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.

tests/nbcheck.py

@@ -0,0 +1,117 @@
+"""Structural checks for every master notebook in the library.
+
+Kernel-free: everything here reads the committed ``.ipynb`` JSON with
+nbformat — no study venv, no execution. Content and engine pins stay in
+each master's own ``tests/`` (run in its venv); this layer guards only
+STRUCTURE: the notebook parses, was executed cleanly top-to-bottom, and
+(for notebook-first masters) carries the tagged-cell taxonomy the
+Assessment Pattern requires.
+
+Classification is explicit and non-silent: every notebook on disk must be
+listed in exactly one of NOTEBOOK_FIRST or GRANDFATHERED (a completeness
+test enforces it), so a new master cannot dodge the suite, and every
+exemption carries its reason — grandfathered notebooks run the structural
+tier and skip the notebook-first tier with that reason shown by
+``pytest -rs``.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import nbformat
+
+REPO = Path(__file__).resolve().parent.parent
+
+# Masters built on the notebook-first content model (Assessment Pattern):
+# full check set, including the tagged-cell taxonomy.
+NOTEBOOK_FIRST = {
+    "assessments/CX_Discovery_Workshop/notebooks/cx_discovery.ipynb",
+}
+
+# Structural tier only, each with its recorded reason (see CLAUDE.md,
+# Known liabilities). Redesigning one of these to notebook-first means
+# moving it up to NOTEBOOK_FIRST — never deleting it from here silently.
+GRANDFATHERED = {
+    "assessments/CX_AI_Diagnostic/notebooks/diagnostic.ipynb":
+        "generated notebook; pre-dates the notebook-first model (redesign pending)",
+    "studies/202512_TEI_Genesys_CX_Cloud/notebooks/business_case.ipynb":
+        "pre-tag TEI master (redesign pending)",
+    "studies/202602_TEI_Amazon_Connect/notebooks/business_case.ipynb":
+        "pre-tag TEI master (redesign pending)",
+    "studies/202607_CTM_GenesysCX/notebooks/ctm_business_case_corrected.ipynb":
+        "real-client study, frozen (see CLAUDE.md known liabilities)",
+    "studies/202607_CTM_GenesysCX/notebooks/ctm_business_case_no_current_state.ipynb":
+        "real-client study, frozen (see CLAUDE.md known liabilities)",
+    "studies/202607_CTM_GenesysCX/notebooks/ctm_business_case_virtual_agents.ipynb":
+        "real-client study, frozen (see CLAUDE.md known liabilities)",
+    "studies/202607_CTM_GenesysCX/notebooks/ctm_migration_wfm.ipynb":
+        "real-client study, frozen (see CLAUDE.md known liabilities)",
+    "studies/202607_CTM_GenesysCX/notebooks/ctm_token_calculator.ipynb":
+        "real-client study, frozen (see CLAUDE.md known liabilities)",
+    "template/MercuryNotebook/notebooks/business_case.ipynb":
+        "template encodes the py-engine model (rework pending)",
+}
+
+ALL_CLASSIFIED = sorted(NOTEBOOK_FIRST | set(GRANDFATHERED))
+
+# Tags that may appear at most once per notebook.
+UNIQUE_TAGS = ("topic-bank", "engagement-data", "gate", "data-appendix")
+
+
+def discover() -> list[str]:
+    """Every notebook on disk under the master roots (repo-relative)."""
+    found: list[str] = []
+    for base in ("studies", "assessments", "template"):
+        root = REPO / base
+        if not root.is_dir():
+            continue
+        for p in root.rglob("*.ipynb"):
+            if ".ipynb_checkpoints" in p.parts or ".venv" in p.parts:
+                continue
+            if p.parent.name != "notebooks":
+                continue
+            found.append(p.relative_to(REPO).as_posix())
+    return sorted(found)
+
+
+def load(rel: str) -> Any:
+    return nbformat.read(REPO / rel, as_version=4)
+
+
+def cell_tags(cell: Any) -> list[str]:
+    return list(cell.metadata.get("tags", []))
+
+
+def cells_tagged(nb: Any, tag: str) -> list[int]:
+    return [i for i, c in enumerate(nb.cells) if tag in cell_tags(c)]
+
+
+def execution_problem(nb: Any) -> str | None:
+    """None if executed cleanly top-to-bottom, else what's wrong.
+
+    Non-empty code cells must carry integer execution counts, strictly
+    increasing 1..N in document order (proof of one clean linear run);
+    empty cells may be unexecuted (``None``).
+    """
+    prev = 0
+    for i, c in enumerate(nb.cells):
+        if c.cell_type != "code" or not c.source.strip():
+            continue
+        ec = c.get("execution_count")
+        if not isinstance(ec, int):
+            return f"cell {i} has no execution count (notebook not executed?)"
+        if ec != prev + 1:
+            return f"cell {i} has execution count {ec}, expected {prev + 1}"
+        prev = ec
+    return None
+
+
+def error_outputs(nb: Any) -> list[int]:
+    return [
+        i
+        for i, c in enumerate(nb.cells)
+        if c.cell_type == "code"
+        and any(o.get("output_type") == "error" for o in c.get("outputs", []))
+    ]