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/configs/contact_center.yaml

@@ -0,0 +1,62 @@
+# CX AI Advisory Diagnostic — contact-center industry overlay.
+#
+# Value-driver ranges are 🟡 placeholder benchmarks pending citation work
+# (see docs/build_spec_v1.md §12): before running with a real client, back
+# each range with public sources or "based on N engagements" framing.
+# `value_formula` strings are documentation of the math implemented in
+# diaglib/value_math.py (dispatched on `kind`) — they are carried into
+# exports verbatim, not evaluated.
+
+extends: base
+industry: contact_center
+display_name: "Contact Center"
+
+value_drivers:
+  - id: deflection_lift
+    name: "Deflection / containment improvement"
+    kind: containment_lift
+    baseline_field: current_containment_rate
+    lift_range_pts_low: 0.15
+    lift_range_pts_high: 0.35
+    value_formula: "annual_contact_volume * lift_pts * blended_cost_per_contact"
+    source: "Public benchmarks; virtual agent maturity studies"   # 🟡 placeholder
+
+  - id: aht_reduction
+    name: "Average handle time reduction"
+    kind: aht_reduction
+    baseline_field: average_handle_time_seconds
+    reduction_pct_low: 0.15
+    reduction_pct_high: 0.25
+    value_formula: "annual_contact_volume * (baseline_seconds * reduction_pct) * (blended_cost_per_contact / baseline_seconds)"
+    source: "Agent assist / copilot case data"                    # 🟡 placeholder
+
+  - id: attrition_reduction
+    name: "Attrition reduction"
+    kind: attrition_reduction
+    baseline_field: annual_attrition_rate
+    reduction_pct_low: 0.10
+    reduction_pct_high: 0.20
+    cost_per_replacement_default: 15000    # 🟡 industry-tunable per engagement
+    value_formula: "agent_headcount * (annual_attrition_rate * reduction_pct) * cost_per_replacement"
+    source: "Job quality / copilot studies"                       # 🟡 placeholder
+
+# ── Unlock costs ───────────────────────────────────────────────────────
+# Rough cost/time ranges per one-level lift, per foundational competency.
+# 🟡 estimates — sparse is OK for MVP; a missing lift renders as
+# "cost not configured" in the unlock sequence rather than a guess.
+unlock_costs:
+  data_readiness:
+    lift_1_to_2: {cost_low: 150000, cost_high: 300000, weeks: 8}
+    lift_2_to_3: {cost_low: 300000, cost_high: 600000, weeks: 12}
+    lift_3_to_4: {cost_low: 400000, cost_high: 800000, weeks: 16}
+    lift_4_to_5: {cost_low: 500000, cost_high: 1000000, weeks: 20}
+  process_discovery:
+    lift_1_to_2: {cost_low: 80000, cost_high: 160000, weeks: 6}
+    lift_2_to_3: {cost_low: 120000, cost_high: 250000, weeks: 8}
+    lift_3_to_4: {cost_low: 150000, cost_high: 300000, weeks: 10}
+    lift_4_to_5: {cost_low: 200000, cost_high: 400000, weeks: 12}
+  technical_architecture:
+    lift_1_to_2: {cost_low: 100000, cost_high: 200000, weeks: 8}
+    lift_2_to_3: {cost_low: 200000, cost_high: 400000, weeks: 10}
+    lift_3_to_4: {cost_low: 250000, cost_high: 500000, weeks: 12}
+    lift_4_to_5: {cost_low: 350000, cost_high: 700000, weeks: 16}