feat(profiler/behave_shell): emit motor.keystroke_cadence

BEHAVE-EXTRACTOR.md Phase B Step B.1.

* SessionContext gains typing_bursts: tuple[tuple[float, ...], ...]
  built by _split_typing_bursts(iats) — splits at gaps > IKI_THINK_MAX_S
  (1.5s) and drops bursts of fewer than 3 IATs. Mirrors prototype's
  _split_into_bursts at BEHAVE/prototype_extractors/shell/extract.py:275.
* _features/motor.py:keystroke_cadence(ctx) emits one Observation
  in {steady, bursty, hunt_and_peck, machine}. Median CV across
  typing bursts; mean IKI < IKI_MACHINE_MAX_S paired with CV <
  CV_MACHINE_MAX → machine. Confidence 0.85/0.70/0.65/0.60 per the
  prototype's calibration history.
* < MIN_INPUTS_FOR_CADENCE inputs or zero typing bursts → skip
  emission. v0.1 emits only the burst-CV variant; the prototype's
  NAIVE session-CV variant is parked for v0.2.
* Calibration grid widened (PHASE_A_PRIMITIVES → PHASE_AB_PRIMITIVES)
  to include motor.keystroke_cadence. Grid green across all five
  shards.

Tests: too-few-inputs → no emit, all-think-pauses → no burst → no
emit, uniform IATs → steady, sub-5ms → machine, mixed-pace → bursty,
extreme bimodal → hunt_and_peck.

decnet/profiler/behave_shell/_features/__init__.py

@@ -19,6 +19,7 @@ from decnet.profiler.behave_shell._features.cognitive import (
 )
 from decnet.profiler.behave_shell._features.motor import (
     input_modality,
+    keystroke_cadence,
     paste_burst_rate,
 )
 
@@ -27,6 +28,7 @@ FeatureFn = Callable[[SessionContext], Iterable[Observation]]
 FEATURES: tuple[FeatureFn, ...] = (
     input_modality,
     paste_burst_rate,
+    keystroke_cadence,
     inter_command_latency_class,
     command_branch_diversity,
     feedback_loop_engagement,