feat(profiler/behave_shell): emit cognitive.inter_command_latency_class

BEHAVE-EXTRACTOR.md Phase A Step 5. Classifies the operator's
thinking pace between commands. Splits LW-sim / CLAUDE-FF /
CLAUDE-CL.

* _features/cognitive.py:inter_command_latency_class(ctx) emits one
  Observation in {instant, typing_speed, deliberate,
  llm_lightweight, llm_heavyweight, long}, computed as the median
  of ctx.inter_cmd_iats bucketed against the prototype thresholds
  (v0.2 split: lightweight 2-8s, heavyweight 8-30s).
* Sample-size honesty: < 5 commands halves confidence (0.40 vs
  0.80) per BEHAVE-EXTRACTOR.md.
* Threshold consts (INTER_CMD_*_MAX, MIN_COMMANDS_FOR_FULL_CONFIDENCE,
  plus parked Step 6/7/8 thresholds for the next three commits)
  added to _thresholds.py.

Tests cover all six buckets at empirically-anchored IATs (15s ≈
Claude Opus driving recon via tmux send-keys), plus the
single-command no-IAT and low-sample-count paths.

decnet/profiler/behave_shell/_features/__init__.py

@@ -11,6 +11,9 @@ 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.cognitive import (
+    inter_command_latency_class,
+)
 from decnet.profiler.behave_shell._features.motor import (
     input_modality,
     paste_burst_rate,
@@ -21,4 +24,5 @@ FeatureFn = Callable[[SessionContext], Iterable[Observation]]
 FEATURES: tuple[FeatureFn, ...] = (
     input_modality,
     paste_burst_rate,
+    inter_command_latency_class,
 )

decnet/profiler/behave_shell/_features/cognitive.py

@@ -0,0 +1,61 @@
+"""``cognitive.*`` feature functions.
+
+Step 5: ``cognitive.inter_command_latency_class``.
+Step 6: ``cognitive.command_branch_diversity``.
+Step 7: ``cognitive.feedback_loop_engagement``.
+Step 8: ``cognitive.inter_command_consistency``.
+"""
+from __future__ import annotations
+
+import statistics
+from typing import Iterator
+
+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 (
+    INTER_CMD_DELIBERATE_MAX,
+    INTER_CMD_INSTANT_MAX,
+    INTER_CMD_LLM_HEAVYWEIGHT_MAX,
+    INTER_CMD_LLM_LIGHTWEIGHT_MAX,
+    INTER_CMD_TYPING_MAX,
+    MIN_COMMANDS_FOR_FULL_CONFIDENCE,
+)
+
+
+def _bucket_inter_cmd_latency(median_iat: float) -> str:
+    if median_iat <= INTER_CMD_INSTANT_MAX:
+        return "instant"
+    if median_iat <= INTER_CMD_TYPING_MAX:
+        return "typing_speed"
+    if median_iat <= INTER_CMD_DELIBERATE_MAX:
+        return "deliberate"
+    if median_iat <= INTER_CMD_LLM_LIGHTWEIGHT_MAX:
+        return "llm_lightweight"
+    if median_iat <= INTER_CMD_LLM_HEAVYWEIGHT_MAX:
+        return "llm_heavyweight"
+    return "long"
+
+
+def inter_command_latency_class(ctx: SessionContext) -> Iterator[Observation]:
+    """Emit ``cognitive.inter_command_latency_class``.
+
+    Operator's *thinking pace* between commands, bucketed against
+    calibrated thresholds. Splits LW-sim / CLAUDE-FF / CLAUDE-CL.
+    """
+    if not ctx.inter_cmd_iats:
+        return
+    median_iat = statistics.median(ctx.inter_cmd_iats)
+    bucket = _bucket_inter_cmd_latency(median_iat)
+    # Sample-size honesty: < 5 commands → halve confidence
+    if len(ctx.commands) < MIN_COMMANDS_FOR_FULL_CONFIDENCE:
+        confidence = 0.40
+    else:
+        confidence = 0.80
+    yield make_observation(
+        ctx,
+        primitive="cognitive.inter_command_latency_class",
+        value=bucket,
+        confidence=confidence,
+    )