Ran the humanizer rules over 79 prose files in the active specs (brief,
design, requirements, research and narrative docs). Removed em dashes,
significance inflation, forced rule-of-three, bold-label list items and
filler. Skipped tasks.md checklists and the retired specs (harnessx-*,
openrouter-sast-harness, unseen-repo-evaluation).
Every number, path, command, hash and identifier was preserved: checked
each file against HEAD for drift in numbers, inline code and code fences,
zero drift across all 79 files.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
Req 6.5 (maintainer decision 2026-10-05, with search-with-proof): BLOCK comes from
verified demonstrations; the engine is judged by top-K recall per push and advisory
false alarms; the pooled block gate governs only families without an oracle.
Task 17 adds the metric and re-scores existing arms at no cost.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Maintainer decision 2026-10-03: BLOCK precision is shown pooled across
families. Req 6.4 (amended; 7.4 annotated): one t_block on a per-family
Platt-normalised score (calibrate.fit_map, training folds, inner
cross-fit), Wilson 95% LB >= 0.95 over all families' training flags; a
family blocks only with >= F = 10 held-out flags and zero errors, else
advisory; t_quiet per family keeps >= 90% of training paired positives.
evaluate: normalised, pooled_threshold, quiet_threshold,
pooled_block_gate, family_floor, apply_floor. experiments: rule
pooled_block_gate (normalise: platt, floor, recall), decide_pooled on the
pre-floor pooled held-out LB, floor table, quiet_recall and after-floor
totals in analyse_rule; exp-003's analysis is unchanged.
plane/experiments/exp-004-pooled-block-gate.yaml: procedure and decision
rule registered now; units frozen at run time from the post-harvest
memory (units_status pending until then).
Tests (tests/test_learn_rules.py): pooled threshold where no family alone
reaches the bound, no held-out label moves a threshold or the map, a
family with one held-out error cannot block, t_quiet and no blind
silencing, the pooled verdict, the exp-004 manifest.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer: data will not scale; the decision engine is an AI classifier, not ML,
with local memory, DSPy style. In-house on the metered client; retrieval by signal
profile re-ranked by code embeddings; compiled (bootstrap few-shot, instruction
search) per training fold; retrieval never crosses the evaluation boundary;
confidence calibrated on held-out repositories. Design and tasks 6+ to revise.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer: hard-coded application specifications are problematic; WordPress is
not the only PHP application, same for Python and JS. Language-level knowledge
stays; framework roles are inferred per repository; existing framework entries
become tagged optional priors kept only by A/B evidence; leave-one-framework-out
evaluation.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer: the tool will never run against the test cases it is trained with;
its purpose is a repo safety net. Repository-grouped folds, repository-agnostic
features, BLOCK/ADVISORY operating points for changed code, out-of-repo
calibration, adaptation kept separate.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
Maintainer direction: A/B testing to extrapolate detection instead of hard-coding
it, and a trained decision engine. Instruments become signal producers; labels
from ground truth with provenance; a simple calibrated engine with population
folds; experiments as plane Runs; extrapolation with intervals, checked once on
frozen v3; the engine is what scan/pre-push/plane report.
Co-Authored-By: Claude Opus 5.5 <[email protected]>
Claude-Session: https://claude.ai/code/session_01EmM1j46qgKMh3zqD1QMwEo
