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.

tests/profiler/behave_shell/test_cognitive_inter_command_latency_class.py

@@ -0,0 +1,81 @@
+"""Step 5: ``cognitive.inter_command_latency_class``."""
+from __future__ import annotations
+
+from decnet.profiler.behave_shell import extract_session
+from decnet.profiler.behave_shell._parse import AsciinemaEvent
+
+
+def _of(observations: list, primitive: str):
+    obs = [o for o in observations if o.primitive == primitive]
+    assert len(obs) == 1, f"expected exactly one {primitive}, got {len(obs)}"
+    return obs[0]
+
+
+def _command_stream(starts: list[float]) -> list[AsciinemaEvent]:
+    """Build an input stream that yields commands at the given start times."""
+    events: list[AsciinemaEvent] = []
+    for s in starts:
+        events.append((s, "i", "x"))
+        events.append((s + 0.05, "i", "\r"))
+    return events
+
+
+def test_no_commands_means_no_observation() -> None:
+    out = list(extract_session([], sid="lat-empty"))
+    assert [o for o in out if o.primitive == "cognitive.inter_command_latency_class"] == []
+
+
+def test_single_command_no_iat_no_observation() -> None:
+    out = list(extract_session(_command_stream([0.0]), sid="lat-1"))
+    assert [o for o in out if o.primitive == "cognitive.inter_command_latency_class"] == []
+
+
+def test_instant_bucket() -> None:
+    # IATs of 0.1s — well under 0.30 cap
+    starts = [i * 0.15 for i in range(6)]
+    out = list(extract_session(_command_stream(starts), sid="lat-instant"))
+    assert _of(out, "cognitive.inter_command_latency_class").value == "instant"
+
+
+def test_typing_speed_bucket() -> None:
+    # IATs around 1.0s
+    starts = [i * 1.0 for i in range(6)]
+    out = list(extract_session(_command_stream(starts), sid="lat-typing"))
+    assert _of(out, "cognitive.inter_command_latency_class").value == "typing_speed"
+
+
+def test_deliberate_bucket() -> None:
+    # IATs around 1.85s — above typing (1.5), under deliberate cap (2.0)
+    starts = [i * 1.9 for i in range(6)]
+    out = list(extract_session(_command_stream(starts), sid="lat-deliberate"))
+    assert _of(out, "cognitive.inter_command_latency_class").value == "deliberate"
+
+
+def test_llm_lightweight_bucket() -> None:
+    # IATs around 5s — within 2-8s band
+    starts = [i * 5.05 for i in range(6)]
+    out = list(extract_session(_command_stream(starts), sid="lat-lwt"))
+    assert _of(out, "cognitive.inter_command_latency_class").value == "llm_lightweight"
+
+
+def test_llm_heavyweight_bucket() -> None:
+    # IATs around 15s — within 8-30s band; matches Claude Opus empirical
+    starts = [i * 15.05 for i in range(6)]
+    out = list(extract_session(_command_stream(starts), sid="lat-hvy"))
+    assert _of(out, "cognitive.inter_command_latency_class").value == "llm_heavyweight"
+
+
+def test_long_bucket() -> None:
+    # IATs > 30s
+    starts = [i * 60.0 for i in range(6)]
+    out = list(extract_session(_command_stream(starts), sid="lat-long"))
+    assert _of(out, "cognitive.inter_command_latency_class").value == "long"
+
+
+def test_low_sample_count_reduces_confidence() -> None:
+    # 2 commands → 1 IAT; below the floor
+    short = list(extract_session(_command_stream([0.0, 1.0]), sid="lat-low"))
+    full = list(extract_session(_command_stream([i * 1.0 for i in range(6)]), sid="lat-full"))
+    s = _of(short, "cognitive.inter_command_latency_class")
+    f = _of(full, "cognitive.inter_command_latency_class")
+    assert s.confidence < f.confidence