profcomff/rating-api
Aug 2025 – Aug 2025
1 Month active
Languages Used
PythonSQL
Technical Skills
API DevelopmentBackend DevelopmentCode FormattingDatabase ManagementDatabase Migrations
Vladislav
PROFILE
Vladislav
Vladislav Voskoboinik developed the Lecturer Ratings System for the profcomff/rating-api repository, introducing a new database model and table to store structured lecturer feedback. He implemented a REST API endpoint using Python and SQL, enabling users to create rating entries while ensuring data integrity and maintainability. His work included careful backend development, database migrations, and code formatting improvements through linting, which enhanced overall code quality. Although the project scope was focused, Vladislav’s disciplined commit practices and attention to code cleanliness positioned the system for analytics support and QA readiness, directly addressing the need for reliable lecturer rating capture and analysis.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
121
Activity Months1
Work History
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for profcomff/rating-api: Delivered the Lecturer Ratings System feature, including a new database table/model for storing lecturer ratings and an API endpoint to create rating entries. Commit activity focused on endpoint logic and code quality cleanup (post-rank-handler; lint-fix). No major bugs fixed this month; minor stability improvements came from lint cleaning tied to the feature. Overall impact: enables structured lecturer feedback, supports analytics, and improves data integrity and maintainability. Technologies demonstrated: REST API design, database modeling, linting/code quality tooling, and disciplined commit practices. This work positions the rating-api for QA and release readiness with clear business value in rating capture and analytics.
2 Commits • 1 Features
Aug 1, 2025August 2025 monthly summary for profcomff/rating-api: Delivered the Lecturer Ratings System feature, including a new database table/model for storing lecturer ratings and an API endpoint to create rating entries. Commit activity focused on endpoint logic and code quality cleanup (post-rank-handler; lint-fix). No major bugs fixed this month; minor stability improvements came from lint cleaning tied to the feature. Overall impact: enables structured lecturer feedback, supports analytics, and improves data integrity and maintainability. Technologies demonstrated: REST API design, database modeling, linting/code quality tooling, and disciplined commit practices. This work positions the rating-api for QA and release readiness with clear business value in rating capture and analytics.
August 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonSQL
Technical Skills
API DevelopmentBackend DevelopmentCode FormattingDatabase ManagementDatabase Migrations
Repositories Contributed To
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