I work on reproducible bugs in existing code: isolate the failing behavior, change the smallest practical surface, add a regression check, and explain what remains untested. I use AI as a development aid and review the result against the code and test output before submitting it.
Merged into Hotragn/husk, PR #131 — The system-information probes could ignore container CPU and memory limits. I changed the cgroup v1/v2 probes and exercised both paths against synthetic layouts. The maintainer then checked a real constrained Docker container and confirmed matching output before merging. The review also identified source-text assertions that were too brittle; I keep behavior checks as the primary evidence.
Merged into petl, PR #718 — A separate Python exception-formatting fix with regression tests. The maintainer approved it and merged it after CI passed. It is not a client project.
I can assess one reproducible issue in an existing local Python CSV/JSON script, then agree on the exact scope and delivery date. A suitable handoff includes the minimal patch, a targeted regression check, a short cause-and-effect explanation, and a way to revert it. Initial examples should use invented data; please do not send credentials or personal records. I work asynchronously from Japan.
See the current service and request a scope check on Coconala. This profile shows public code contributions, not a history of paid client projects.