feat(profiler/behave_shell): emit cognitive.tool_vocabulary

Absolute distinct first_token_hash count, bucketed against
TOOL_VOCAB_NARROW_MAX / TOOL_VOCAB_BROAD_MIN. v0.1; D.8 re-tunes.

tests/profiler/behave_shell/test_cognitive_tool_vocabulary.py

@@ -0,0 +1,61 @@
+"""Step D.4: ``cognitive.tool_vocabulary``."""
+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 _cmds(tokens: list[str]) -> list[AsciinemaEvent]:
+    events: list[AsciinemaEvent] = []
+    for i, tok in enumerate(tokens):
+        t0 = i * 1.0
+        for j, c in enumerate(tok):
+            events.append((t0 + j * 0.05, "i", c))
+        events.append((t0 + len(tok) * 0.05, "i", "\r"))
+    return events
+
+
+def test_no_commands_no_emission() -> None:
+    out = list(extract_session([(0.0, "i", "x")], sid="tv-empty"))
+    assert [o for o in out if o.primitive == "cognitive.tool_vocabulary"] == []
+
+
+def test_few_distinct_tools_emit_narrow() -> None:
+    out = list(extract_session(
+        _cmds(["ls", "ls", "ps", "ps", "ls", "ps", "ls", "ps"]),
+        sid="tv-narrow",
+    ))
+    obs = _of(out, "cognitive.tool_vocabulary")
+    assert obs.value == "narrow"
+
+
+def test_mid_distinct_emit_moderate() -> None:
+    out = list(extract_session(
+        _cmds(["ls", "ps", "id", "uname", "whoami", "pwd"]),
+        sid="tv-mod",
+    ))
+    obs = _of(out, "cognitive.tool_vocabulary")
+    assert obs.value == "moderate"
+
+
+def test_many_distinct_tools_emit_broad() -> None:
+    out = list(extract_session(
+        _cmds(["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k"]),
+        sid="tv-broad",
+    ))
+    obs = _of(out, "cognitive.tool_vocabulary")
+    assert obs.value == "broad"
+
+
+def test_low_sample_count_reduces_confidence() -> None:
+    short = list(extract_session(_cmds(["a", "b"]), sid="tv-short"))
+    full = list(extract_session(_cmds(["a", "b", "c", "d", "e", "f"]), sid="tv-full"))
+    s = _of(short, "cognitive.tool_vocabulary")
+    f = _of(full, "cognitive.tool_vocabulary")
+    assert s.confidence < f.confidence