Skool Event Scraper

Skool Event Scraper extracts and organizes upcoming calendar events from Skool communities into clean, structured datasets. It helps community managers, organizers, and members stay informed, plan ahead, and reuse event data across workflows with ease.

     

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Introduction

Skool Event Scraper collects upcoming events from selected Skool communities and converts them into structured, reusable data. It eliminates manual tracking of community calendars and centralizes event information in one place. This project is designed for community managers, event organizers, and active members who need reliable visibility into future events.

Centralized Community Event Tracking

• Aggregates events from multiple Skool communities in a single run
• Normalizes dates, times, locations, and meeting links
• Supports forward-looking event planning across custom time ranges
• Produces datasets ready for analysis, sharing, or publishing

Features

Feature	Description
Multi-community support	Extract events from multiple Skool communities in one execution.
Time-range control	Scrape events from 1 to 12 months ahead based on planning needs.
Smart filtering	Filter events by keywords or restrict results to online-only events.
Rich event metadata	Captures titles, descriptions, dates, locations, and URLs.
Structured exports	Outputs data in clean, analysis-ready formats.
Secure execution	Handles credentials safely and retries automatically on failures.

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What Data This Scraper Extracts

Field Name	Field Description
eventId	Unique identifier for each event.
title	Name of the community event.
description	Full event description and details.
startTime	Event start date and time.
endTime	Event end date and time.
timezone	Detected timezone of the event.
location	Physical location or online indicator.
meetingUrl	Online meeting link if applicable.
groupUrl	URL of the Skool community hosting the event.

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Example Output

[
      {
            "eventId": "evt_10293",
            "title": "Weekly Growth Workshop",
            "description": "Live session covering community growth strategies.",
            "startTime": "2025-01-15T18:00:00",
            "endTime": "2025-01-15T19:30:00",
            "timezone": "UTC",
            "location": "Online",
            "meetingUrl": "https://zoom.us/j/123456789",
            "groupUrl": "https://www.skool.com/your-community"
      }
]

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Directory Structure Tree

skool-event-scraper/
├── src/
│   ├── main.py
│   ├── auth/
│   │   └── login_handler.py
│   ├── extractors/
│   │   ├── events_parser.py
│   │   └── filters.py
│   ├── exporters/
│   │   ├── json_exporter.py
│   │   └── csv_exporter.py
│   └── utils/
│       └── datetime_utils.py
├── data/
│   ├── sample_input.json
│   └── sample_output.json
├── requirements.txt
└── README.md

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Use Cases

• Community managers use it to track all upcoming events, so they can plan promotions and announcements efficiently.
• Event organizers use it to coordinate schedules across communities, so they can avoid conflicts and overlaps.
• Community members use it to export events, so they never miss important sessions.
• Marketing teams use it to build newsletters and calendars, so events stay visible and attended.

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FAQs

Do I need access to the community to extract events? Yes, the account used must be a member of the target community to access its events.

Can I limit results to specific types of events? Yes, keyword-based filtering allows you to extract only relevant events such as workshops or webinars.

How far ahead can events be collected? The scraper supports configurable ranges from 1 up to 12 months into the future.

What happens if no events are found? The output will return an empty dataset, indicating no upcoming events within the selected range.

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Performance Benchmarks and Results

Primary Metric: Average extraction time of 30–60 seconds per community.

Reliability Metric: Over 99% successful runs with automatic retry handling.

Efficiency Metric: Processes multiple communities in a single execution with minimal overhead.

Quality Metric: High data completeness with consistent field coverage across events.

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