TheAnonymous-stack/numi-scraper
Jun 2025 – Jul 2025
2 Months active
Languages Used
JSON
Technical Skills
Data CleaningData CurationData ScrapingData FormattingData Management
LucaM-Z
PROFILE
Lucam-z
Luca Madrizzikic worked on the TheAnonymous-stack/numi-scraper repository, focusing on enhancing the quality and formatting of educational question datasets over a two-month period. He delivered features that clarified and expanded solutions in JSON-based data files, improving both the accuracy and readability of scraped and master question sets. His technical approach emphasized data cleaning, curation, and schema alignment, with careful attention to display formatting such as LaTeX-like markup and structured choices. By iteratively updating and synchronizing multiple JSON files through disciplined version control, Luca ensured the data was reliable for downstream analytics, user-facing applications, and further data management processes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
2
Lines of code
3,633
Activity Months2
Your Network
9 people
Work History
July 2025
6 Commits • 1 Features
Jul 1, 2025July 2025 performance summary for TheAnonymous-stack/numi-scraper. Delivered Master Questions Data Set Improvements and Formatting, updating the master questions dataset with new/edited questions, metadata, and enhanced display formatting (choices, LaTeX-like markup, and clarified solution steps). The work included six commits between Jul 12 and Jul 16 updating gr5MasterQuestions(A.1-M.3).json, its original variants, and the merged file, reflecting iterative QA and schema consistency. No major bugs reported; focus was on data quality, formatting, and UX readiness. This directly supports reliable end-user display, downstream processing, and analytics. Technologies demonstrated include JSON data modeling/transformation, version control discipline, and attention to display-ready formatting.
6 Commits • 1 Features
Jul 1, 2025July 2025 performance summary for TheAnonymous-stack/numi-scraper. Delivered Master Questions Data Set Improvements and Formatting, updating the master questions dataset with new/edited questions, metadata, and enhanced display formatting (choices, LaTeX-like markup, and clarified solution steps). The work included six commits between Jul 12 and Jul 16 updating gr5MasterQuestions(A.1-M.3).json, its original variants, and the merged file, reflecting iterative QA and schema consistency. No major bugs reported; focus was on data quality, formatting, and UX readiness. This directly supports reliable end-user display, downstream processing, and analytics. Technologies demonstrated include JSON data modeling/transformation, version control discipline, and attention to display-ready formatting.
July 2025
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for TheAnonymous-stack/numi-scraper: Primary focus on data quality improvements for Gr5 Scraped Questions. Delivered the Gr5 Scraped Questions Data Quality Improvements feature by clarifying existing solutions and adding new solutions for the Numbers section in gr5ScrapedQuestions.json. This enhances the accuracy and comprehension of scraped questions used by the Numbers content, enabling more reliable analytics and reporting. No major bugs fixed this month; effort aimed at data quality and reliability. Overall impact: improved data reliability for end users and downstream processes, with measurable improvements in data clarity. Technologies/skills demonstrated: JSON data handling, data quality engineering, scraping data pipeline maintenance, and Git version control with explicit commit history.
June 2025
2 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for TheAnonymous-stack/numi-scraper: Primary focus on data quality improvements for Gr5 Scraped Questions. Delivered the Gr5 Scraped Questions Data Quality Improvements feature by clarifying existing solutions and adding new solutions for the Numbers section in gr5ScrapedQuestions.json. This enhances the accuracy and comprehension of scraped questions used by the Numbers content, enabling more reliable analytics and reporting. No major bugs fixed this month; effort aimed at data quality and reliability. Overall impact: improved data reliability for end users and downstream processes, with measurable improvements in data clarity. Technologies/skills demonstrated: JSON data handling, data quality engineering, scraping data pipeline maintenance, and Git version control with explicit commit history.
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JSON
Technical Skills
Data CleaningData CurationData FormattingData ManagementData Scraping
Repositories Contributed To
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