Description
When analyzing candidates with a large number of GitHub repositories, the agent makes deep API calls to fetch commit history, languages, and metadata for every single repository. This creates two critical issues:
- Severe performance bottlenecks (evaluations take an unacceptably long time).
- High risk of hitting GitHub API rate limits, which causes the agent to crash or fail to complete the evaluation.
Expected Behavior
The agent should prioritize the most relevant repositories to analyze and cap the maximum number of repositories to prevent rate limit exhaustion and ensure timely processing.
Steps to Reproduce
- Run
python score.py on a candidate's resume/profile who has 50+ repositories on GitHub.
- Observe the console hanging for a long period during the GitHub retrieval phase.
- If run multiple times or with multiple high-repo candidates, observe GitHub API Rate Limit errors.
Proposed Solution
Modify the repository retrieval logic to sort the candidate's repositories by stargazers_count (descending) and cap the deep analysis to the top 15 repositories. This ensures the agent still evaluates the most impactful work while preserving API quota and execution speed.
Description
When analyzing candidates with a large number of GitHub repositories, the agent makes deep API calls to fetch commit history, languages, and metadata for every single repository. This creates two critical issues:
Expected Behavior
The agent should prioritize the most relevant repositories to analyze and cap the maximum number of repositories to prevent rate limit exhaustion and ensure timely processing.
Steps to Reproduce
python score.pyon a candidate's resume/profile who has 50+ repositories on GitHub.Proposed Solution
Modify the repository retrieval logic to sort the candidate's repositories by
stargazers_count(descending) and cap the deep analysis to the top 15 repositories. This ensures the agent still evaluates the most impactful work while preserving API quota and execution speed.