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/docs/build_spec_v1.md

@@ -0,0 +1,111 @@
+# CX AI Advisory Diagnostic — Build Spec v1.0
+
+> Source record for this study (received 2026-07-19, owner Robert
+> Helewka). Kept verbatim in intent; §2 layout was adapted to the Mercury
+> Notebook Pattern (study package `diaglib/`, generated notebook,
+> `pyproject.toml` instead of requirements.txt) — see README for the
+> deviations log.
+
+**Purpose:** Facilitator's cockpit for the CX AI Advisory diagnostic
+workshop. Captures capability scores across 12 competencies, ingests
+client operational baseline, computes value-at-stake bounded by
+capability gaps, exports structured data.
+
+**Users:** Single facilitator (Robert) running a live half-day workshop
+with 4–8 client participants.
+
+**NOT for:** Client self-service, unattended use, public deployment, SaaS.
+
+## 1. Stack
+
+Python 3.11+, Jupyter Notebook, Mercury (mljar-mercury), pandas, plotly
+(matplotlib fallback), pydantic, PyYAML. Runs locally. No server, no
+auth, no database.
+
+## 2. Structure
+
+Study package (`diaglib/`) with models / scoring / value_math / visuals /
+export; `configs/` (base + industry overlays); one deliverable notebook;
+gitignored `exports/`.
+
+## 3. Data model
+
+`Engagement` (id `client_slug_YYYY-MM-DD`, client, industry config,
+facilitator, date, participants, baseline, scores, computed value,
+notes) · `Participant` (name, role, function cx|it|ops|finance|other) ·
+`OperationalBaseline` (annual_contact_volume, blended_cost_per_contact,
+agent_headcount, annual_attrition_rate, current_containment_rate,
+average_handle_time_seconds; optional csat_baseline, revenue_at_risk;
+per-field confidence known|estimated|unknown) · `CompetencyScore`
+(competency, dimension, score 1–5, evidence line, scorer role,
+timestamp) · `ValueAtStake` (theoretical annual low/high, realizable
+18-mo low/high, trapped low/high, binding constraints, unlock sequence)
+· `UnlockMove` (competencies, level lift, cost range, weeks, value
+unlocked range).
+
+## 4. Configs
+
+**base.yaml** — 12 competencies across 4 dimensions, each with
+description, failure vignette, level descriptors 1–5; capping heuristic
+(weakest foundational score → realization band): 1: 0.00–0.15,
+2: 0.25–0.40, 3: 0.50–0.65, 4: 0.65–0.85, 5: 0.80–1.00; foundational
+competencies: process_discovery, data_readiness, technical_architecture.
+
+**contact_center.yaml** — value drivers: deflection/containment lift
+(+15–35 pts × volume × cost/contact), AHT reduction (15–25%), attrition
+reduction (10–20%, $15K/replacement default); unlock costs per
+level-lift per foundational competency (sparse OK).
+
+**financial_services.yaml** — stub for MVP.
+
+## 5. Notebook sections
+
+0 Setup (hidden) · 1 Engagement form · 2 Operational baseline with
+confidence flags · 3 Capability scoring, one screen per competency
+(name, description, vignette, level descriptors; score + evidence) ·
+4 Live analysis · 5 Visuals (heatmap, value bands, trapped/realizable
+split, unlock chart) · 6 Export button → JSON + CSV.
+
+## 6. Value math
+
+theoretical = Σ driver ranges → realization band from weakest
+foundational score → realizable_18mo = theoretical × factor × 1.5 →
+trapped = theoretical − realizable run-rate → binding constraints = all
+foundational competencies at the weakest score → unlock sequence: lift
+binding constraints one level, recompute factor, up to 3 moves.
+
+Guard rails: all outputs are ranges; explicit warnings on 🔴-unknown
+inputs; money display ≤ 2 significant figures.
+
+## 7–8. Visuals & exports
+
+Room-facing interactive plotly. Heatmap 4×3 with scores + evidence
+hover; horizontal value bands (theoretical light / realizable dark,
+trapped labeled); split chart; unlock cost-vs-value chart.
+`exports/{engagement_id}.json` (full Engagement — source of truth) +
+`.csv` (one row per competency: engagement_id, client_name, industry,
+workshop_date, competency_id, dimension, score, evidence,
+is_foundational, is_binding_constraint).
+
+## 9. Non-goals (MVP)
+
+No auth · no multi-stakeholder independent scoring (v1.1) · no LLM
+recommendations · no PDF · no history dashboard · no cloud · no SaaS ·
+no client-facing scoring.
+
+## 10. Acceptance
+
+Launch Mercury → contact_center → new engagement; enter client info +
+4–8 participants; six baseline numbers with confidence flags; score all
+12 competencies with evidence; four visuals render live; Export writes
+valid JSON + CSV; (nice-to-have) reload saved JSON; edit YAML ranges →
+recomputed outputs. Done = end-to-end in under 90 minutes with mock
+inputs and sensible output.
+
+## 12. Open questions (Robert)
+
+- Benchmark citations for §4 ranges before first real client (Alan's
+  research subagent offer pending go-ahead).
+- $15K cost-per-replacement default — config-driven, tune per engagement.
+- Multi-stakeholder scoring — v1.1 if workshops routinely score
+  independently; don't build now.