feat: add master notebook library scaffolding and review tooling

Add CLAUDE.md defining the Palladium master notebook conventions and
Red Panda Approval criteria, plus a review-notebook slash command for
LLM-driven notebook review.

Expand .gitignore to block client/engagement documents and generated
exports, keeping masters client-clean while allowing text/image sources.

Normalize slider widget numeric values from floats to integers in
notebook JSON.

assessments/CX_AI_Diagnostic/tests/conftest.py

@@ -0,0 +1,70 @@
+"""Shared fixtures: the mock workshop scenario every pin is hand-checked
+against (see test_value_math for the arithmetic). Also makes diaglib
+importable without the study venv active (normal setup is
+``pip install -e ".[dev]"`` into the study-local ``.venv/``)."""
+
+import pathlib
+import sys
+from datetime import datetime
+
+import pytest
+
+sys.path.insert(0, str(pathlib.Path(__file__).resolve().parent.parent))
+
+from diaglib import OperationalBaseline, build_scores, load_config  # noqa: E402
+
+CONFIGS = pathlib.Path(__file__).resolve().parent.parent / "configs"
+
+SCORED_AT = datetime(2026, 7, 19, 9, 0)
+
+#: The seed capability profile: data_readiness is the unique weakest
+#: foundation (level 2), so the binding constraint is a single competency.
+SEED_SCORES = {
+    "automation_ai_strategy": (2, "AI driven by board pressure; no written thesis"),
+    "value_realization": (2, "Business cases pre-investment only"),
+    "executive_alignment": (3, "COO owns CX AI; steering meets quarterly"),
+    "process_discovery": (3, "Top 10 call reasons mapped with volumes"),
+    "data_readiness": (2, "KB stale; interaction data siloed in recordings"),
+    "technical_architecture": (3, "CCaaS APIs available; shared integration layer WIP"),
+    "use_case_prioritization": (3, "Scored backlog reviewed monthly"),
+    "delivery_capability": (3, "Two bots in production via SI partner"),
+    "talent_and_skills": (2, "One conversation designer, contractor"),
+    "ai_operations": (2, "Containment eyeballed weekly, no drift alerts"),
+    "change_adoption": (3, "Agent champions for copilot rollout"),
+    "governance_and_risk": (3, "AI policy signed; review board for voice bots"),
+}
+
+
+@pytest.fixture(scope="session")
+def config():
+    return load_config("contact_center", CONFIGS)
+
+
+@pytest.fixture(scope="session")
+def stub_config():
+    return load_config("financial_services", CONFIGS)
+
+
+@pytest.fixture()
+def baseline():
+    return OperationalBaseline(
+        annual_contact_volume=1_200_000,
+        blended_cost_per_contact=6.50,
+        agent_headcount=450,
+        annual_attrition_rate=0.30,
+        current_containment_rate=0.20,
+        average_handle_time_seconds=420,
+        field_confidence={
+            "annual_contact_volume": "known",
+            "blended_cost_per_contact": "estimated",
+            "agent_headcount": "known",
+            "annual_attrition_rate": "estimated",
+            "current_containment_rate": "estimated",
+            "average_handle_time_seconds": "known",
+        },
+    )
+
+
+@pytest.fixture()
+def scores(config):
+    return build_scores(config, SEED_SCORES, scored_at=SCORED_AT)