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
Design approved in session. Every one of the 36 criteria is claimed by a task.
Group 1 calls no model at all. The candidate enumerator, the ceiling report
and the stop-or-go reading are offline, so the condition that ends this
feature -- a deciding slice whose ceiling sits below its recorded detection
rate -- is reachable before a cent is spent. The ruleset variant of this same
design would have failed that condition at 12.1% against 64.9%.
Group 2 is the only work that touches scoring.py, and its observable is that
nothing changes: the recorded round-zero artifacts are the pin, and if they
cannot be reproduced exactly then the refactor is wrong, not the numbers.
Group 3 builds the detector, and task 3.3 is the test that would have caught
the defect this spec exists for -- two runs must make an identical sequence of
model calls. It lives in the suite and runs repeatedly, not in a one-off
measurement.
Group 4 is the maintainer's emphasis as code: deciding slices, development
slices, the cross-slice rejection by name, and the overfitting gap printed
beside every headline number.
Group 5 compares the two detectors per slice, re-measures the trajectory count
and the noise floor, and names -- without amending -- the learning-harness
requirements that a collapsed floor would retire.
Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG
