HumationHuman + Automation
Back to Catalog

Weekly job discovery and CV matching with Gemini 1.5 Pro and Decodo Scraper

Abdullah AlshiekhAbdullah Alshiekh
111 views
2/3/2026
Lead ManagementPipedriveAutomationZoho FormsCRM
Official Page

Header 1Smart Weekly Job Discovery Powered by Decodo

This workflow automates the entire search process: every week, it uses Decodo’s reliable scraping engine to scan the web for fresh opportunities in your region and industry — no manual searching, no endless scrolling.

Decodo handles the heavy lifting behind the scenes: it gathers search results, opens each job link, and extracts clean, readable text from pages that are normally full of scripts and formatting noise. The workflow always receives structured, usable information ready for AI analysis.

Intelligent Matching — Not Just Scraping

Once the jobs are collected, the system analyzes the candidate’s CV and compares it to each posting. It evaluates:

  • Skill alignment
  • Experience relevance
  • Domain match
  • Seniority level

Then it generates a Match Percentage for each role, filtering out weak options and keeping only meaningful opportunities.

A Weekly Report That Feels Human

Every week, the workflow sends a polished report straight to your inbox:

  • A quick overview of the candidate’s strengths
  • Best-fit roles sorted by match score
  • Clear reasons why each job fits
  • Posted dates and direct links
  • Insights on skills and market trends

It reads like a personalized career briefing — generated automatically.

How to Configure It

Decodo Setup

Add your Decodo API credentials to n8n. The Google Search + Scraper nodes rely on Decodo’s Web Scraping API. Make sure your plan supports scraping LinkedIn/Indeed pages.

AI Setup

Add your Google Gemini API key. The workflow uses two Gemini models: one for summarizing, one for job-matching. You can switch to OpenAI or Claude if you prefer.

CV Input

Add your CV text into the workflow (or connect Google Drive/Sheets for auto-loading). The Job Matcher Agent will use this text to compute match percentages.

Email Setup

Add your Gmail credentials and choose where the final report should be sent.

Flexible and Easy to Customize

  • Change the search region.
  • Target different industries.
  • Store all job data in Notion or Google Sheets.

With Decodo’s scraping pipeline at the core, the whole process stays consistent, fast, and dependable.

If you need any help Get in Touch

Weekly Job Discovery and CV Matching with Gemini 1.5 Pro and Decodo Scraper

This n8n workflow automates the process of discovering new job postings, extracting key information, and matching them against a predefined CV, then sending a summary via email. It leverages AI capabilities from Google Gemini and a hypothetical web scraping service (Decodo Scraper, inferred from the directory name and common use cases for AI agents in job discovery, though not explicitly present in the provided JSON nodes).

Description

This workflow streamlines the job search and application preparation process by automatically identifying relevant job opportunities, analyzing their content, and comparing them to a user's CV using a powerful AI model. It then compiles a concise summary of the findings and delivers it directly to your inbox.

What it does

  1. Triggers Weekly: The workflow is scheduled to run periodically (e.g., weekly) to check for new job postings.
  2. Initial Setup (Edit Fields): Sets up initial data or parameters for the subsequent steps.
  3. Loop Over Items: Processes items in batches, likely iterating through a list of job search queries or scraped job postings.
  4. AI Agent: Utilizes an AI Agent (likely for complex decision-making, data extraction, or interaction with external tools like a web scraper for job postings).
  5. Google Gemini Chat Model: Employs the Google Gemini Chat Model for advanced natural language processing tasks, such as understanding job descriptions, extracting requirements, and comparing them to a CV.
  6. Summarization Chain: Generates a concise summary of the job postings and their relevance to the CV using a Langchain summarization chain.
  7. Code (Custom Logic): Executes custom JavaScript code, potentially for formatting data, applying specific filtering logic, or preparing the email content.
  8. Gmail: Sends an email containing the summarized job findings and CV matching results.
  9. Sticky Note: Provides a textual note within the workflow for documentation or reminders.

Prerequisites/Requirements

  • n8n Instance: A running n8n instance to host the workflow.
  • Google Account: For Gmail integration and potentially Google Gemini API access.
  • Google Gemini API Key: To utilize the Google Gemini Chat Model for AI tasks.
  • Langchain Credentials: If the AI Agent or Summarization Chain requires specific Langchain-related credentials.
  • Decodo Scraper Account/API Key (Inferred): Although not explicitly in the JSON, the directory name suggests a web scraping service like Decodo Scraper would be used by the AI Agent to fetch job postings.

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your Gmail credential to allow the workflow to send emails.
    • Configure your Google Gemini Chat Model credential with your API key.
    • Ensure any other necessary Langchain-related credentials are set up.
  3. Customize "Edit Fields": Adjust the initial data in the "Edit Fields" node to define your job search criteria, target roles, or the CV content to be matched.
  4. Review "AI Agent" and "Code" Nodes: Examine the configuration of the "AI Agent" and "Code" nodes to understand and potentially customize their logic for job scraping, extraction, and matching.
  5. Set "Schedule Trigger": Configure the "Schedule Trigger" node to run at your desired frequency (e.g., once a week).
  6. Activate the Workflow: Once configured, activate the workflow to start automating your job discovery and CV matching process.

Related Templates

Track competitor SEO keywords with Decodo + GPT-4.1-mini + Google Sheets

This workflow automates competitor keyword research using OpenAI LLM and Decodo for intelligent web scraping. Who this is for SEO specialists, content strategists, and growth marketers who want to automate keyword research and competitive intelligence. Marketing analysts managing multiple clients or websites who need consistent SEO tracking without manual data pulls. Agencies or automation engineers using Google Sheets as an SEO data dashboard for keyword monitoring and reporting. What problem this workflow solves Tracking competitor keywords manually is slow and inconsistent. Most SEO tools provide limited API access or lack contextual keyword analysis. This workflow solves that by: Automatically scraping any competitor’s webpage with Decodo. Using OpenAI GPT-4.1-mini to interpret keyword intent, density, and semantic focus. Storing structured keyword insights directly in Google Sheets for ongoing tracking and trend analysis. What this workflow does Trigger — Manually start the workflow or schedule it to run periodically. Input Setup — Define the website URL and target country (e.g., https://dev.to, france). Data Scraping (Decodo) — Fetch competitor web content and metadata. Keyword Analysis (OpenAI GPT-4.1-mini) Extract primary and secondary keywords. Identify focus topics and semantic entities. Generate a keyword density summary and SEO strength score. Recommend optimization and internal linking opportunities. Data Structuring — Clean and convert GPT output into JSON format. Data Storage (Google Sheets) — Append structured keyword data to a Google Sheet for long-term tracking. Setup Prerequisites If you are new to Decode, please signup on this link visit.decodo.com n8n account with workflow editor access Decodo API credentials OpenAI API key Google Sheets account connected via OAuth2 Make sure to install the Decodo Community node. Create a Google Sheet Add columns for: primarykeywords, seostrengthscore, keyworddensity_summary, etc. Share with your n8n Google account. Connect Credentials Add credentials for: Decodo API credentials - You need to register, login and obtain the Basic Authentication Token via Decodo Dashboard OpenAI API (for GPT-4o-mini) Google Sheets OAuth2 Configure Input Fields Edit the “Set Input Fields” node to set your target site and region. Run the Workflow Click Execute Workflow in n8n. View structured results in your connected Google Sheet. How to customize this workflow Track Multiple Competitors → Use a Google Sheet or CSV list of URLs; loop through them using the Split In Batches node. Add Language Detection → Add a Gemini or GPT node before keyword analysis to detect content language and adjust prompts. Enhance the SEO Report → Expand the GPT prompt to include backlink insights, metadata optimization, or readability checks. Integrate Visualization → Connect your Google Sheet to Looker Studio for SEO performance dashboards. Schedule Auto-Runs → Use the Cron Node to run weekly or monthly for competitor keyword refreshes. Summary This workflow automates competitor keyword research using: Decodo for intelligent web scraping OpenAI GPT-4.1-mini for keyword and SEO analysis Google Sheets for live tracking and reporting It’s a complete AI-powered SEO intelligence pipeline ideal for teams that want actionable insights on keyword gaps, optimization opportunities, and content focus trends, without relying on expensive SEO SaaS tools.

Ranjan DailataBy Ranjan Dailata
161

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Automate invoice processing with OCR, GPT-4 & Salesforce opportunity creation

PDF Invoice Extractor (AI) End-to-end pipeline: Watch Drive ➜ Download PDF ➜ OCR text ➜ AI normalize to JSON ➜ Upsert Buyer (Account) ➜ Create Opportunity ➜ Map Products ➜ Create OLI via Composite API ➜ Archive to OneDrive. --- Node by node (what it does & key setup) 1) Google Drive Trigger Purpose: Fire when a new file appears in a specific Google Drive folder. Key settings: Event: fileCreated Folder ID: google drive folder id Polling: everyMinute Creds: googleDriveOAuth2Api Output: Metadata { id, name, ... } for the new file. --- 2) Download File From Google Purpose: Get the file binary for processing and archiving. Key settings: Operation: download File ID: ={{ $json.id }} Creds: googleDriveOAuth2Api Output: Binary (default key: data) and original metadata. --- 3) Extract from File Purpose: Extract text from PDF (OCR as needed) for AI parsing. Key settings: Operation: pdf OCR: enable for scanned PDFs (in options) Output: JSON with OCR text at {{ $json.text }}. --- 4) Message a model (AI JSON Extractor) Purpose: Convert OCR text into strict normalized JSON array (invoice schema). Key settings: Node: @n8n/n8n-nodes-langchain.openAi Model: gpt-4.1 (or gpt-4.1-mini) Message role: system (the strict prompt; references {{ $json.text }}) jsonOutput: true Creds: openAiApi Output (per item): $.message.content → the parsed JSON (ensure it’s an array). --- 5) Create or update an account (Salesforce) Purpose: Upsert Buyer as Account using an external ID. Key settings: Resource: account Operation: upsert External Id Field: taxid_c External Id Value: ={{ $json.message.content.buyer.tax_id }} Name: ={{ $json.message.content.buyer.name }} Creds: salesforceOAuth2Api Output: Account record (captures Id) for downstream Opportunity. --- 6) Create an opportunity (Salesforce) Purpose: Create Opportunity linked to the Buyer (Account). Key settings: Resource: opportunity Name: ={{ $('Message a model').item.json.message.content.invoice.code }} Close Date: ={{ $('Message a model').item.json.message.content.invoice.issue_date }} Stage: Closed Won Amount: ={{ $('Message a model').item.json.message.content.summary.grand_total }} AccountId: ={{ $json.id }} (from Upsert Account output) Creds: salesforceOAuth2Api Output: Opportunity Id for OLI creation. --- 7) Build SOQL (Code / JS) Purpose: Collect unique product codes from AI JSON and build a SOQL query for PricebookEntry by Pricebook2Id. Key settings: pricebook2Id (hardcoded in script): e.g., 01sxxxxxxxxxxxxxxx Source lines: $('Message a model').first().json.message.content.products Output: { soql, codes } --- 8) Query PricebookEntries (Salesforce) Purpose: Fetch PricebookEntry.Id for each Product2.ProductCode. Key settings: Resource: search Query: ={{ $json.soql }} Creds: salesforceOAuth2Api Output: Items with Id, Product2.ProductCode (used for mapping). --- 9) Code in JavaScript (Build OLI payloads) Purpose: Join lines with PBE results and Opportunity Id ➜ build OpportunityLineItem payloads. Inputs: OpportunityId: ={{ $('Create an opportunity').first().json.id }} Lines: ={{ $('Message a model').first().json.message.content.products }} PBE rows: from previous node items Output: { body: { allOrNone:false, records:[{ OpportunityLineItem... }] } } Notes: Converts discount_total ➜ per-unit if needed (currently commented for standard pricing). Throws on missing PBE mapping or empty lines. --- 10) Create Opportunity Line Items (HTTP Request) Purpose: Bulk create OLIs via Salesforce Composite API. Key settings: Method: POST URL: https://<your-instance>.my.salesforce.com/services/data/v65.0/composite/sobjects Auth: salesforceOAuth2Api (predefined credential) Body (JSON): ={{ $json.body }} Output: Composite API results (per-record statuses). --- 11) Update File to One Drive Purpose: Archive the original PDF in OneDrive. Key settings: Operation: upload File Name: ={{ $json.name }} Parent Folder ID: onedrive folder id Binary Data: true (from the Download node) Creds: microsoftOneDriveOAuth2Api Output: Uploaded file metadata. --- Data flow (wiring) Google Drive Trigger → Download File From Google Download File From Google → Extract from File → Update File to One Drive Extract from File → Message a model Message a model → Create or update an account Create or update an account → Create an opportunity Create an opportunity → Build SOQL Build SOQL → Query PricebookEntries Query PricebookEntries → Code in JavaScript Code in JavaScript → Create Opportunity Line Items --- Quick setup checklist 🔐 Credentials: Connect Google Drive, OneDrive, Salesforce, OpenAI. 📂 IDs: Drive Folder ID (watch) OneDrive Parent Folder ID (archive) Salesforce Pricebook2Id (in the JS SOQL builder) 🧠 AI Prompt: Use the strict system prompt; jsonOutput = true. 🧾 Field mappings: Buyer tax id/name → Account upsert fields Invoice code/date/amount → Opportunity fields Product name must equal your Product2.ProductCode in SF. ✅ Test: Drop a sample PDF → verify: AI returns array JSON only Account/Opportunity created OLI records created PDF archived to OneDrive --- Notes & best practices If PDFs are scans, enable OCR in Extract from File. If AI returns non-JSON, keep “Return only a JSON array” as the last line of the prompt and keep jsonOutput enabled. Consider adding validation on parsing.warnings to gate Salesforce writes. For discounts/taxes in OLI: Standard OLI fields don’t support per-line discount amounts directly; model them in UnitPrice or custom fields. Replace the Composite API URL with your org’s domain or use the Salesforce node’s Bulk Upsert for simplicity.

Le NguyenBy Le Nguyen
942