musmem-locations

Extract contest dates, venues, and locations into contest_locations.json from articles and scorecards.

Updated Mar 1, 2026

Search Tags:#json #contest #scraping #xml #merging #locations

Ready to run

npx skills add https://github.com/timfogarty1549/musmemSkills --skill musmem-locations

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: musmem-locations
Source: https://github.com/timfogarty1549/musmemSkills/tree/main/musmem-locations
Command: npx skills add https://github.com/timfogarty1549/musmemSkills --skill musmem-locations

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps researchers and historians quickly locate reliable dates, venues, and locations for bodybuilding contests by parsing both article narratives and scorecard data, then updating a centralized dataset.

Core Features & Use Cases

Scrapes historic and modern contest articles to extract contest name, year, date, venue, and location, then merges results into contest_locations.json without overwriting existing data.
Supports two data sources and workflows: article-based extraction from XML article files and scorecard-based extraction from scorecard listings, with conflict checks and deduplication.
Use Case: a researcher wants to fill gaps for the Mr. America - AAU entries across 1940s–1970s and build a complete contest-history dataset.

Quick Start

Run prescreen_articles.py to scan all articles and generate a manifest, then run merge_locations.py to append new contest data to contest_locations.json.

Frequently Asked Questions about musmem-locations

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract bodybuilding contest dates and venues from historical articles and scorecards?▼

To extract bodybuilding contest dates and venues, this Skill parses XML article notes and NPC scorecard listing pages, then normalizes the extracted contest names and locations using MuscleMemory conventions before appending them to a centralized JSON dataset.

Can I scrape contest locations from scorecards without overwriting my existing dataset?▼

Yes, you can scrape scorecard locations without data loss because the merge process is non-destructive, appending new contest dates and venues to contest_locations.json only after performing conflict checks and deduplication against existing entries.

What is the best way to build a dataset of contest locations for bodybuilding events from the 1940s to present?▼

The best way to build a contest location dataset spanning the 1940s to present is running prescreen_articles.py to scan articles and generate a manifest, then executing merge_locations.py to consolidate the extracted dates, venues, and locations into contest_locations.json.

Does this bodybuilding contest scraper support merging data from both XML article files and scorecard listings?▼

Yes, the scraper supports merging data from both XML article files and scorecard listings by utilizing two distinct workflows that extract contest metadata and run conflict checks before performing a non-destructive merge into the centralized JSON file.

How does the prescreen_articles.py script work for scraping bodybuilding contest history?▼

The prescreen_articles.py script works by scanning all provided historical articles to identify relevant bodybuilding contest data, generating a manifest of contest names, years, dates, venues, and locations that feed into the subsequent merge_locations.py consolidation step.