google/adk-samples
Apr 2026 – May 2026
2 Months active
Languages Used
Python
Technical Skills
AI DevelopmentAI IntegrationAgile MethodologiesCloud ComputingDevOpsPython Development
Agata Golebiowska
PROFILE
Agata Golebiowska
Contributed to the google/adk-samples repository by developing AI-driven automation components for the software development lifecycle, including agents for task planning, technical design, and user story refinement. Leveraged Python and cloud computing to standardize outputs, remove internal references, and streamline external sharing, supporting faster decision-making and improved collaboration. Enhanced deployment automation by updating configurations for Google Cloud, enabling region support and dependency management for scalable releases. Applied agile methodologies and DevOps practices to ensure code hygiene and effective teamwork across multiple co-authored commits. The work focused on robust software architecture, task automation, and seamless integration of AI capabilities into SDLC workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
5Total
Bugs
0
Commits
5
Features
2
Lines of code
30,215
Activity Months2
Your Network
42 peopleWork History
May 2026
1 Commits • 1 Features
May 1, 2026May 2026 monthly summary focusing on the Google ADK Samples repo contributions and deployment improvements.
1 Commits • 1 Features
May 1, 2026May 2026 monthly summary focusing on the Google ADK Samples repo contributions and deployment improvements.
May 2026
April 2026
4 Commits • 1 Features
Apr 1, 2026April 2026 (Month: 2026-04) - Concise performance summary for google/adk-samples focusing on business value and technical execution. Delivered AI-driven SDLC automation components, refined collaboration workflows, and improved output clarity to support external sharing and faster decision making.
April 2026
4 Commits • 1 Features
Apr 1, 2026April 2026 (Month: 2026-04) - Concise performance summary for google/adk-samples focusing on business value and technical execution. Delivered AI-driven SDLC automation components, refined collaboration workflows, and improved output clarity to support external sharing and faster decision making.
Activity
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Quality Metrics
Correctness84.0%
Maintainability80.0%
Architecture84.0%
Performance80.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AI DevelopmentAI IntegrationAgile MethodologiesCloud ComputingCloud DeploymentDevOpsPython DevelopmentSoftware ArchitectureSoftware EngineeringTask Automation
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