Group 1 complete. Tasks 1.2 and 1.3: `candidates.ceiling`, `ousast learning
candidates`, and the stop-or-go reading. No model is called anywhere in this
group, so the condition that could have ended the feature was reachable for
free.
Verdict: go.
agent-vfc (deciding) 89.7% ceiling against 44.4% round-zero detection
vfc (deciding) 84.7%, baseline still running, non-gating
vfc-js (deciding) 52.9% -- see below
github (deciding) 100.0%
vibe-py (development)100.0% against 83.3%
The margin is largest on agent-vfc, which is the slice the tool is actually
for.
The measurement forced a shape correction before the detector existed.
config_secrets first read 63.6% with a bind-and-literal shape, because a
permissive default is as often an argument to a call -- cors({origin: '*'}),
app.use(session({secure: false})) -- as it is a binding. Adding call sites to
that family's shape took it to 100% and recovered four agent-vfc rows.
Carry-forward, and not this feature's to fix: vfc-js sits at 52.9% because
eight of seventeen rows label a function the JavaScript CST names `<anon>` --
`module.exports.publish = function () {}` and its relatives. That is a
function-naming gap, it caps the existing hunter on that slice exactly as
much, and it is the same class as the `_declarator_start` defects found in
learning-harness task 4.1.
The ruleset arm of `ceiling` is kept so the 12.1% that rejected it stays a
measurement rather than a remembered number.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
