yt-dlp/yt-dlp
Jul 2025 – Jan 2026
2 Months active
Languages Used
Python
Technical Skills
Data ExtractionPython DevelopmentWeb ScrapingAPI integrationPythonPython programming
rdamas
PROFILE
Rdamas
Over a two-month period, contributed to the yt-dlp/yt-dlp repository by developing and enhancing video extraction features using Python, with a focus on data extraction and web scraping. Improved the Archive.org playlist parsing by replacing regex-based extraction with a more robust tag-based approach, which reduced parsing errors and increased metadata accuracy. Expanded source coverage by enhancing the CCC extractor and introducing a new ERR Arhiiv extractor, enabling retrieval of richer metadata and support for additional streaming formats. Demonstrated strong skills in Python development, API integration, and metadata normalization, resulting in more reliable extraction and improved downstream metadata quality.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
3Total
Bugs
0
Commits
3
Features
3
Lines of code
113
Activity Months2
Your Network
127 peopleWork History
January 2026
2 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for the yt-dlp/yt-dlp repository, focusing on feature delivery, stability, and value realization. Key work concentrated on expanding source coverage and metadata quality through extractor enhancements and new extractors. What was delivered: - CCC extractor enhancement: improved event ID extraction logic and added new video entries with richer metadata. - ERR Arhiiv extractor: added a new extractor to retrieve video content, including handling video metadata and supported streaming formats. Impact and business value: - Expanded content coverage across European sources, enabling broader content retrieval and reducing manual curation effort. - Improved metadata quality and consistency, enhancing downstream processing, searchability, and user-facing metadata presentation. - Strengthened extraction reliability and maintainability by adding robust extractors within the existing framework. Technologies/skills demonstrated: - Python extractor architecture, metadata handling, and streaming formats - Source integration and data normalization - Code-level collaboration and review, with clear author attribution to contributors
2 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary for the yt-dlp/yt-dlp repository, focusing on feature delivery, stability, and value realization. Key work concentrated on expanding source coverage and metadata quality through extractor enhancements and new extractors. What was delivered: - CCC extractor enhancement: improved event ID extraction logic and added new video entries with richer metadata. - ERR Arhiiv extractor: added a new extractor to retrieve video content, including handling video metadata and supported streaming formats. Impact and business value: - Expanded content coverage across European sources, enabling broader content retrieval and reducing manual curation effort. - Improved metadata quality and consistency, enhancing downstream processing, searchability, and user-facing metadata presentation. - Strengthened extraction reliability and maintainability by adding robust extractors within the existing framework. Technologies/skills demonstrated: - Python extractor architecture, metadata handling, and streaming formats - Source integration and data normalization - Code-level collaboration and review, with clear author attribution to contributors
January 2026
July 2025
1 Commits • 1 Features
Jul 1, 2025Summary for 2025-07: Focused on reliability and metadata quality for Archive.org playlist parsing in yt-dlp. Implemented ArchiveOrgIE Playlist Parsing Enhancement by replacing regex-based extraction with a robust get_element_text_and_html_by_tag approach and added extraction of the 'track' field to improve metadata accuracy. The change reduces parsing errors for playlists and enhances downstream metadata quality.
July 2025
1 Commits • 1 Features
Jul 1, 2025Summary for 2025-07: Focused on reliability and metadata quality for Archive.org playlist parsing in yt-dlp. Implemented ArchiveOrgIE Playlist Parsing Enhancement by replacing regex-based extraction with a robust get_element_text_and_html_by_tag approach and added extraction of the 'track' field to improve metadata accuracy. The change reduces parsing errors for playlists and enhances downstream metadata quality.
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
API integrationData ExtractionPythonPython DevelopmentPython programmingWeb Scrapingdata extractionvideo extractionweb scraping
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