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/diaglib/scoring.py

@@ -0,0 +1,133 @@
+"""Score aggregation, gap analysis, and engagement assembly.
+
+Everything the notebook needs between raw widget values and the engine's
+value math lives here — the notebook itself computes nothing.
+"""
+
+from __future__ import annotations
+
+import re
+from datetime import date, datetime
+from typing import get_args
+
+from .models import (
+    CompetencyScore,
+    DiagnosticConfig,
+    Engagement,
+    Function,
+    OperationalBaseline,
+    Participant,
+    ValueAtStake,
+)
+
+FUNCTIONS: tuple[str, ...] = get_args(Function)
+
+
+# ── Engagement identity ──────────────────────────────────────────────
+
+
+def make_engagement_id(client_name: str, workshop_date: date) -> str:
+    """``"Acme Corp!" + 2026-07-19 -> "acme_corp_2026-07-19"``."""
+    slug = re.sub(r"[^a-z0-9]+", "_", client_name.lower()).strip("_") or "client"
+    return f"{slug}_{workshop_date.isoformat()}"
+
+
+def parse_participants(text: str) -> list[Participant]:
+    """Parse ``"Name | Role | function; Name | Role | function"``.
+
+    Forgiving by design — the facilitator types this live. Missing parts
+    default (role empty, function ``other``); unknown functions map to
+    ``other`` rather than erroring mid-workshop.
+    """
+    participants: list[Participant] = []
+    for entry in text.split(";"):
+        parts = [p.strip() for p in entry.split("|")]
+        if not parts or not parts[0]:
+            continue
+        function = parts[2].lower() if len(parts) > 2 else "other"
+        participants.append(Participant(
+            name=parts[0],
+            role=parts[1] if len(parts) > 1 else "",
+            function=function if function in FUNCTIONS else "other",
+        ))
+    return participants
+
+
+# ── Scores ───────────────────────────────────────────────────────────
+
+
+def build_scores(config: DiagnosticConfig, raw: dict[str, tuple[int, str]],
+                 scored_at: datetime,
+                 scorer_role: str = "facilitator") -> list[CompetencyScore]:
+    """``raw[competency_id] = (score, evidence)`` → validated scores, config order."""
+    missing = [c.id for c in config.competencies if c.id not in raw]
+    if missing:
+        raise ValueError(f"unscored competencies: {missing}")
+    return [
+        CompetencyScore(
+            competency_id=c.id, dimension=c.dimension,
+            score=raw[c.id][0], evidence=raw[c.id][1].strip(),
+            scorer_role=scorer_role, scored_at=scored_at,
+        )
+        for c in config.competencies
+    ]
+
+
+def dimension_rollup(config: DiagnosticConfig,
+                     scores: list[CompetencyScore]) -> list[tuple[str, str, float]]:
+    """``(dimension_id, dimension_name, mean score)`` per dimension, config order."""
+    by_dim: dict[str, list[int]] = {d.id: [] for d in config.dimensions}
+    for s in scores:
+        by_dim[s.dimension].append(s.score)
+    return [(d.id, d.name, sum(v) / len(v))
+            for d in config.dimensions if (v := by_dim[d.id])]
+
+
+def evidence_coverage(scores: list[CompetencyScore]) -> tuple[int, int]:
+    """``(scores with evidence captured, total scores)``."""
+    return sum(1 for s in scores if s.evidence), len(scores)
+
+
+def heatmap_grid(config: DiagnosticConfig, scores: list[CompetencyScore]) -> dict:
+    """Pure data for the 4×3 heatmap — rows are dimensions, three
+    competencies per row in config order. Returned as plain lists so the
+    visuals layer holds no logic."""
+    by_id = {s.competency_id: s for s in scores}
+    rows, z, text, hover = [], [], [], []
+    for d in config.dimensions:
+        comps = [c for c in config.competencies if c.dimension == d.id]
+        rows.append(d.name)
+        z.append([by_id[c.id].score for c in comps])
+        text.append([f"{by_id[c.id].score}<br>{c.name}" for c in comps])
+        hover.append([
+            f"<b>{c.name}</b> — level {by_id[c.id].score}<br>"
+            f"{c.level_descriptors[by_id[c.id].score]}<br>"
+            f"<i>{by_id[c.id].evidence or 'no evidence captured'}</i>"
+            for c in comps
+        ])
+    return {"rows": rows, "z": z, "text": text, "hover": hover,
+            "cols": ["", "", ""]}
+
+
+# ── Assembly ─────────────────────────────────────────────────────────
+
+
+def build_engagement(*, config: DiagnosticConfig, client_name: str,
+                     facilitator: str, workshop_date: date,
+                     participants: list[Participant],
+                     baseline: OperationalBaseline,
+                     scores: list[CompetencyScore],
+                     computed_value: ValueAtStake | None,
+                     notes: str = "") -> Engagement:
+    return Engagement(
+        engagement_id=make_engagement_id(client_name, workshop_date),
+        client_name=client_name.strip() or "Unnamed client",
+        industry_config=config.industry,
+        facilitator=facilitator.strip(),
+        workshop_date=workshop_date,
+        participants=participants,
+        operational_baseline=baseline,
+        scores=scores,
+        computed_value=computed_value,
+        notes=notes,
+    )