keiyoushi/extensions-source
Dec 2024 – Aug 2025
2 Months active
Languages Used
GradleKotlinJava
Technical Skills
API IntegrationAndroid DevelopmentBackend DevelopmentData ParsingExtension DevelopmentKotlin
hatozuki-programmer
PROFILE
Hatozuki-programmer
Hatozuki contributed to the keiyoushi/extensions-source repository by developing two new manga sources, KadoComi and Manga Koinu, delivering end-to-end integration from build configuration to parsing pipelines. Using Kotlin and Java, Hatozuki implemented robust DTO modeling, image descrambling, and metadata correction to improve data quality and reliability. They addressed a NullPointerException in GigaViewer chapter list parsing and introduced a generic, pagination-aware approach for handling multi-page chapter lists, enhancing compatibility with diverse sources. Their work established scalable patterns for future source integration and improved maintainability, demonstrating strong skills in backend development, API integration, and web scraping within Android extension development.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
2
Lines of code
539
Activity Months2
Your Network
57 people
Work History
August 2025
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly performance highlights: Delivered GigaViewer paginated chapter list support and parsing enhancements for the keiyoushi/extensions-source repository. Refactored chapter list parsing to support both single-page and paginated responses, simplified date handling, and updated chapter status label logic. This work improves compatibility with diverse sources, reduces parsing errors, and enhances data quality for downstream consumers.
1 Commits • 1 Features
Aug 1, 2025August 2025 monthly performance highlights: Delivered GigaViewer paginated chapter list support and parsing enhancements for the keiyoushi/extensions-source repository. Refactored chapter list parsing to support both single-page and paginated responses, simplified date handling, and updated chapter status label logic. This work improves compatibility with diverse sources, reduces parsing errors, and enhances data quality for downstream consumers.
August 2025
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 performance summary for keiyoushi/extensions-source: Expanded the manga catalog with two new sources—KadoComi extension and Manga Koinu source—delivering end-to-end integration, including build configurations, DTOs, parsing for manga details, chapters, and pages, image descrambling, and author/artist name corrections. Added base configuration and launcher assets to support the new sources and improve deployment readiness. Fixed reliability issues by resolving a NullPointerException in GigaViewer chapter list parsing and adopting a generic, pagination-aware approach to fetch chapter information for sites with paginated lists, accompanied by a base version increment. These changes broaden content coverage, improve metadata accuracy and chapter-list reliability, and establish a scalable pattern for adding future sources. Technologies demonstrated include DTO modeling, parsing pipelines, image descrambling, build/configuration management, and structured versioning.
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 performance summary for keiyoushi/extensions-source: Expanded the manga catalog with two new sources—KadoComi extension and Manga Koinu source—delivering end-to-end integration, including build configurations, DTOs, parsing for manga details, chapters, and pages, image descrambling, and author/artist name corrections. Added base configuration and launcher assets to support the new sources and improve deployment readiness. Fixed reliability issues by resolving a NullPointerException in GigaViewer chapter list parsing and adopting a generic, pagination-aware approach to fetch chapter information for sites with paginated lists, accompanied by a base version increment. These changes broaden content coverage, improve metadata accuracy and chapter-list reliability, and establish a scalable pattern for adding future sources. Technologies demonstrated include DTO modeling, parsing pipelines, image descrambling, build/configuration management, and structured versioning.
Activity
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Quality Metrics
Correctness90.0%
Maintainability85.0%
Architecture87.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
GradleJavaKotlin
Technical Skills
API IntegrationAndroid DevelopmentBackend DevelopmentData ParsingExtension DevelopmentKotlinWeb Scraping
Repositories Contributed To
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