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/README.md

@@ -0,0 +1,74 @@
+# CX AI Advisory Diagnostic
+
+Facilitator's cockpit for the CX AI Advisory diagnostic workshop
+(Mercury Notebook Pattern — see
+[`docs/Mercury_Notebook_Pattern_V1-00.md`](../../docs/Mercury_Notebook_Pattern_V1-00.md)).
+Captures capability scores across **12 competencies** in four dimensions
+(Strategy & Value, Foundations, Delivery, Sustain), ingests the client's
+operational baseline, computes **value-at-stake bounded by capability
+gaps**, and exports a structured engagement record.
+
+Single facilitator, live half-day workshop, 4–8 client participants.
+Not for client self-service, unattended use, or deployment.
+Specification: [`docs/build_spec_v1.md`](docs/build_spec_v1.md).
+
+## Run
+
+```bash
+python -m venv .venv && .venv/bin/pip install -e ".[dev]"   # once
+source .venv/bin/activate
+
+mercury --working-dir .        # the workshop stage (serve from study root)
+jupyter lab                    # analyst view (backstage)
+pytest                         # engine pins
+python scripts/export_report.py     # exports/diagnostic.{html,md}
+```
+
+The notebook is **generated** — edit cell sources in
+`scripts/build_notebook.py`, then:
+
+```bash
+python scripts/build_notebook.py
+jupyter nbconvert --to notebook --execute --inplace notebooks/diagnostic.ipynb
+```
+
+## Layout
+
+```
+diaglib/            # THE study package — all math and contracts
+│   models.py       #   pydantic models: engagement data + config schema
+│   config.py       #   configs/*.yaml loading, base+overlay merge
+│   scoring.py      #   score aggregation, participants, assembly
+│   value_math.py   #   drivers, capping, trapped value, unlock sequence
+│   visuals.py      #   plotly builders (house chrome)
+│   export.py       #   exports/{engagement_id}.json + .csv
+│   staging.py      #   stage/backstage detection (pattern verbatim)
+configs/
+│   base.yaml       #   the instrument: 12 competencies, capping heuristic
+│   contact_center.yaml       # value drivers + unlock costs (🟡 ranges)
+│   financial_services.yaml   # stub — scoring only until drivers added
+notebooks/diagnostic.ipynb    # the deliverable (generated)
+scripts/            # build_notebook.py · export_report.py
+tests/              # hand-checked pins for every engine number
+exports/            # per-engagement JSON/CSV + report sources (gitignored)
+```
+
+## Design decisions (deviations from the build spec, both additive)
+
+- **Unlock moves are tier lifts of the binding set** (`competency_ids`
+  plural). When several foundations tie at the weakest level, lifting one
+  alone honestly unlocks nothing — the set is the move, costs summed,
+  weeks = longest parallel workstream.
+- **"One screen per competency" is a `Now scoring` selector**, not a
+  Next button — Mercury's reactivity model makes stateless buttons
+  awkward, and the selector adds random access for revisits. All score
+  sliders stay live in the sidebar.
+- `requirements.txt` is replaced by `pyproject.toml` per the pattern
+  (`pip install -e .` provisions everything).
+
+## Before using with a real client
+
+The value-driver ranges and unlock costs in `contact_center.yaml` are
+🟡 placeholders (build spec §12): back them with cited public sources or
+"based on N engagements" framing, and tune `cost_per_replacement_default`
+per engagement.