Apache Airflow

Rather than contributing to open source one PR at a time, I built an autonomous agent pipeline that runs the contribution cycle on apache/airflow: issue selection, bug reproduction, fix, tests, review follow-up. The result: 42 pull requests submitted, 20 merged into one of the largest Python open-source projects (38,000+ stars). These are not typo fixes: blocked path traversal through dag_id and run_id, fixed the JWT header for symmetric-key tokens, optimized a database migration to pure SQL, fixes across the Google (BigQuery, Dataproc), Oracle, Alibaba, SMTP and FAB providers, and in the task-sdk.

Challenges

• Understanding a very large codebase with strict contribution conventions
• Getting fixes through review by demanding maintainers of an Apache project
• Orchestrating reliable autonomous agents across the whole cycle: issue, fix, tests, review
• Covering varied subsystems: scheduler, task-sdk, providers, migrations, security

Solutions

• Autonomous agent pipeline built on Claude Code to run the contribution cycle
• Systematic bug reproduction before any fix, tests added with every PR
• Review follow-up and iteration until merge, following the project's conventions

Results

• 20 pull requests merged out of 42 submitted into apache/airflow
• 2 security fixes merged (path traversal via dag_id/run_id, JWT kid header)
• Contributions across scheduler, task-sdk, migrations and 6+ providers (Google, Oracle, Alibaba, SMTP, FAB, standard)
• All PRs public and verifiable on GitHub

Technologies

Python · Apache Airflow · Autonomous agents · Claude Code · Open Source · pytest