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Generate LinkedIn Activity Reports via Slack Commands with GPT-4.1 and Email
https://n8nworkflows.xyz/workflows/generate-linkedin-activity-reports-via-slack-commands-with-gpt-4-1-and-email-11052
# Generate LinkedIn Activity Reports via Slack Commands with GPT-4.1 and Email
### 1. Workflow Overview
This n8n workflow enables users to generate detailed LinkedIn activity reports by invoking a Slack slash command. When a user types `/check-linkedin [firstName lastName]` in Slack, the workflow extracts the name, finds the corresponding LinkedIn profile using Apify actors, scrapes recent posts, and uses GPT-4.1 AI models to analyze posting frequency, topics, and highlights. Finally, it composes a formatted HTML report and sends it via email to a designated recipient.
The workflow is logically divided into these blocks:
- **1.1 Slack Command Intake & Validation:** Receive Slack slash command input, validate if it contains a full name.
- **1.2 Name Extraction:** Use GPT to extract first and last names from the input.
- **1.3 LinkedIn Profile Lookup:** Use Apify actors to find the LinkedIn profile URL for the extracted name.
- **1.4 LinkedIn Posts Scraping:** Scrape recent LinkedIn posts of the profile.
- **1.5 Posts Aggregation:** Aggregate and structure the scraped posts for analysis.
- **1.6 AI Analysis & Summarization:** Use GPT-4.1 and a Langchain structured output parser to analyze posts and generate a detailed summary.
- **1.7 HTML Report Generation & Email Delivery:** Format the AI summary into an HTML report and email it to the requester.
- **1.8 Error Handling & Slack Feedback:** Notify Slack if the input name is invalid.
---
### 2. Block-by-Block Analysis
---
#### 1.1 Slack Command Intake & Validation
**Overview:**
This block receives the Slack slash command POST request, checks if the input text is a full name, branching to error messaging if not.
**Nodes Involved:**
- Webhook
- GPT 4.1-mini for classification
- Do we have a full name ? (text classifier)
- Tell on slack that no full name was found
**Node Details:**
- **Webhook**
- Type: HTTP Webhook (POST)
- Configuration: Receives Slack slash command at a defined path (placeholder to be replaced)
- Input: Slack slash command payload (JSON)
- Output: Passes the raw Slack command data downstream
- Edge cases: Invalid HTTP method or malformed requests
- **GPT 4.1-mini for classification**
- Type: OpenAI GPT chat model (GPT 4.1-mini)
- Role: Classify if input text is a full name or not
- Input: `{{ $json.body.text }}` (text entered in Slack command)
- Output: Prediction used for branching
- Edge cases: API errors, rate limiting, classification uncertainty
- **Do we have a full name ?**
- Type: Text Classifier node (Langchain)
- Role: Categorize input as "full-name" or "not-full-name"
- InputText: `{{ $json.body.text }}`
- Categories: "full-name", "not-full-name"
- Output: Branches workflow based on category
- Edge cases: Misclassification, expression evaluation failures
- **Tell on slack that no full name was found**
- Type: Slack node (chat message)
- Role: Sends a message back to the Slack channel notifying the user that the input was not a full name
- Configuration: Uses OAuth2 Slack credential, posts to the originating channel `{{ $json.body.channel_id }}`
- Input: Triggered if name classification is negative
- Edge cases: Slack API auth failures, invalid channel IDs
---
#### 1.2 Name Extraction
**Overview:**
Extract first name and last name explicitly from the validated input text using GPT.
**Nodes Involved:**
- GPT 4.1-mini to extract firstName + lastName
- Extract firstName and lastName (Information Extractor node)
**Node Details:**
- **GPT 4.1-mini to extract firstName + lastName**
- Type: OpenAI GPT chat model (GPT 4.1-mini)
- Role: Refine and normalize the name extraction process
- Input: Raw Slack command text
- Output: Passes text to the information extractor node
- Edge cases: API errors, ambiguous names
- **Extract firstName and lastName**
- Type: Langchain Information Extractor
- Role: Identifies and extracts structured attributes: `firstName` and `lastName`
- Input: Text from previous GPT node
- Output: JSON with `firstName` and `lastName` fields (both required)
- Edge cases: Missing attributes, parsing failures
---
#### 1.3 LinkedIn Profile Lookup
**Overview:**
Find the LinkedIn profile URL for the extracted person using Apify actor.
**Nodes Involved:**
- Find Linkedin Profile (Apify actor)
- Get Linkedin Profile URL (Apify dataset query)
**Node Details:**
- **Find Linkedin Profile**
- Type: Apify actor call
- Role: Runs an Apify actor (configured via URL) that searches LinkedIn based on the first and last name
- Input: JSON body with `firstName` and `lastName`
- Output: Returns datasetId for collected profile data
- Edge cases: Actor timeout, invalid input, no profile found
- **Get Linkedin Profile URL**
- Type: Apify dataset query
- Role: Retrieves profile URL from Apify dataset using the datasetId from previous node
- Input: Uses `defaultDatasetId` from previous node
- Output: Extracted LinkedIn profile URL (`linkedinProfileUrl`)
- Edge cases: Dataset empty, API errors
---
#### 1.4 LinkedIn Posts Scraping
**Overview:**
Scrape recent posts of the found LinkedIn profile via another Apify actor.
**Nodes Involved:**
- Scrap what this person posted recently (Apify actor)
- Structure recent posts (Apify dataset query)
- Aggregate (Aggregate node)
**Node Details:**
- **Scrap what this person posted recently**
- Type: Apify actor call
- Role: Runs Apify actor to scrape LinkedIn posts for the given profile URL
- Input: JSON body with `usernames` array containing the LinkedIn profile URL
- Output: Dataset id for scraped posts
- Edge cases: Actor failure, rate limits, empty posts
- **Structure recent posts**
- Type: Apify dataset query
- Role: Retrieves up to 20 recent posts from Apify dataset
- Input: Dataset ID from scraping node
- Output: List of post objects with text and posted_at.date properties
- Edge cases: Empty dataset, partial data
- **Aggregate**
- Type: Aggregate node (standard n8n)
- Role: Collects and merges fields `posted_at.date` and `text` from the posts into a single object
- Input: List of recent posts
- Output: Aggregated JSON data for analysis
- Edge cases: Empty input array, aggregation errors
---
#### 1.5 AI Analysis & Summarization
**Overview:**
Analyze aggregated LinkedIn posts data with GPT 4.1 to generate a detailed analytical summary following a strict JSON schema.
**Nodes Involved:**
- Summer McBriefing (Langchain Agent)
- Structured Output Parser (Langchain Output Parser)
- GPT 4.1 (OpenAI chat model)
**Node Details:**
- **Summer McBriefing**
- Type: Langchain Agent (AI language model with custom prompt)
- Role: Receives aggregated post dates and text, generates a JSON report including posting frequency, topics, highlights, and data quality stats
- Prompt: Detailed instructions to produce exactly the required JSON schema, no extra fields or prose
- Input: Aggregated dates and texts from previous nodes
- Output: JSON object matching LinkedInPostsAnalysis schema
- Edge cases: Parsing ambiguity, incomplete input data, GPT API errors
- **Structured Output Parser**
- Type: Langchain Output Parser
- Role: Validates and enforces JSON structure according to the provided JSON schema
- Input: Raw JSON from Summer McBriefing
- Output: Structured, validated JSON for further processing
- Edge cases: Schema validation failures, parser errors
- **GPT 4.1**
- Type: OpenAI GPT chat model (GPT 4.1)
- Role: Connected as AI languageModel input to Summer McBriefing (used internally)
- Edge cases: API failures, rate limits
---
#### 1.6 HTML Report Generation & Email Delivery
**Overview:**
Generate an HTML report from the structured AI output and send it by email to a configured recipient.
**Nodes Involved:**
- HTML (HTML generation)
- Send report via Email (Gmail node)
- Sticky Note12 (Contact info)
**Node Details:**
- **HTML**
- Type: HTML node (n8n built-in)
- Role: Formats the JSON report into a styled, professional HTML email
- Uses Mustache-style expressions to inject AI output and extracted names into the template
- Includes summary, posting frequency, topics, and highlights in a visually clear layout
- Edge cases: Missing JSON fields, malformed HTML
- **Send report via Email**
- Type: Gmail node (OAuth2)
- Role: Sends the generated HTML email to a fixed recipient (`admin@example.com`)
- Subject includes person’s name dynamically: `Recent LinkedIn Activity about [personName]`
- Requires Gmail OAuth2 credentials
- Edge cases: Email sending failures, auth errors, invalid recipient
- **Sticky Note12**
- Type: Sticky Note
- Role: Contains contact information for questions or feedback (email and LinkedIn)
- No direct functional role in automation
---
#### 1.7 Error Handling & Slack Feedback
**Overview:**
If the input text is not a full name, this block provides user feedback on Slack.
**Nodes Involved:**
- Tell on slack that no full name was found
- Sticky Note1 (Error handling explanation)
**Node Details:**
- **Tell on slack that no full name was found**
- As described in 1.1
- **Sticky Note1**
- Provides a note reminding the user about error handling for no full name found input
---
### 3. Summary Table
| Node Name | Node Type | Functional Role | Input Node(s) | Output Node(s) | Sticky Note |
|-----------------------------------|--------------------------------------|----------------------------------------------|-------------------------------|--------------------------------|---------------------------------------------------------------|
| Webhook | Webhook | Receive Slack slash command request | — | GPT 4.1-mini for classification | |
| GPT 4.1-mini for classification | OpenAI GPT chat model | Classify if input is a full name | Webhook | Do we have a full name ? | |
| Do we have a full name ? | Text classifier (Langchain) | Branch workflow on full name check | GPT 4.1-mini for classification | Extract firstName and lastName / Tell on slack no full name | Sticky Note1: No full name found error handling |
| Tell on slack that no full name was found | Slack message node | Notify user on Slack of invalid input | Do we have a full name ? | — | Sticky Note1: No full name found error handling |
| GPT 4.1-mini to extract firstName + lastName | OpenAI GPT chat model | Extract first and last name from input | Do we have a full name ? | Extract firstName and lastName | |
| Extract firstName and lastName | Langchain Information Extractor | Extract structured firstName and lastName | GPT 4.1-mini to extract names | Find Linkedin Profile | |
| Find Linkedin Profile | Apify actor | Find LinkedIn profile URL from name | Extract firstName and lastName | Get Linkedin Profile URL | Sticky Note3: Data gathering via Apify |
| Get Linkedin Profile URL | Apify dataset query | Retrieve LinkedIn profile URL | Find Linkedin Profile | Scrap what this person posted recently | Sticky Note3: Data gathering via Apify |
| Scrap what this person posted recently | Apify actor | Scrape recent LinkedIn posts | Get Linkedin Profile URL | Structure recent posts | Sticky Note3: Data gathering via Apify |
| Structure recent posts | Apify dataset query | Retrieve structured posts (limit 20) | Scrap what this person posted recently | Aggregate | Sticky Note3: Data gathering via Apify |
| Aggregate | Aggregate node | Aggregate post dates and texts | Structure recent posts | Summer McBriefing | Sticky Note4: Summarization & Intelligence Layer |
| Summer McBriefing | Langchain Agent | Analyze posts and generate JSON report | Aggregate | HTML | Sticky Note4: Summarization & Intelligence Layer |
| Structured Output Parser | Langchain Output Parser | Validate and structure AI output JSON | Summer McBriefing | Summer McBriefing (loop back) | |
| HTML | HTML node | Format JSON summary into styled HTML email | Summer McBriefing | Send report via Email | Sticky Note5: HTML Output & Email |
| Send report via Email | Gmail node | Email the report to recipient | HTML | — | |
| Sticky Note12 | Sticky Note | Contact info for questions and feedback | — | — | |
| Sticky Note | Sticky Note | Workflow overview and instructions | — | — | |
| Sticky Note2 | Sticky Note | Slack intake and parsing explanation | — | — | |
| Sticky Note3 | Sticky Note | Notes on Apify data gathering | — | — | Sticky Note3: Data gathering via Apify |
| Sticky Note4 | Sticky Note | Summarization and AI layer explanation | — | — | Sticky Note4: Summarization & Intelligence Layer |
| Sticky Note5 | Sticky Note | HTML output and email explanation | — | — | Sticky Note5: HTML Output & Response via Email |
| Sticky Note9 | Sticky Note | Video walkthrough link | — | — | # 🎙️ Video Walkthrough @[youtube](-4VwNYsOIPc) |
---
### 4. Reproducing the Workflow from Scratch
1. **Create Webhook Node**
- Type: Webhook
- HTTP Method: POST
- Path: `/path-placeholder-to-be-replaced-by-your-webhook` (replace with your actual path)
- Purpose: Receive Slack slash command payloads
2. **Add GPT 4.1-mini for Classification Node**
- Type: OpenAI GPT chat model
- Model: `gpt-4.1-mini`
- Input: Text from `Webhook` node — `{{$json.body.text}}`
- Purpose: Classify if input is a full name or not
3. **Add Text Classifier Node "Do we have a full name ?"**
- Type: Langchain Text Classifier
- Input Text: `{{$json.body.text}}`
- Categories:
- `full-name` (input is a full name)
- `not-full-name` (otherwise)
- Connect from GPT classification node
- Purpose: Branch the workflow
4. **Add Slack Node for Invalid Name Feedback**
- Type: Slack
- Action: Send Message to Channel
- Channel: `{{$json.body.channel_id}}` (from Webhook)
- Text: `"{{$json.body.text}} is not a full name. Please send a full name to get information."`
- Authentication: OAuth2 (Slack credentials)
- Connect from classifier node on `not-full-name` branch
5. **Add GPT 4.1-mini to Extract First and Last Name**
- Model: `gpt-4.1-mini`
- Input: Slack command text `{{$json.body.text}}`
- Connect from classifier node on `full-name` branch
6. **Add Information Extractor Node**
- Type: Langchain Information Extractor
- Input Text: Output text from previous GPT node
- Attributes:
- `firstName` (required)
- `lastName` (required)
- Connect from GPT extraction node
7. **Add Apify Actor Node "Find Linkedin Profile"**
- Actor URL: `https://console.apify.com/actors/FbqC9BRstFBddhUqj/input`
- Input JSON Body:
```json
{
"firstName": "{{ $json.output.firstName }}",
"lastName": "{{ $json.output.lastName }}"
}
```
- Connect from Information Extractor node
- Credentials: Apify API key
8. **Add Apify Dataset Query Node "Get Linkedin Profile URL"**
- Resource: Datasets
- Dataset ID: `={{ $json.defaultDatasetId }}` (from previous node)
- Connect from "Find Linkedin Profile"
9. **Add Apify Actor Node "Scrap what this person posted recently"**
- Actor URL: `https://console.apify.com/actors/r4oNX7IHlW4RQAjKP`
- Input JSON Body:
```json
{
"usernames": ["{{ $json.linkedinProfileUrl }}"]
}
```
- Connect from "Get Linkedin Profile URL"
- Credentials: Apify API key
10. **Add Apify Dataset Query Node "Structure recent posts"**
- Resource: Datasets
- Dataset ID: `={{ $json.defaultDatasetId }}` (from previous node)
- Limit: 20
- Connect from "Scrap what this person posted recently"
11. **Add Aggregate Node**
- Fields to Aggregate:
- `posted_at.date`
- `text`
- Connect from "Structure recent posts"
12. **Add Langchain Agent Node "Summer McBriefing"**
- Text Input Template:
```
Dates:
{{ $json.date }}
Posts:
{{ $json.text }}
```
- System Message: Detailed analytical instructions to produce JSON summary with posting frequency, topics, highlights, and data quality
- Input: Connect from Aggregate node
- Built-in Tools: GPT 4.1 model (configured)
- Credentials: OpenAI API key
13. **Add Langchain Output Parser Node "Structured Output Parser"**
- Schema: Use the detailed LinkedInPostsAnalysis JSON schema embedded in the workflow
- Connect as AI output parser to "Summer McBriefing" node
14. **Add HTML Node**
- Paste the provided HTML template from the workflow, including placeholders for dynamic data injection using n8n expressions, e.g., `{{ $json.output.latest_post_date }}`
- Connect from "Summer McBriefing" node output
15. **Add Gmail Node "Send report via Email"**
- Send To: `admin@example.com` (replace with desired recipient)
- Subject: `Recent LinkedIn Activity about {{ $('Get Linkedin Profile URL').item.json.personName }}`
- Message: `={{ $json.html }}` (HTML output from previous node)
- Credentials: Gmail OAuth2 credentials configured in n8n
- Connect from HTML node
16. **Set up Slack App and Slash Command**
- Create Slack App with `/check-linkedin` slash command
- Set Request URL to your n8n webhook URL path
- Ensure Slack OAuth2 credentials have `chat:write` and `commands` scopes
17. **Configure Credentials**
- Apify API key with access to required actors
- OpenAI API key with GPT 4.1 and GPT 4.1-mini
- Slack OAuth2 token with required scopes
- Gmail OAuth2 account for sending emails
18. **Testing & Validation**
- Test slash command with valid full names to verify end-to-end functionality
- Test with invalid inputs to verify error handling messages in Slack
- Monitor Apify actor usage and costs
- Monitor OpenAI API usage and costs
---
### 5. General Notes & Resources
| Note Content | Context or Link |
|----------------------------------------------------------------------------------------------------|-----------------------------------------------------------------|
| Slack Slash Command: Run `/check-linkedin [firstName lastName]` in Slack to get LinkedIn activity. | Sticky Note overview at the start of the workflow |
| Workflow requires two Apify actors for LinkedIn profile lookup and post scraping. | Sticky Note3: Data gathering via Apify |
| Summarization and intelligence layer uses GPT 4.1 with a detailed JSON schema for output format. | Sticky Note4: Summarization & Intelligence Layer |
| HTML email format includes profile name, activity stats, topics, and highlights. | Sticky Note5: HTML Output & Response via Email |
| Video walkthrough available on YouTube: `@[youtube](-4VwNYsOIPc)` | Sticky Note9: Video Walkthrough |
| Contact for feedback: Emir Belkahia, email: emir.belkahia@gmail.com, LinkedIn: linkedin.com/in/emirbelkahia | Sticky Note12 |
| Cost considerations: Apify actors and GPT calls incur usage costs; optimize parameters accordingly. | Sticky Note3 and Sticky Note4 |
---
This document comprehensively covers the workflow logic, node configurations, integration points, and reproduction steps to facilitate thorough understanding, modification, and deployment by advanced users or automation agents.