🔍 Glassdoor Job Finder: Bright Data Scraping + Keyword-Based Automation A comprehensive n8n automation that scrapes Glassdoor job listings using Bright Data's web scraping service based on user-defined keywords, location, and country parameters, then automatically stores the results in Google Sheets. 📋 Overview This workflow provides an automated job search solution that extracts job listings from Glassdoor using form-based inputs and stores organized results in Google Sheets. Perfect for recruiters, job seekers, market research, and competitive analysis. Workflow Description: Automates Glassdoor job searches using Bright Data's web scraping capabilities. Users submit keywords, location, and country via form trigger. The workflow scrapes job listings, extracts company details, ratings, and locations, then automatically stores organized results in Google Sheets for easy analysis and tracking.
Tags
Google SheetsWHTTP RequestRelated workflows
See all Google Sheets→WTrack brand visibility in ChatGPT results with OpenAI and Google Sheets
Quick overview This workflow runs weekly, reads buyer-style queries from Google Sheets, asks OpenAI Chat Completions (ChatGPT) each query three times, judges whether your brand is mentioned, and writes per-query visibility metrics and a 0–100 score back to a Google Sheets results tab. How it works Runs every week on a schedule trigger. Reads the query set (query, brand, and competitors) from a Google Sheets tab named queries. Duplicates each query into three sample runs to account for variation in OpenAI responses. Sends each sample query to OpenAI Chat Completions (gpt-4o-mini) to generate an answer at temperature 1. Sends the generated answer to a separate OpenAI Chat Completions judge call (gpt-4o-mini) at temperature 0 to extract structured JSON about brand mention, rank, sentiment, recommendation, and competitors mentioned. Aggregates the three judged samples per query to compute inclusion rate, average rank/sentiment, and a weighted 0–100 visibility score. Appends the per-query results (including the run date and metrics) to the results tab in Google Sheets. Setup Add an OpenAI credential for HTTP Header Auth using Authorization: Bearer . Add a Google Sheets credential with a
WEnrich LinkedIn leads with Unipile and Google Sheets
Quick overview Automatically search LinkedIn for prospects using Unipile's API, enrich each profile with full details, and sync leads into a Google Sheet CRM — runs daily on a schedule with rate limiting, deduplication, and error logging built in. How it works A daily Schedule Trigger fires at 08:00 and loads your search config — Unipile DSN, LinkedIn account ID, target keywords, and daily safety limits — into all downstream nodes. Unipile's Search API (POST /api/v1/linkedin/search) runs a keyword search across Classic, Sales Navigator, or Recruiter LinkedIn and auto-paginates via cursor up to a configurable page limit. Search results are split into individual profile items, de-duplicated by LinkedIn profile ID to prevent processing the same person twice across pages. A Limit node caps total profiles per run to protect your daily LinkedIn quota — Unipile recommends staying under 100 profile look-ups per account per day. Each profile is enriched with full details via GET /api/v1/users/{id}?linkedin_sections=*, processed in batches of 5 with a 3-second pause between calls to respect rate limits. A Set node maps the Unipile response to 12 clean CRM columns — name, headline, title, com
WScore draft content citability with GPT-4o-mini and Google Sheets
Quick overview This workflow manually scores draft content for AI citability using OpenAI GPT-4o-mini and logs detailed, evidence-backed results to Google Sheets, either from pasted draft text or by fetching and cleaning a provided page URL. How it works Starts when you run the workflow manually. The workflow reads drafts from the content tab of your Google Sheet (content_id, title, brand, target_query, draft_text). A Code node validates each row, computes word count, and builds the full judge payload. The complete anchored rubric lives in this node, so the entire scoring methodology is readable in one place. One HTTP call per draft sends the rubric and draft to gpt-4o-mini (temperature 0, JSON mode, retries with backoff). The rubric anchors describe what a 90, 70, 50, and 30 look like on each dimension, so the judge grades against fixed criteria. A second Code node parses the judge output, clamps each score to 0-100, computes the weighted composite (Answer Structure 40%, Entity Clarity 30%, Factual Density 30%), and assigns the verdict band: 80+ publish, 60-79 revise, below 60 rework. Results append to the scores tab with the run date, per-dimension scores, the top 3 fixes, the ju