← ClaudeAtlas

apify-google-flights-trackinglisted

Build your own fare history for google flights tracking with the Apify Google Flights Deals API Actor (johnvc/google-flights-deals-api). Run it on a schedule against one or several home airports and every row arrives with the fare, what that route typically costs, the gap between them, exact dates, airline and a booking link, so successive runs accumulate into a price record you own and can query. This is the data layer under a price-alert product, not the alerting itself. The Actor takes a snapshot and keeps no history and sends no notifications, so the repeated observation is yours to store and the notify rule is yours to write, and a baseline in every row means that rule can be below typical instead of a threshold you guessed. Use when the user wants flight price tracking, airfare monitoring, fare data with a reference price, or the source feed behind flight price alerts. Pay per deal returned, MCP-ready for Claude and other AI agents.
johnisanerd/claude-skill-google-flights-tracking · ★ 0 · API & Backend · score 63
Install: claude install-skill johnisanerd/claude-skill-google-flights-tracking
# Google Flights Tracking: Your Own Fare History, With a Baseline in Every Row Schedule the deals feed, store the rows, and you own a fare record where every observation already knows what that route normally costs. ## When to use this skill - The user wants "flight price tracking" or airfare monitoring from their own data. - They are building a price-alert product and need a source feed to sit under it. - They want fare data where each row carries a reference price, not just a number. - They want to watch several home airports on one schedule. Not for: a one-shot look at today's bargains (use the companion apify-google-flights-deals skill) or pricing a specific route you have already picked (`johnvc/Google-Flights-Data-Scraper-Flight-and-Price-Search`). See `references/actor-index.md`. ## What this does and does not do An alert is two pieces: a repeated observation, and a rule about when to notify. This Actor is the observation half. It returns a snapshot each run, keeps no history, and sends nothing. You schedule it, you store the rows, and you write the rule. Any copy or agent response that implies this Actor notifies the user is wrong. The payoff for doing it this way is `average_price`. Because every observation arrives with what that route typically costs, your notify rule can be "below typical" rather than a fixed threshold you guessed at. ## What you get One dataset row per destination, per run. `result_type` separates `deal` rows from `error` rows: - `price