Detect athlete name homonyms in bodybuilding contest data using year-gap clustering.
npx skills add https://github.com/timfogarty1549/musmemSkills --skill musmem-homonym
Please help me install this Agent Skill. Skill: musmem-homonym Source: https://github.com/timfogarty1549/musmemSkills/tree/main/musmem-homonym Command: npx skills add https://github.com/timfogarty1549/musmemSkills --skill musmem-homonym
Scans bodybuilding contest data files to flag athlete name pairs that likely refer to two different individuals, using year-gap analysis to catch reappearing athletes across wide time spans and age-group transitions.
Copy the script to a temporary location and run it to begin interactive review.
High-intent search queries and answers about installing and using this skill.
This skill identifies athlete name homonyms in bodybuilding contest data by detecting year gaps of 10 or more years, evaluating multi-criteria confidence levels, and clustering contest years to flag reappearing names.
The best way is applying year-gap clustering to semicolon-delimited contest data files, which flags names with 10-plus year gaps and supports cross-file analyses to identify age-division transitions generating false duplicate matches.
Yes, the interactive workflow supports loading multiple .dat files to perform cross-file analyses of long-time gaps, allowing you to validate homonym candidates across different contest datasets simultaneously.
Yes, the interactive review workflow includes a non-homonym exclusion log feature that saves your validation decisions, allowing you to track reviewed candidates and filter them out of future analyses.
Year-gap clustering requires semicolon-delimited .dat files with consistent athlete names, years, and contest codes, and may miss homonyms with gaps under 10 years or fail to detect duplicates without open or masters division transitions.
timfogarty1549Extract athlete names from MusMem semicolon-delimited files into candidate-group TSVs.
timfogarty1549Detect duplicate placements across bodybuilding contest divisions and flag mislabeled results.
timfogarty1549Detect duplicate contests by comparing athlete results across six data files.
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