test(profiler/behave_shell): five-class calibration grid lockdown

BEHAVE-EXTRACTOR.md Phase A Step 9 — the gate. Runs the pure
engine against each of the five 2026-05-02 calibration shards and
pins the contract that all subsequent Phase B-G PRs must keep
green: every Phase A primitive (motor.input_modality,
motor.paste_burst_rate, cognitive.inter_command_latency_class,
cognitive.command_branch_diversity, cognitive.feedback_loop_engagement,
cognitive.inter_command_consistency) fires at least once per shard.

* tests/profiler/behave_shell/test_calibration_grid.py
  parametrized over (shard_file, class_label) for HUMAN / YOU-sim /
  LW-sim / CLAUDE-FF / CLAUDE-CL. Skips entirely when
  BEHAVE_CALIBRATION_DIR is unset (CI provides the path; local dev
  doesn't have to).
* Plus a discrimination-smoke check: at least one primitive
  produces different majority values across present classes —
  catches the "constant-output regression" failure mode where the
  engine quietly degenerates to a stub.

Calibration tweak: BRANCH_DIVERSITY_LINEAR_MIN dropped from 0.80 to
0.70 to align with the prototype's empirical anchors (CLAUDE-CL ≈
0.55-0.60 adaptive; YOU-sim / CLAUDE-FF scripted recon ≈ 0.75+
linear). Test for the middle band re-pinned at the new boundary.

Per-class value pinning (e.g. HUMAN must emit
inter_command_consistency=bimodal) is intentionally NOT a hard gate
yet — v0.1 thresholds put real human sessions in "variable", and
true bimodal detection (Hartigan dip / two-peak) is registry-flagged
for v0.2. Tighter pinning lands as the corpus grows.

decnet/profiler/behave_shell/_thresholds.py

@@ -54,14 +54,11 @@ INTER_CMD_LLM_HEAVYWEIGHT_MAX: float = 30.00
 MIN_COMMANDS_FOR_FULL_CONFIDENCE: int = 5
 
 # ── cognitive.command_branch_diversity (Step 6) ─────────────────────────────
-# unique_first_tokens / total_commands ratio. Empirical (CLAUDE-FF vs
-# CLAUDE-CL on 2026-05-02): fire-and-forget runs ~10 distinct tools (ratio
-# near 1.0) → linear_playbook; closed-loop runs ~5-6 tools with the same
-# tool re-invoked → adaptive_branching.
-BRANCH_DIVERSITY_LINEAR_MIN: float = 0.80   # >= → linear_playbook
-BRANCH_DIVERSITY_ADAPTIVE_MAX: float = 0.60  # <= → adaptive_branching
-# Between is the ambiguous middle band — bias toward adaptive (the
-# operator is reusing tools).
+# unique_first_tokens / total_commands ratio. Prototype's empirical
+# split (sessions-2026-05-02-* corpus): CLAUDE-CL chasing one finding
+# ≈ 0.55-0.60 (adaptive), HUMAN exploring filesystem ≈ 0.65-0.70
+# (adaptive), YOU-sim / CLAUDE-FF scripted recon ≈ 0.75+ (linear).
+BRANCH_DIVERSITY_LINEAR_MIN: float = 0.70   # >= → linear_playbook
 
 # ── cognitive.feedback_loop_engagement (Step 7) ─────────────────────────────
 # Pearson r threshold for "the operator's pause grew with the volume of