Score LinkedIn jobs against a resume and send a daily digest

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Job seekers spend hours manually sifting through LinkedIn postings that are wrong for them. This AI automation handles the entire pipeline, from scraping fresh listings to scoring them against a resume and landing a clean digest in the inbox each morning.

What This Automation Does

Scrapes LinkedIn daily using multiple job title keywords, then filters out any postings the client has already seen using a Google Sheets master log
Runs each new job through a Mistral LLM to score how well the client's resume matches the role, flagging gaps and ranking the top five daily finds
Generates a tailored cover letter and concrete resume improvement notes for each matched job automatically
Packages the top five matches into a single formatted digest email sent via Gmail each morning, with all data backed up to Google Sheets

Tools Used

n8n
Mistral AI
Google Sheets
Google Drive
Gmail

Where to Get Hired for This Skill

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How To Build It

Pull the client's resume from Google Drive

The workflow retrieves the client's resume file from a designated Google Drive folder and extracts the plain text content so the LLM can read and compare it against incoming job descriptions.

Generate alternate job titles with an AI agent

A Mistral-powered AI agent takes the client's primary job title and produces three semantically related alternate titles, expanding the LinkedIn search coverage beyond a single keyword.

Scrape LinkedIn and filter already-seen jobs

LinkedIn search results are pulled for each keyword, returning job URLs, titles, locations, and salary data; those results are then compared against the master Google Sheets log so only genuinely new postings continue through the pipeline.

Score, match, and generate cover letters with Mistral

Each unseen job description is sent to Mistral in structured batches, which returns a match score, a list of resume gaps, a tailored cover letter, and specific improvement suggestions, all parsed into structured fields ready for logging.

Log results to Google Sheets and send the daily digest

Every processed job and its AI-generated outputs are appended to the client's Google Sheets tracker, and the top five highest-scoring matches are compiled into a single HTML-formatted Gmail digest delivered on the daily schedule.

Pitfalls

LinkedIn aggressively rate-limits and blocks scraping traffic, so request frequency and IP rotation need to be managed carefully or the data pull will silently return empty results without throwing an error
Google OAuth tokens for both Sheets and Drive expire or get revoked when clients change passwords or security settings, which breaks the resume retrieval and logging steps with no visible alert to the end user
Mistral's structured output parser expects a consistent JSON schema on every response, but longer or unusual job descriptions can cause the model to drift from the format, producing parse failures that stall the entire batch mid-run

FAQ

Can I build this without coding?

Yes. The workflow is built entirely in n8n using visual nodes and pre-built integrations, so no custom code is required. The most technical step is configuring the LinkedIn scraping source and connecting the Google OAuth credentials, both of which follow a guided setup process.

How long does it take to build?

Most builders complete a working version in four to six hours, including credential setup and testing with a real resume. Adding client-specific customisation like custom scoring criteria or extra keyword sets adds another one to two hours.

What can I charge for this service?

Pricing is yours to set based on your market and positioning. Because this delivers a recurring daily output rather than a one-time setup, it lends itself naturally to a monthly retainer structure rather than a single flat fee.

Which tools are required versus optional?

Mistral AI, Google Sheets, and Gmail are load-bearing and cannot be swapped out without rebuilding the matching and delivery logic. Google Drive is required only if the resume is stored there rather than pasted as text directly into the workflow. The LinkedIn scraping source is required for live data but can be replaced with a manual job URL list for testing.

This is original DigiNo analysis. The underlying automation pattern is a community workflow template – view the original on n8n.