docs: plain-English pass over the active specs

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]>

.kiro/specs/constrained-detector/design.md

@@ -29,10 +29,10 @@ already reaches, while the **semantic IR caps it at 96.6%** with a median of ten
 
 ### This Spec Owns
 
-- `learning/candidates.py` — what a candidate is, how the IR enumerates one per family shape, and the ceiling report.
-- `learning/judgments.py` — the typed answer, and the toolless call that produces it.
-- `learning/predicates.py` — the checks that constrain a claim at generation time and score it afterwards, defined once.
-- `learning/slices.py` — deciding versus development slices, the overfitting gap, and the cross-slice rejection rule.
+- `learning/candidates.py`, what a candidate is, how the IR enumerates one per family shape, and the ceiling report.
+- `learning/judgments.py`, the typed answer, and the toolless call that produces it.
+- `learning/predicates.py`, the checks that constrain a claim at generation time and score it afterwards, defined once.
+- `learning/slices.py`, deciding versus development slices, the overfitting gap, and the cross-slice rejection rule.
 - `run_constrained_detector` in `learning/detectors.py`, beside the existing `run_family_detector`.
 - `ousast learning candidates`, and a detector selector on `score` and `baseline`.
 
@@ -113,16 +113,16 @@ src/openultrasast/learning/
 
 ### Modified Files
 
-- `src/openultrasast/learning/detectors.py` — `run_constrained_detector(root, region, config, *, client, model,
+- `src/openultrasast/learning/detectors.py`, `run_constrained_detector(root, region, config, *, client, model,
   taxonomy, ranges)`; `FamilyConfig` gains nothing, because a constrained detector is configured by the same
   prompt/checklist/limits and reads no tools.
-- `src/openultrasast/learning/scoring.py` — `_hits`, `_others` and `_fabricated` delegate to `predicates`; the
+- `src/openultrasast/learning/scoring.py`, `_hits`, `_others` and `_fabricated` delegate to `predicates`; the
   family metrics gain nothing. A test asserts the recorded baseline numbers are reproduced exactly.
-- `src/openultrasast/learning/publish.py` — the overfitting gap and the per-slice table (Req 3.1, 3.4).
-- `src/openultrasast/config.py` — `[learning] detector = "hunter" | "constrained"`, default `hunter` until the
+- `src/openultrasast/learning/publish.py`, the overfitting gap and the per-slice table (Req 3.1, 3.4).
+- `src/openultrasast/config.py`, `[learning] detector = "hunter" | "constrained"`, default `hunter` until the
   comparison says otherwise.
-- `src/openultrasast/cli.py` — `learning candidates`; `--detector` on `score` and `baseline`.
-- `pyproject.toml` — unchanged.
+- `src/openultrasast/cli.py`, `learning candidates`; `--detector` on `score` and `baseline`.
+- `pyproject.toml`, unchanged.
 
 ## System Flows
 
@@ -282,13 +282,13 @@ def teachable(slice_name: str, cases) -> bool       # False while every row of a
 
 ## Migration and Phases
 
-1. **Candidates and the ceiling, offline.** `candidates.py`, `learning candidates`, the per-slice ceiling artifact
+1. Candidates and the ceiling, offline. `candidates.py`, `learning candidates`, the per-slice ceiling artifact
    including the real-world slices (Req 3.5). No model call, so the phase is free and its result can still cancel
-   the design — if the ceiling on a deciding slice is below that slice's current detection rate, stop here.
-2. **Predicates extracted.** `predicates.py`, `scoring.py` delegating, the baseline-reproduction test.
-3. **Judgment and the constrained detector.** `judgments.py`, `run_constrained_detector`, the determinism test.
-4. **Slices and reporting.** `slices.py`, the overfitting gap, `publish` per slice.
-5. **The comparison.** Constrained against unconstrained on every slice, the re-measured trajectory count and noise
+   the design. If the ceiling on a deciding slice is below that slice's current detection rate, stop here.
+2. Predicates extracted. `predicates.py`, `scoring.py` delegating, the baseline-reproduction test.
+3. Judgment and the constrained detector. `judgments.py`, `run_constrained_detector`, the determinism test.
+4. Slices and reporting. `slices.py`, the overfitting gap, `publish` per slice.
+5. The comparison. Constrained against unconstrained on every slice, the re-measured trajectory count and noise
    floor, and the named `learning-harness` amendments if the floor collapses.
 
 Phase 1 is deliberately able to kill the design before any money is spent on it.