The dependency gap in AI-generated code is what the source imports minus what the manifest declares. A green CI run only proves the packages were already installed on the author's machine, not on a fresh checkout. So measure the gap statically, before merge. repro_probe.py reads the source with ast and never runs it. On a project that declared one package but imported four: gap=3, coverage 25.0%, exit 1.
AI disclosure: I drafted this with an AI writing assistant. The tool, the three fixtures, and every number below come from a real local run on Python 3.13.5, stdlib only, no network. I ran it, checked the exit codes, hashed the STDOUT twice to confirm it's byte-for-byte deterministic, and edited every line before publishing.
Green CI is a machine telling you it agreed with itself. The interesting question is what happens on a machine that isn't yours.
Here's the failure I keep seeing. An agent writes a feature. It imports pandas, numpy, a YAML parser. The tests pass, because the agent's environment already had those installed from some earlier task. The diff lands. A teammate pulls it, runs pip install -r requirements.txt, runs the code, and gets ModuleNotFoundError: No module named 'numpy'. The manifest never learned about the import. Nobody lied. The information just never made it from the source file into the dependency list.
