feat(profiler/behave_shell): emit motor.paste_burst_rate

BEHAVE-EXTRACTOR.md Phase A Step 3. Same paste-event ratio as
motor.input_modality but coarser-bucketed: this is the *habit*
signal (does the operator reach for paste at all?), where
input_modality is the dominant-channel signal.

* _features/motor.py:paste_burst_rate(ctx) emits one Observation
  per session in {none, occasional, habitual} with confidence
  0.70 / 0.70 / 0.80.
* Thresholds: PASTE_RATE_OCCASIONAL_MIN=0.10,
  PASTE_RATE_HABITUAL_MIN=0.50.

Splits YOU-sim from LW/CLAUDE-FF/CLAUDE-CL — LLM-driven sessions
paste habitually, real humans rarely paste.

Tests: pure-typed → none; 1-paste-in-10 → occasional;
paste-majority → habitual; output-only → no observation; habitual
confidence > occasional confidence.

decnet/profiler/behave_shell/_features/__init__.py

@@ -11,10 +11,14 @@ from typing import Callable, Iterable
 from decnet_behave_core.spec.envelope import Observation
 
 from decnet.profiler.behave_shell._ctx import SessionContext
-from decnet.profiler.behave_shell._features.motor import input_modality
+from decnet.profiler.behave_shell._features.motor import (
+    input_modality,
+    paste_burst_rate,
+)
 
 FeatureFn = Callable[[SessionContext], Iterable[Observation]]
 
 FEATURES: tuple[FeatureFn, ...] = (
     input_modality,
+    paste_burst_rate,
 )