Three checks to verify an AI coding assistant's bug fix before trusting it

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When an AI coding assistant claims a bug is fixed, the article recommends requesting three kinds of evidence: a reproduction of the bug, a regression test that failed before the patch, and fresh test output. It describes three skills from the obra/superpowers project for this purpose, starting with systematic-debugging, which asks the assistant to pinpoint where actual behavior first diverges from expectations. The test-driven-development approach involves capturing the expected behavior in a test, such as rejecting February 29 in a non-leap year for a date parser, and confirming it fails for the right reason before applying the smallest fix. The verification-before-completion skill calls for reviewing the commands, exit status, and untested paths behind any claimed fix, while noting that a passing regression test only covers what it asserts.

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