n8nworkflows.xyz/workflows/Generate LinkedIn posts using Telegram, Supabase vector DB and OpenAI RAG-11385/readme-11385.md

Generate LinkedIn posts using Telegram, Supabase vector DB and OpenAI RAG

https://n8nworkflows.xyz/workflows/generate-linkedin-posts-using-telegram--supabase-vector-db-and-openai-rag-11385

Generate LinkedIn posts using Telegram, Supabase vector DB and OpenAI RAG

1. Workflow Overview

Purpose: This n8n workflow builds and uses a personal “viral LinkedIn posts” knowledge base. It has two modules:

1. Collect viral posts: a Telegram bot accepts a LinkedIn post URL, scrapes the post text, generates embeddings, and stores it in a Supabase vector store.
2. Generate new posts (RAG): a web form collects a hook, then a multi-agent AI pipeline analyzes the hook, produces a post outline, retrieves similar posts from the vector DB (RAG), and generates a final LinkedIn post that can be auto-published to LinkedIn.

1.1 Viral Post Collection (Telegram → Scrape → Vector DB)

• Trigger on Telegram message
• Validate URL contains linkedin.com
• Fetch LinkedIn HTML and extract post commentary
• Load as document, embed with OpenAI, insert into Supabase vector table
• Notify user of success/failure

1.2 Post Generation (Form → Agents → RAG → LinkedIn publish)

• Trigger via n8n Form
• Agent 1: analyze hook into structured fields (topic/niche/tone/key points)
• Agent 2: generate a 5-part outline (hook/problem/value/solution/CTA)
• Agent 3: retrieve similar posts from Supabase as a tool (RAG) and ghostwrite a post
• Publish via LinkedIn node

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2. Block-by-Block Analysis

Block A — Telegram Intake & URL Validation

Overview: Receives a Telegram message, shows a typing indicator, and routes execution based on whether the message looks like a LinkedIn URL.
Nodes involved: On Telegram Message, Typing...., If, Wrong URL

Node: On Telegram Message

• Type / role: telegramTrigger — entry point for Telegram bot messages.
• Config: Listens to message updates.
• Key data used later: $json.message.text, $json.message.chat.id
• Outputs: Feeds both If and Typing.... in parallel.
• Failure modes: Telegram credential revoked; webhook not registered; bot not started by user.

Node: Typing....

• Type / role: telegram — sends chat action for UX.
• Config: Operation sendChatAction, chatId = {{ $json.message.chat.id }}
• Inputs: From trigger message.
• Failure modes: Telegram API errors (403 if bot blocked, 400 bad chat id).

Node: If

• Type / role: if — validates message content.
• Condition:
  {{ $json.message.text && $json.message.text.includes("linkedin.com") }}
• Routing:
  • True → LinkedIn Post URL
  • False → Wrong URL
• Edge cases: URLs without linkedin.com (shorteners), empty text messages, media-only messages.

Node: Wrong URL

• Type / role: telegram — user feedback.
• Config: Sends “⚠️ Please provide a valid LinkedIn post URL” to
  chatId = {{ $('On Telegram Message').item.json.message.chat.id }}
• Edge cases: The node references the trigger node by name; renaming the trigger breaks this expression.

---

Block B — Scraping LinkedIn Post Content

Overview: Fetches the URL HTML, extracts the post text via CSS selector, and handles scraping failures.
Nodes involved: LinkedIn Post URL, Scrap Content, Unable to Scrape

Node: LinkedIn Post URL

• Type / role: httpRequest — fetch HTML of the provided LinkedIn URL.
• Config:
  • url = {{ $('On Telegram Message').item.json.message.text }}
  • onError: continueErrorOutput (important: prevents workflow from stopping on HTTP error)
• Outputs:
  • Main success output → Scrap Content
  • Error output → Unable to Scrape (via the second connection path)
• Failure modes / edge cases:
  • LinkedIn blocks scraping (401/403, bot detection, requires cookies/login).
  • Redirects, regional pages, consent walls.
  • Non-HTML responses, rate limits, timeouts.
  • If URL points to a post that requires authentication, extraction will likely fail.

Node: Scrap Content

• Type / role: html — extracts post content from HTML.
• Config: Operation extractHtmlContent with:
  • Key: Post Content
  • CSS selector: [data-test-id="main-feed-activity-card__commentary"]
• Input: HTML from LinkedIn Post URL.
• Output: JSON containing extracted “Post Content”.
• Failure modes / edge cases:
  • Selector changes (LinkedIn frequently changes DOM).
  • Multiple matches or empty match -> blank content.
  • Content loaded dynamically (not in initial HTML).

Node: Unable to Scrape

• Type / role: telegram — failure notification.
• Config: Sends “😶 Scraping failed for this LinkedIn post” to the trigger chat id.
• When it runs: When HTTP request errors (and potentially when scrape produces unusable content, though the current logic only wires HTTP error output).
• Gap: There is no explicit “empty extracted content” check; an empty scrape may still proceed to storage.

---

Block C — Vector DB Ingestion (Supabase + OpenAI Embeddings)

Overview: Converts scraped post text into embeddings and inserts it into a Supabase vector table for later RAG retrieval.
Nodes involved: Upload Document, Data Loader, Embeddings, Code, ✅ Post Scrapped Sucessfully

Node: Data Loader

• Type / role: documentDefaultDataLoader (LangChain) — converts incoming item data into a “Document” for vector insertion.
• Config: Defaults (no custom mapping shown).
• Connection: Provides ai_document input to Upload Document.
• Edge cases: If “Post Content” is missing/empty, the produced document may be empty or poorly structured depending on defaults.

Node: Embeddings

• Type / role: embeddingsOpenAi — generates embedding vectors.
• Config: Uses OpenAI credentials “n8n - Vekrro”; default embedding model depends on node defaults/version.
• Connection: Provides ai_embedding to Upload Document.
• Failure modes: OpenAI auth errors, quota limits, rate limiting, model deprecation.

Node: Upload Document

• Type / role: vectorStoreSupabase (LangChain) — inserts documents + embeddings into Supabase.
• Mode: insert
• Table: linkedin_post
• Embedding batch size: 500
• Dependencies: Requires both ai_document (from Data Loader) and ai_embedding (from Embeddings).
• Failure modes / edge cases:
  • Supabase credentials invalid.
  • Table missing or schema mismatch (vector column dimensions must match embedding output).
  • Row-level security policies blocking inserts.
  • Large content leading to token/size constraints upstream.

Node: Code

• Type / role: code — reshapes output for the next step.
• JS: return $('Upload Document').all()[0]
• Meaning: Returns the first item from the Upload Document node’s output, ignoring current input.
• Edge cases:
  • If Upload Document outputs 0 items, this throws.
  • Strong coupling to node name; renaming breaks it.

Node: ✅ Post Scrapped Sucessfully

• Type / role: telegram — success confirmation.
• Config: Sends “✅ Post Scraped successfully” to trigger chat id.
• Note: Spelling in node name is “Scrapped Sucessfully”, message says “Scraped”.

---

Block D — Web Form Entry for Post Generation

Overview: Provides a web form endpoint to input a hook (and optional image), initiating the AI generation chain.
Nodes involved: LinkedIn Form

Node: LinkedIn Form

• Type / role: formTrigger — second entry point (independent of Telegram).
• Config:
  • Path: linkedin-post
  • Title: “LinkedIn - Bhavy Shekhaliya”
  • Button label: “Generate”
  • Ignore bots: enabled
  • Fields:
    • Hook (textarea, required)
    • Post Image (optional; type not explicitly set in JSON, appears as a default text field)
• Output: Feeds Hook Analyse Agent.
• Failure modes: Public form abuse if not protected; path conflicts; bot protection limitations.

---

Block E — Multi-Agent Analysis + Structured Outputs

Overview: Uses LLM agents with structured output parsers to convert free text into machine-usable JSON, then builds an outline.
Nodes involved: Hook Analyse Agent, Structured Output Parser, Post Structure Agent, Structured Output Parser., 4o mini, 2.5-flash, 5 nano

Node: 4o mini

• Type / role: lmChatOpenAi — primary LLM for agents.
• Model: gpt-4o-mini
• Connections: Supplies ai_languageModel to all three agents as index 0 (primary).
• Failure modes: OpenAI credential issues, rate limits.

Node: 2.5-flash

• Type / role: lmChatGoogleGemini — fallback LLM for agents.
• Connections: Supplies ai_languageModel to agents as index 1 (fallback).
• Failure modes: Google credential/config issues (not shown in JSON), quota limits.

Node: Hook Analyse Agent

• Type / role: agent — extracts structured hook metadata.
• Prompt: Analyzes {{ $json.Hook }} and extracts:
  • topic
  • niche/industry
  • emotion/tone
  • 3–5 key points
• Config: needsFallback: true, hasOutputParser: true
• Inputs: Main input from LinkedIn Form; LLM from 4o mini + fallback 2.5-flash; parser from Structured Output Parser.
• Output: Feeds Post Structure Agent.
• Failure modes: Model returns non-JSON; parser auto-fix may still fail on very malformed outputs.

Node: Structured Output Parser

• Type / role: outputParserStructured — enforces JSON schema for Hook analysis.
• AutoFix: true
• Schema example: topic/niche/tone/key_points array.
• LLM dependency: Uses 5 nano as ai_languageModel (shared across parsers).
• Edge cases: If the hook is very short/ambiguous, keys may be hallucinated; schema still forces fields.

Node: Post Structure Agent

• Type / role: agent — generates a 5-section outline from analyzed hook.
• Prompt inputs:
  Topic: {{ $json.output.topic }}, etc.
• Config: needsFallback: true, hasOutputParser: true
• Parser: Structured Output Parser.
• Output: Feeds Post Generator Agent.
• Potential issue: In the Post Generator prompt, Problem is mapped to {{ $json.output.structure.value }} (likely a mistake; should be .problem).

Node: Structured Output Parser.

• Type / role: structured parser for outline.
• AutoFix: true
• Schema example: { structure: { hook, problem, value, solution, cta } }
• LLM dependency: 5 nano
• Edge cases: If the agent produces extra keys, they may be dropped/rewritten.

Node: 5 nano

• Type / role: lmChatOpenAi — used by output parsers for auto-fix / schema correction.
• Model: gpt-5-nano
• Connections: Feeds all three structured parsers.
• Failure modes: Access to GPT-5 models may require specific OpenAI account entitlements.

---

Block F — RAG Retrieval + Post Generation + LinkedIn Publishing

Overview: Retrieves similar viral posts from Supabase vector store as a tool (RAG) and generates the final post, then publishes it to LinkedIn.
Nodes involved: Supabase Vector Store, Embeddings., Post Generator Agent, Structured Output Parser1, Create a post

Node: Embeddings.

• Type / role: embeddingsOpenAi — embeddings for retrieval queries.
• Config: Default options (credentials not explicitly attached in JSON here; may use default/implicit credentials in your n8n instance).
• Connection: Provides ai_embedding to Supabase Vector Store.
• Failure modes: Missing credential assignment (common cause of runtime error), rate limits.

Node: Supabase Vector Store

• Type / role: vectorStoreSupabase — configured as a LangChain “tool” for retrieval.
• Mode: retrieve-as-tool
• Table: linkedin_post
• topK: 5
• Tool description: “You are helpful assistant”
• Connection: Exposed as ai_tool to Post Generator Agent.
• Failure modes / edge cases:
  • Table not populated yet → low-quality retrieval.
  • Embedding dimension mismatch between ingestion and retrieval.
  • RLS policies block select operations.

Node: Post Generator Agent

• Type / role: agent — final ghostwriter using RAG.
• Prompt: Describes a 3-step DB analysis process and then generates a post using outline fields:
  • Hook: {{ $json.output.structure.hook }}
  • Problem: {{ $json.output.structure.value }} (likely incorrect mapping)
  • Value: {{ $json.output.structure.value }}
  • Solution: {{ $json.output.structure.solution }}
  • CTA: {{ $json.output.structure.cta }}
• Inputs:
  • Main: from Post Structure Agent
  • LLM: 4o mini primary, 2.5-flash fallback
  • Tool: Supabase Vector Store (RAG)
  • Output parser: Structured Output Parser1
• Failure modes: Tool call failures, low recall if embeddings mismatch, agent may ignore tool if not compelled strongly enough.

Node: Structured Output Parser1

• Type / role: structured parser for final post.
• Schema example: { "Post Content": "Post Content" }
• LLM dependency: 5 nano
• Edge cases: If you want rich output (hashtags, line breaks, etc.), ensure the parser schema allows it and the agent puts content under Post Content.

Node: Create a post

• Type / role: linkedIn — publishes generated content to LinkedIn.
• Config: Minimal (additionalFields empty). The workflow JSON does not show the exact text mapping; you typically map Post Content into the LinkedIn node’s text field.
• Failure modes: OAuth permissions, token expiry, missing “w_member_social”/posting permissions, API limitations for images.

---

3. Summary Table

Node Name	Node Type	Functional Role	Input Node(s)	Output Node(s)	Sticky Note
On Telegram Message	telegramTrigger	Entry point for Telegram scraper module	—	If; Typing....	# LinkedIn Post Generator with Viral Content Vector Database (full overview + setup notes incl. BotFather link https://t.me/BotFather)
Typing....	telegram	Sends “typing” chat action	On Telegram Message	—	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
If	if	Validates message contains LinkedIn URL	On Telegram Message	LinkedIn Post URL (true); Wrong URL (false)	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
Wrong URL	telegram	Notifies invalid URL	If (false)	—	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
LinkedIn Post URL	httpRequest	Fetch LinkedIn post HTML	If (true)	Scrap Content (success); Unable to Scrape (error)	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
Scrap Content	html	Extracts post commentary via CSS selector	LinkedIn Post URL	Upload Document	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
Unable to Scrape	telegram	Notifies scraping failure	LinkedIn Post URL (error output)	—	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
Upload Document	vectorStoreSupabase (LangChain)	Inserts scraped post into Supabase vector table	Scrap Content (+ Data Loader ai_document + Embeddings ai_embedding)	Code	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
Data Loader	documentDefaultDataLoader (LangChain)	Converts extracted text into document	(implicit flow into Upload Document via ai_document)	Upload Document (ai_document)	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
Embeddings	embeddingsOpenAi (LangChain)	Creates embeddings for insertion	(implicit flow into Upload Document via ai_embedding)	Upload Document (ai_embedding)	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
Code	code	Returns first output item from Upload Document	Upload Document	✅ Post Scrapped Sucessfully	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
✅ Post Scrapped Sucessfully	telegram	Confirms scrape+store success	Code	—	## 📌 Viral LinkedIn Post Vector Database - Transform any LinkedIn post URL into actionable insights...
LinkedIn Form	formTrigger	Entry point for generation module via web form	—	Hook Analyse Agent	## Generate LinkedIn Post using LinkedIn Post Vector Store
Hook Analyse Agent	agent (LangChain)	Extracts topic/niche/tone/key points from hook	LinkedIn Form	Post Structure Agent	## Generate LinkedIn Post using LinkedIn Post Vector Store
Structured Output Parser	outputParserStructured (LangChain)	Enforces JSON schema for hook analysis	5 nano (ai_languageModel)	Hook Analyse Agent (ai_outputParser)	## Generate LinkedIn Post using LinkedIn Post Vector Store
Post Structure Agent	agent (LangChain)	Builds 5-part post outline	Hook Analyse Agent	Post Generator Agent	## Generate LinkedIn Post using LinkedIn Post Vector Store
Structured Output Parser.	outputParserStructured (LangChain)	Enforces JSON schema for outline	5 nano (ai_languageModel)	Post Structure Agent (ai_outputParser)	## Generate LinkedIn Post using LinkedIn Post Vector Store
Post Generator Agent	agent (LangChain)	RAG-based final post writing	Post Structure Agent (+ Supabase Vector Store tool)	Create a post	## Generate LinkedIn Post using LinkedIn Post Vector Store
Structured Output Parser1	outputParserStructured (LangChain)	Enforces JSON schema for final post content	5 nano (ai_languageModel)	Post Generator Agent (ai_outputParser)	## Generate LinkedIn Post using LinkedIn Post Vector Store
4o mini	lmChatOpenAi (LangChain)	Primary LLM for agents	—	Hook Analyse Agent; Post Structure Agent; Post Generator Agent	## Generate LinkedIn Post using LinkedIn Post Vector Store
2.5-flash	lmChatGoogleGemini (LangChain)	Fallback LLM for agents	—	Hook Analyse Agent; Post Structure Agent; Post Generator Agent	## Generate LinkedIn Post using LinkedIn Post Vector Store
5 nano	lmChatOpenAi (LangChain)	LLM used by parsers for schema auto-fix	—	Structured Output Parser; Structured Output Parser.; Structured Output Parser1	## Generate LinkedIn Post using LinkedIn Post Vector Store
Supabase Vector Store	vectorStoreSupabase (LangChain)	Retrieval tool (topK=5) for RAG	Embeddings. (ai_embedding)	Post Generator Agent (ai_tool)	## Generate LinkedIn Post using LinkedIn Post Vector Store
Embeddings.	embeddingsOpenAi (LangChain)	Creates embeddings for retrieval queries	—	Supabase Vector Store (ai_embedding)	## Generate LinkedIn Post using LinkedIn Post Vector Store
Create a post	linkedIn	Publishes generated post to LinkedIn	Post Generator Agent	—	## Generate LinkedIn Post using LinkedIn Post Vector Store

---

4. Reproducing the Workflow from Scratch

1. Create a new workflow in n8n with two separate entry points: Telegram Trigger and Form Trigger.

Module 1 — Telegram scraper → Supabase vector insert

2. Add Telegram Trigger node named On Telegram Message

   • Updates: message
   • Credentials: Telegram bot token (create via @BotFather).

3. Add Telegram node named Typing....

   • Operation: Send Chat Action
   • Chat ID: {{ $json.message.chat.id }}
   • Connect: On Telegram Message → Typing....

4. Add If node named If

   • Condition (Boolean true):
     {{ $json.message.text && $json.message.text.includes("linkedin.com") }}
   • Connect: On Telegram Message → If

5. Add Telegram node named Wrong URL

   • Send message text: ⚠️ Please provide a valid LinkedIn post URL
   • Chat ID: {{ $('On Telegram Message').item.json.message.chat.id }}
   • Connect: If (false) → Wrong URL

6. Add HTTP Request node named LinkedIn Post URL

   • URL: {{ $('On Telegram Message').item.json.message.text }}
   • Set Error Handling to: “Continue on Fail” / onError=continueErrorOutput
   • Connect: If (true) → LinkedIn Post URL

7. Add HTML node named Scrap Content

   • Operation: Extract HTML Content
   • CSS selector: [data-test-id="main-feed-activity-card__commentary"]
   • Key name: Post Content
   • Connect: LinkedIn Post URL (success) → Scrap Content

8. Add Telegram node named Unable to Scrape

   • Text: 😶 Scraping failed for this LinkedIn post
   • Chat ID: {{ $('On Telegram Message').item.json.message.chat.id }}
   • Connect: LinkedIn Post URL (error output) → Unable to Scrape

9. Add Document Default Data Loader (LangChain) node named Data Loader

   • Leave defaults unless you want to map specific fields into the document content/metadata.

10. Add OpenAI Embeddings (LangChain) node named Embeddings

• Credentials: OpenAI API key
• Keep default model/options (or select your preferred embedding model).

11. Add Supabase Vector Store (LangChain) node named Upload Document

• Mode: Insert
• Table name: linkedin_post
• Credentials: Supabase (URL + service role key recommended for server-side inserts)
• Connect:
• Scrap Content → Upload Document (main)
• Data Loader → Upload Document (ai_document)
• Embeddings → Upload Document (ai_embedding)

12. Add Code node named Code

• JS: return $('Upload Document').all()[0]
• Connect: Upload Document → Code

13. Add Telegram node named ✅ Post Scrapped Sucessfully

• Text: ✅ Post Scraped successfully
• Chat ID: {{ $('On Telegram Message').item.json.message.chat.id }}
• Connect: Code → ✅ Post Scrapped Sucessfully

Module 2 — Form → Agents → RAG → LinkedIn publish

14. Add Form Trigger node named LinkedIn Form

• Path: linkedin-post
• Title: “LinkedIn - Bhavy Shekhaliya”
• Field 1: Hook (Textarea, required)
• Field 2: Post Image (optional)
• Connect: LinkedIn Form → Hook Analyse Agent

15. Add LLM nodes:

• OpenAI Chat Model named 4o mini: select model gpt-4o-mini, set OpenAI credentials.
• Google Gemini Chat Model named 2.5-flash: configure Google/Gemini credentials; use as fallback.
• OpenAI Chat Model named 5 nano: select model gpt-5-nano (requires access), used by parsers.

16. Add Structured Output Parser node (LangChain) named Structured Output Parser

• Auto-fix: enabled
• Provide JSON schema example with topic, niche, tone, key_points[]
• Connect: 5 nano → Structured Output Parser (ai_languageModel)

17. Add Agent node named Hook Analyse Agent

• Prompt: hook analysis (topic/niche/tone/key points) using {{ $json.Hook }}
• Enable: hasOutputParser, needsFallback
• Connect:
  • LinkedIn Form → Hook Analyse Agent (main)
  • 4o mini → Hook Analyse Agent (ai_languageModel index 0)
  • 2.5-flash → Hook Analyse Agent (ai_languageModel index 1)
  • Structured Output Parser → Hook Analyse Agent (ai_outputParser)

18. Add Structured Output Parser node named Structured Output Parser. for the outline schema

• Auto-fix enabled; schema example with structure.hook/problem/value/solution/cta
• Connect: 5 nano → Structured Output Parser. (ai_languageModel)

19. Add Agent node named Post Structure Agent

• Prompt: produce 5-section outline using {{ $json.output.* }} from Hook agent
• Connect: Hook Analyse Agent → Post Structure Agent (main)
• LLM connections: 4o mini (primary) + 2.5-flash (fallback)
• Parser connection: Structured Output Parser. → agent.

20. Add OpenAI Embeddings node named Embeddings. (for retrieval)

• Set OpenAI credentials (explicitly assign to avoid runtime credential errors).

21. Add Supabase Vector Store node named Supabase Vector Store

• Mode: Retrieve as tool
• Table: linkedin_post
• topK: 5
• Connect: Embeddings. → Supabase Vector Store (ai_embedding)

22. Add Structured Output Parser node named Structured Output Parser1 for final output

• Schema example: { "Post Content": "..." }
• Connect: 5 nano → Structured Output Parser1 (ai_languageModel)

23. Add Agent node named Post Generator Agent

• Prompt: instruct to query vector DB for similar posts + generate final post using outline fields
• Connect:
  • Post Structure Agent → Post Generator Agent (main)
  • 4o mini (primary LLM) + 2.5-flash (fallback)
  • Supabase Vector Store → Post Generator Agent (ai_tool)
  • Structured Output Parser1 → Post Generator Agent (ai_outputParser)

24. Add LinkedIn node named Create a post

• Configure LinkedIn OAuth2 credentials (LinkedIn app with posting permissions).
• Map the generated content (typically {{ $json.output["Post Content"] }} or whatever field your agent emits) into the LinkedIn post text field.
• Connect: Post Generator Agent → Create a post

Recommended fix while reproducing: In the Post Generator Agent prompt, map:

• Problem: {{ $json.output.structure.problem }} (not .value), unless intentional.

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5. General Notes & Resources

Note Content	Context or Link
Create Telegram bot via BotFather and use the token in Telegram credentials	https://t.me/BotFather
Supabase setup: enable vector extension and create linkedin_post with correct vector dimensions	Mentioned in the workflow notes (Supabase vector DB requirement)
Workflow concept: two modules (Telegram scraper + web form generation with RAG)	Sticky note “LinkedIn Post Generator with Viral Content Vector Database”
LinkedIn posting is optional; you can remove the LinkedIn node to only return formatted text	Mentioned in sticky note “Stage 4: Publication”
Scraping relies on a LinkedIn DOM selector that may break if LinkedIn changes markup	CSS selector [data-test-id="main-feed-activity-card__commentary"]

Disclaimer: Le texte fourni provient exclusivement d’un workflow automatisé réalisé avec n8n, un outil d’intégration et d’automatisation. Ce traitement respecte strictement les politiques de contenu en vigueur et ne contient aucun élément illégal, offensant ou protégé. Toutes les données manipulées sont légales et publiques.