What is an 'ATH' in this context?

ATH stands for All-Time High, which occurs when the current search volume reaches or exceeds 98% of the highest historical value recorded since 2004.

Do I need to be a programmer to use this?

The skill is designed for use within Claude Code, providing a guided workflow that handles the complex Python and Selenium execution for you.

How does it avoid Google's bot detection?

It uses Selenium to simulate human behavior, including removing 'navigator.webdriver' flags, rotating User-Agents, and introducing random delays between interactions.

Can it distinguish between a holiday spike and a news event?

Yes, the skill uses 'Signal Typing' to categorize spikes as 'Seasonal' (repeating cycles) or 'Event-driven' (sudden shocks with high Z-scores).

What outputs does the skill provide?

It generates structured JSON results including ATH status, anomaly scores, signal types, and related search queries to explain the trend drivers.

Google Trends ATH Detector

Name: Google Trends ATH Detector
Author: fatfingererr

byfatfingererr

Web Scraping & Data Collection

Detects and analyzes Google Trends search spikes and all-time highs using automated browser simulation and statistical modeling.

About

The Google Trends ATH Detector is a sophisticated tool designed to automate the collection and analysis of search interest data. By leveraging Selenium with advanced anti-detection techniques like User-Agent rotation and randomized delays, it bypasses bot mitigations to retrieve accurate historical data. The skill applies mathematical models, including Z-score analysis, to identify All-Time Highs (ATH) and anomalies. It further categorizes these signals into seasonal cycles, event-driven shocks, or structural regime shifts, allowing users to distinguish between temporary noise and significant market transitions with objective data.

Key Features

Statistical anomaly scoring using Z-score methodology to identify outliers
Advanced signal classification for seasonal, event-driven, or structural shifts
Support for multi-topic comparison and related query extraction for root-cause analysis
Automated All-Time High (ATH) detection based on historical data thresholds
Human-like browser simulation using Selenium to bypass scraping protections
0 GitHub stars

Use Cases

Identifying emerging market trends and 'black swan' events through search volume spikes
Differentiating recurring seasonal demand from genuine structural changes in consumer behavior
Monitoring brand health and competitive search share relative to historical peaks

About

Key Features

Statistical anomaly scoring using Z-score methodology to identify outliers
Advanced signal classification for seasonal, event-driven, or structural shifts
Support for multi-topic comparison and related query extraction for root-cause analysis
Automated All-Time High (ATH) detection based on historical data thresholds
Human-like browser simulation using Selenium to bypass scraping protections
0 GitHub stars

Use Cases

Identifying emerging market trends and 'black swan' events through search volume spikes
Differentiating recurring seasonal demand from genuine structural changes in consumer behavior
Monitoring brand health and competitive search share relative to historical peaks