Skip to main content

Filter AI slop from your LinkedIn feed (backend for StopSlopIn Chrome extension)

Workflow preview

Workflow preview
100%
Filter AI slop from your LinkedIn feed (backend for StopSlopIn Chrome extension) preview
Open on n8n.io

1. Workflow Overview

Purpose This workflow is the official backend for the StopSlopIn Chrome extension – it classifies LinkedIn posts as quality or slop using a strict LLM quality gate and learns from user votes over t...

Best for

  • Market Research automation workflows
  • AI RAG automation workflows
  • advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.webhook, n8n-nodes-base.respondtowebhook, n8n-nodes-base.splitout, n8n-nodes-base.aggregate, @n8n/n8n-nodes-langchain.outputparserstructured, @n8n/n8n-nodes-langchain.embeddingsopenai, @n8n/n8n-nodes-langchain.documentdefaultdataloader, n8n-nodes-base.splitinbatches

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Mario.

Original n8n.io source

1.1 Workflow description

Title
Filter AI slop from your LinkedIn feed (backend for StopSlopIn Chrome extension)
Workflow name
Filter AI slop from your LinkedIn feed (backend for StopSlopIn Chrome extension)

Purpose

This workflow is the official backend for the StopSlopIn Chrome extension – it classifies LinkedIn posts as quality or slop using a strict LLM quality gate and learns from user votes over time via a Qdrant vector store.

What this is for

This runs the webhook that powers the StopSlopIn Chrome extension on the Chrome Web Store. The extension sends LinkedIn posts here for analysis and user votes here for training – everything stays on your own n8n instance.

Setup

  • Add your OpenAI credentials to the chat model and embeddings nodes
  • Add your Qdrant credentials to both vector store nodes, pointing to a collection named stopslopin
  • Activate the workflow, copy the webhook URL, and paste it into the StopSlopIn Chrome extension settings
  • Follow the instructions on the yellow sticky notes for anything else

How it works

A single webhook exposes two actions, selected via a ?action= query parameter: analyze for classification, vote for training.

  1. A Switch node routes incoming requests based on the action parameter
  2. On analyze: each post is enriched with similar prior-rated posts pulled from Qdrant (RAG), batched together, and sent to the LLM with a strict quality-gate system prompt
  3. The LLM returns a structured JSON array of pass / fail results, which is sent back to the caller
  4. On vote: the post is embedded and stored in Qdrant along with the user's "good" or "slop" rating as metadata
  5. Every new vote becomes in-context reference material for future classifications, so the filter gradually adapts to personal taste

Customization

  • LLM – swap the OpenAI Chat Model for any LangChain-compatible chat model (Claude, Ollama, etc.)
  • Prompt – edit the system prompt inside the Basic LLM Chain node to match your own feed taste
  • Similarity threshold – change the value in the Filter node (default 0.7) to make RAG examples looser or stricter

Compatibility

  • n8n Version 2.17.0 or above
  • Cloud or Self-Hosted
  • Requires: OpenAI account, Qdrant instance

Note: post contents sent through this workflow are forwarded to the configured LLM and embeddings provider (OpenAI by default). Swap those nodes for a local or alternative provider if that is a concern.

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Webhook

Type / Role
n8n-nodes-base.webhook - webhook
Config choices
Version 2.1

Block 2 - Respond to Webhook

Type / Role
n8n-nodes-base.respondToWebhook - respondToWebhook
Config choices
Version 1.5

Block 3 - Split Out

Type / Role
n8n-nodes-base.splitOut - splitOut
Config choices
Version 1

Block 4 - Aggregate

Type / Role
n8n-nodes-base.aggregate - aggregate
Config choices
Version 1

Block 5 - Structured Output Parser

Type / Role
@n8n/n8n-nodes-langchain.outputParserStructured - outputParserStructured
Config choices
Version 1.3

Block 6 - Embeddings OpenAI

Type / Role
@n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices
Version 1.2

Block 7 - Default Data Loader

Type / Role
@n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices
Version 1.1

Block 8 - Loop Over Items

Type / Role
n8n-nodes-base.splitInBatches - splitInBatches
Config choices
Version 3

Block 9 - Aggregate1

Type / Role
n8n-nodes-base.aggregate - aggregate
Config choices
Version 1

Block 10 - Respond to Webhook1

Type / Role
n8n-nodes-base.respondToWebhook - respondToWebhook
Config choices
Version 1.5

Block 11 - OpenAI Chat Model

Type / Role
@n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices
Version 1.3

Block 12 - Merge

Type / Role
n8n-nodes-base.merge - merge
Config choices
Version 3.2

Block 13 - Skip empty items

Type / Role
n8n-nodes-base.filter - filter
Config choices
Version 2.3

Block 14 - Sticky Note

Type / Role
n8n-nodes-base.stickyNote - stickyNote
Config choices
Version 1

Block 15 - Switch by action

Type / Role
n8n-nodes-base.switch - switch
Config choices
Version 3.4

Block 16 - Retrieve similar posts

Type / Role
@n8n/n8n-nodes-langchain.vectorStoreQdrant - vectorStoreQdrant
Config choices
Version 1.3

Block 17 - Store post

Type / Role
@n8n/n8n-nodes-langchain.vectorStoreQdrant - vectorStoreQdrant
Config choices
Version 1.3

Block 18 - Filter by similarity score

Type / Role
n8n-nodes-base.filter - filter
Config choices
Version 2.3

Block 19 - If similar posts found

Type / Role
n8n-nodes-base.if - if
Config choices
Version 2.3

Block 20 - Prepare fields

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 21 - Prepare output

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 22 - Prepare output1

Type / Role
n8n-nodes-base.set - set
Config choices
Version 3.4

Block 23 - No Operation

Type / Role
n8n-nodes-base.noOp - noOp
Config choices
Version 1

Block 24 - Analyze posts

Type / Role
@n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices
Version 1.9

Showing the first 24 of 27 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow Filter AI slop from your LinkedIn feed (backend for StopSlopIn Chrome extension)
Complexity advanced
Nodes 27
Categories Market Research, AI RAG
Author Mario
Published 22 Apr 2026

4. Reproducing the Workflow from Scratch

  1. 1. Download the workflow JSON

    Use the JSON export at /data/workflows/15226/15226.json as the source template for this automation.

  2. 2. Import the template into n8n

    Open n8n, import the downloaded JSON, and review each node before activating the workflow.

  3. 3. Configure credentials and variables

    Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.

  4. 4. Test with sample data

    Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.

  5. 5. Activate and monitor

    Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Frequently asked questions

What does Filter AI slop from your LinkedIn feed (backend for StopSlopIn Chrome extension) do?

Purpose This workflow is the official backend for the StopSlopIn Chrome extension – it classifies LinkedIn posts as quality or slop using a strict LLM quality gate and learns from user votes over t...

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Market Research, AI RAG use case.