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/_ctx.py

@@ -20,6 +20,7 @@ from decnet.profiler.behave_shell._parse import (
     hash_token,
 )
 from decnet.profiler.behave_shell._thresholds import (
+    IKI_THINK_MAX_S,
     PASTE_BURST_MAX_IAT_S,
     PASTE_MIN_CHARS_PER_EVENT,
 )
@@ -47,6 +48,9 @@ class SessionContext:
     inter_cmd_iats: tuple[float, ...] = field(default_factory=tuple)
     output_per_cmd: tuple[int, ...] = field(default_factory=tuple)
 
+    # Step B.1 derivations — typing bursts (IATs split at think-pauses)
+    typing_bursts: tuple[tuple[float, ...], ...] = field(default_factory=tuple)
+
 
 def _detect_paste_bursts(
     inputs: list[AsciinemaEvent],
@@ -102,6 +106,22 @@ def _detect_paste_bursts(
     return tuple(bursts), paste_count
 
 
+def _split_typing_bursts(iats: tuple[float, ...]) -> tuple[tuple[float, ...], ...]:
+    """Split a flat IAT sequence at gaps > IKI_THINK_MAX_S.
+
+    Drops bursts of fewer than 3 IATs — too short to compute a stable
+    CV. Mirrors BEHAVE prototype's ``_split_into_bursts``.
+    """
+    bursts: list[list[float]] = [[]]
+    for x in iats:
+        if x > IKI_THINK_MAX_S:
+            if bursts[-1]:
+                bursts.append([])
+        else:
+            bursts[-1].append(x)
+    return tuple(tuple(b) for b in bursts if len(b) >= 3)
+
+
 def _segment_commands(inputs: list[AsciinemaEvent]) -> tuple[Command, ...]:
     """Walk input events, splitting on ``\\r`` / ``\\n`` into commands.
 
@@ -179,6 +199,7 @@ def build_session_context(
         max(0.0, inputs[i][0] - inputs[i - 1][0]) for i in range(1, len(inputs))
     )
     paste_bursts, paste_count = _detect_paste_bursts(inputs)
+    typing_bursts = _split_typing_bursts(iats)
     commands = _segment_commands(inputs)
     inter_cmd_iats = tuple(
         max(0.0, commands[i + 1].start_ts - commands[i].end_ts)
@@ -204,4 +225,5 @@ def build_session_context(
         commands=commands,
         inter_cmd_iats=inter_cmd_iats,
         output_per_cmd=output_per_cmd,
+        typing_bursts=typing_bursts,
     )

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,

decnet/profiler/behave_shell/_features/motor.py

@@ -2,9 +2,12 @@
 
 Step 2: ``motor.input_modality`` — typed / pasted / mixed.
 Step 3: ``motor.paste_burst_rate`` — none / occasional / habitual.
+Step B.1: ``motor.keystroke_cadence`` — steady / bursty / hunt_and_peck / machine.
 """
 from __future__ import annotations
 
+import statistics
+from itertools import chain
 from typing import Iterator
 
 from decnet_behave_core.spec.envelope import Observation
@@ -12,6 +15,11 @@ from decnet_behave_core.spec.envelope import Observation
 from decnet.profiler.behave_shell._ctx import SessionContext
 from decnet.profiler.behave_shell._features._emit import make_observation
 from decnet.profiler.behave_shell._thresholds import (
+    CV_BURSTY_MAX,
+    CV_MACHINE_MAX,
+    CV_STEADY_MAX,
+    IKI_MACHINE_MAX_S,
+    MIN_INPUTS_FOR_CADENCE,
     MODALITY_PASTED_MIN,
     MODALITY_TYPED_MAX,
     PASTE_RATE_HABITUAL_MIN,
@@ -76,3 +84,45 @@ def paste_burst_rate(ctx: SessionContext) -> Iterator[Observation]:
         value=level,
         confidence=confidence,
     )
+
+
+def keystroke_cadence(ctx: SessionContext) -> Iterator[Observation]:
+    """Emit ``motor.keystroke_cadence`` ∈ {steady, bursty, hunt_and_peck, machine}.
+
+    Median CV of within-typing-burst IATs (bursts split at gaps >
+    ``IKI_THINK_MAX_S`` so think-pauses between commands don't
+    inflate the variance). Pasted-only sessions and sessions below
+    ``MIN_INPUTS_FOR_CADENCE`` skip emission — no honest cadence
+    available.
+
+    v0.1 emits only the burst-CV variant. The prototype's NAIVE
+    session-CV variant (lower confidence, second emission per
+    primitive) is parked for v0.2.
+    """
+    if len(ctx.input_events) < MIN_INPUTS_FOR_CADENCE:
+        return
+    if not ctx.typing_bursts:
+        return
+    burst_cvs: list[float] = []
+    for b in ctx.typing_bursts:
+        m = statistics.fmean(b)
+        if m > 0:
+            burst_cvs.append(statistics.pstdev(b) / m)
+    if not burst_cvs:
+        return
+    cv = statistics.median(burst_cvs)
+    mean_iki = statistics.fmean(chain.from_iterable(ctx.typing_bursts))
+    if mean_iki < IKI_MACHINE_MAX_S and cv < CV_MACHINE_MAX:
+        value, confidence = "machine", 0.85
+    elif cv < CV_STEADY_MAX:
+        value, confidence = "steady", 0.70
+    elif cv < CV_BURSTY_MAX:
+        value, confidence = "bursty", 0.65
+    else:
+        value, confidence = "hunt_and_peck", 0.60
+    yield make_observation(
+        ctx,
+        primitive="motor.keystroke_cadence",
+        value=value,
+        confidence=confidence,
+    )

decnet/profiler/behave_shell/_thresholds.py

@@ -75,3 +75,18 @@ FEEDBACK_MIN_PAIRS: int = 5
 # via Hartigan dip is filed for v0.2).
 PAUSE_CV_METRONOMIC_MAX: float = 0.40
 PAUSE_CV_BIMODAL_MIN: float = 1.50
+
+# ── motor.keystroke_cadence (Step B.1) ──────────────────────────────────────
+# Typing bursts split at gaps > IKI_THINK_MAX_S so think-pauses between
+# commands don't inflate the within-burst CV. Mirrors the prototype's
+# _split_into_bursts (BEHAVE/prototype_extractors/shell/extract.py:275-286).
+IKI_THINK_MAX_S: float = 1.50
+# Sub-human floor for the "machine" classification — only paired with a
+# pathologically uniform CV, since real humans never produce sub-5ms IATs
+# in a sustained burst.
+IKI_MACHINE_MAX_S: float = 0.005
+CV_MACHINE_MAX: float = 0.05
+CV_STEADY_MAX: float = 0.50
+CV_BURSTY_MAX: float = 1.50
+# Need this many input events before we'll claim a cadence at all.
+MIN_INPUTS_FOR_CADENCE: int = 5