conda-forge/staged-recipes
Apr 2026 – Apr 2026
1 Month active
Languages Used
YAML
Technical Skills
PythonYAML configurationcondadependency managementdependency resolutionpackage management
Delong Chen
PROFILE
Delong Chen
Developed and delivered a new packaging recipe for the Choice-learn library in the conda-forge/staged-recipes repository, focusing on robust dependency management and cross-platform compatibility. Addressed Windows build handling and resolved noarch linter issues by refining build instructions and enforcing TensorFlow 2.14+ compatibility, which improved downstream install reliability. Utilized Python and YAML configuration to define dependencies, build steps, and automated tests, ensuring the package met conda-forge standards. Demonstrated attention to detail through clear documentation and collaborative problem-solving, resulting in smoother installation experiences for users and accelerated release readiness for the Choice-learn library across multiple operating systems.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
1
Lines of code
56
Activity Months1
Your Network
928 peopleShared Repositories
902
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Aaron RohrbaughMember
Work History
April 2026
3 Commits • 1 Features
Apr 1, 2026In April 2026, contributed to conda-forge/staged-recipes to enable reliable packaging for the Choice-learn library. Delivered a new packaging recipe for choice-learn 1.3.1 with its dependencies, build instructions, and tests, and resolved Windows build handling and noarch lint issues to align with TensorFlow 2.14+ compatibility. These changes improve cross-platform installability, reduce downstream packaging failures, and accelerate release readiness. Demonstrated proficiency in conda-build packaging, cross-platform (Windows) support, noarch constraints, and TF compatibility management, with clear, well-documented commits and collaboration across the team.
3 Commits • 1 Features
Apr 1, 2026In April 2026, contributed to conda-forge/staged-recipes to enable reliable packaging for the Choice-learn library. Delivered a new packaging recipe for choice-learn 1.3.1 with its dependencies, build instructions, and tests, and resolved Windows build handling and noarch lint issues to align with TensorFlow 2.14+ compatibility. These changes improve cross-platform installability, reduce downstream packaging failures, and accelerate release readiness. Demonstrated proficiency in conda-build packaging, cross-platform (Windows) support, noarch constraints, and TF compatibility management, with clear, well-documented commits and collaboration across the team.
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage33.4%
Skills & Technologies
Programming Languages
YAML
Technical Skills
PythonYAML configurationcondadependency managementdependency resolutionpackage management
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline