity Intelligence also analyzes what they engage with and surfaces outreach signals.

## Actor input object example

```json
{
  "linkedin_urls": [
    "https://www.linkedin.com/in/stuartbrent"
  ],
  "analysis_type": "activity_check"
}
```

# Actor output Schema

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

No description

# 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("saasydb/linkedpulse-linkedin-activity-intelligence").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 = {}

# Run the Actor and wait for it to finish
run = client.actor("saasydb/linkedpulse-linkedin-activity-intelligence").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 '{}' |
apify call saasydb/linkedpulse-linkedin-activity-intelligence --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,saasydb/linkedpulse-linkedin-activity-intelligence"
        }
    }
}
```

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/hl249YpnGeGjGUjJb/builds/RDvY7f7t9oyLf0VQl/openapi.json