Who this is for? This workflow automates the process of Wikipedia data extraction using the Bright Data Web Unlocker, parsing and cleaning the data, and then sending the results to a specified webhook URL for downstream processing, reporting, or integration. What problem is this workflow solving? Researchers who need structured information from Wikipedia pages regularly. Data Engineers building knowledge bases or enriching datasets with factual data. Digital Marketers or Content Writers automating fact-checking or content sourcing. Automation Enthusiasts who want to trigger external systems with rich context from Wikipedia. What this workflow does This workflow addresses the challenges of manually retrieving, structuring, and using data from Wikipedia at scale. Workflow Breakdown Trigger Type: Scheduled or Manual Purpose: Starts the workflow either on a fixed schedule (e.g., daily) or on-demand via a manual trigger or incoming webhook. Bright Data Wikipedia Scraping Tool Used: Bright Data Web Unlocker Action: Scrape the HTML content of one or multiple Wikipedia article URLs.
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Monitor Japanese business partners for registry changes with gBizINFO and Gemini
Quick Overview This workflow runs weekly to check Japanese business partners listed in Google Sheets against the gBizINFO corporate registry API, uses Google Gemini to assess the risk impact of detected changes, updates the stored partner snapshot, and escalates findings to Slack and Gmail based on materiality. How it works Runs every week on a schedule. Reads the partner list (including corporate numbers and last-known name/address) from Google Sheets. Queries the gBizINFO corporate registry API for each partner and posts a Slack message if the lookup fails. Compares the latest registry data to the stored snapshot to detect name, address, or closure/dissolution changes and continues only when changes are found. Sends the detected changes to Google Gemini to classify materiality (high/medium/low) with a short summary and recommended action, and posts a Slack message if the assessment fails. Updates the Google Sheets partner snapshot with the latest registry details so the change is recorded. Routes by materiality to notify Slack (medium/other), and for high materiality also posts a Slack alert and creates a Gmail draft requesting a credit review. Setup Create a Google Sheets spread
Match Japanese jGrants subsidies to your company with Gemini, Slack and Gmail
Quick Overview This workflow runs weekly to search the public jGrants API for open Japanese subsidies, scores each subsidy’s fit for your company using Google Gemini, logs results to Google Sheets, and notifies a Slack channel (and drafts a Gmail kickoff email) for strong matches. How it works Runs weekly on a schedule and loads your company profile details (industry, headcount, prefecture, investment plan, and search keyword). Calls the public jGrants subsidies API to retrieve currently accepting programs sorted by newest first, and posts to Slack if the search fails. Processes each returned subsidy individually and asks Google Gemini to produce a fit score, verdict (strong/possible/no), reasons, next steps, and a short summary. Normalizes the Gemini response into consistent fields, and posts to Slack if scoring fails for a subsidy. Upserts each scored subsidy into a Google Sheets tracker (keyed by subsidy ID) to avoid duplicates across weekly runs. Routes results by verdict: sends a detailed Slack alert and creates a Gmail draft for strong matches, posts a lightweight Slack message for possible matches, and flags unclear verdicts in Slack. Setup Add credentials for Google Gemini
Monitor overtime risk against Japanese labour caps with Gemini, Sheets, Slack & Gmail
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