Add OpProf campaign: protocols, results, patches, run evidence (P0-P6)

Workload-conditioned operator profiling on patched vLLM 0.24.0 +
Qwen3-30B-A3B/H20. H1b PASS (irregular patterns carry +23-45pp R64
raggedness, 8-45% token-efficiency loss vs rectangular controls);
mechanism decomposition kills the padding narrative and finds the
arrival-uniformization artifact (-12.9%); cross-version churn surface
shows TP2/MNS64 -29.4% across vLLM 0.20->0.24 while the argmax held.
Raw Layer-1 JSONL streams (507 MB) stay on disk, git-ignored; footer
sidecars and metrics are tracked.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

runs/opprof-phase5/test_phase5_analysis.py

@@ -0,0 +1,34 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import numpy as np
+
+import analyze_phase5 as a
+
+
+def main() -> None:
+    adjusted = a.holm({"A1": 0.001, "A2": 0.02, "A3": 0.04, "A4": 0.5})
+    assert adjusted == {"A1": 0.004, "A2": 0.06, "A3": 0.08, "A4": 0.5}
+    assert a.ci(np.arange(100, dtype=np.float64)) == [2.475, 96.52499999999999]
+    runs = [
+        {"blocks": np.asarray([[10.0, 2.0]] * 48)},
+        {"blocks": np.asarray([[20.0, 4.0]] * 48)},
+        {"blocks": np.asarray([[30.0, 6.0]] * 48)},
+    ]
+    draws = a.hierarchical_draws(runs, np.random.default_rng(a.SEED))
+    assert draws.shape == (a.RESAMPLES,)
+    assert np.allclose(draws, 5.0)
+    assert a.point_efficiency(runs) == 5.0
+    idle_blocks = np.asarray([[0.0, 0.0]] + [[10.0, 2.0]] * 47)
+    idle_draws = a.hierarchical_draws(
+        [{"blocks": idle_blocks}], np.random.default_rng(a.SEED)
+    )
+    assert np.all(np.isfinite(idle_draws))
+    assert np.allclose(idle_draws, 5.0)
+    assert a.point_efficiency([{"blocks": idle_blocks}]) == 5.0
+    assert a.two_sided_p(np.asarray([-1.0, 1.0])) == 1.0
+    print("phase5 analysis: PASS")
+
+
+if __name__ == "__main__":
+    main()