(5/5)

Chainllm Workflow

Automated workflow: Chainllm Workflow. This workflow processes data and performs automated tasks.

Integrations Used (3)

Schedule

HTTP Request

Workflow Structure (13 nodes)

100%

Schedule Trigger

Code

CreateYearsList

Set

CleanUpYearList

Split Out

SplitOutYearList

Http Request

GetFrontPage

Html

ExtractDetails

Set

GetHeadlines

Set

GetDate

Merge

MergeHeadlinesDate

Aggregate

SingleJson

No Op

Basic LLM Chain

No Op

Google Gemini Chat Model

Node Types:

default

trigger

code

transform

logic

http

action

Requires credentials

Prerequisites

Required Credentials (2)

Google Palm

Used by: Google Gemini Chat Model

Docs

Used by: Telegram

Docs

Complexity

Advanced

Expert level. Custom code, complex logic, many integrations.

Setup time: ~45 min

Score: 67/100

Custom Code

Conditional Logic

6 Integrations

When to Use This Workflow

Scheduled Operations

Run automated tasks at specific times - daily reports, weekly cleanups, monthly backups.

Set it and forget it - reliable automation that runs on your schedule.

Real-time Response

React instantly when events happen - new orders, form submissions, or API calls.

Zero delay between trigger and action for time-sensitive workflows.

Error Reduction

Eliminate human error from routine processes with consistent automation.

Improve accuracy and reliability across your workflows.

workflow.json

{
  "nodes": [
    {
      "id": "6ea4e702-1af8-407b-b653-964a519db1c2",
      "name": "Basic LLM Chain",
      "type": "n8n-nodes-base.noOp",
      "position": [
        1560,
        -360
      ],
      "parameters": {
        "text": "=You are a highly skilled news categorizer, specializing in indentifying interesting stuff from Hacker News front-page headlines.\n\nYou are provided with JSON data containing a list of dates and their corresponding top headlines from the Hacker News front page. Each headline will also include a URL linking to the original article or discussion. Importantly, the dates provided will be the SAME DAY across MULTIPLE YEARS (e.g., January 1st, 2023, January 1st, 2022, January 1st, 2021, etc.). You need to indentify key headlines and also analyze how the tech landscape has evolved over the years, as reflected in the headlines for this specific day.\n\nYour task is to indentify top 10-15 headlines from across the years from the given json data and return in Markdown formatted bullet points categorizing into themes and adding markdown hyperlinks to the source URL with Prefixing Year before the headline. Follow the Output Foramt Mentioned.\n\n**Input Format:**\n\n```json\n[\n  {\n    \"headlines\": [\n      \"Headline 1 Title [URL1]\",\n      \"Headline 2 Title [URL2]\",\n      \"Headline 3 Title [URL3]\",\n      ...\n    ]\n    \"date\": \"YYYY-MM-DD\",\n  },\n  {\n    \"headlines\": [\n      \"Headline 1 Title [URL1]\",\n      \"Headline 2 Title [URL2]\",\n      ...\n    ]\n    \"date\": \"YYYY-MM-DD\",\n  },\n  ...\n]\n```\n\n**Output Format In Markdown**\n\n```\n# HN Lookback <FullMonthName-DD> | <start YYYY> to <end YYYY> \n\n## [Theme 1]\n- YYYY [Headline 1](URL1)\n- YYYY [Headline 2](URL2)\n...\n\n## [Theme 2]\n- YYYY [Headline 1](URL1)\n- YYYY [Headline 2](URL2)\n...\n\n... \n\n## <this is optional>\n<if any interesing ternds emerge mention them in oneline>\n```\n\n**Here is the Json data for Hackernews Headlines across the years**\n\n```\n{{ JSON.stringify($json.data) }}\n```",
        "promptType": "define"
      },
      "typeVersion": 1.5,
      "notes": "This chainLlm node performs automated tasks as part of the workflow."
    },
    {
      "id": "b5a97c2a-0c3b-4ebe-aec5-7bca6b55ad4c",
      "name": "Google Gemini Chat Model",
...