palladium/assessments/CX_Discovery_Workshop

CX Exploration & Discovery Workshop

An Assessment — a reusable workshop master (see docs/Assessment_Pattern_V1-00.md) — and a live facilitation aid for a CX discovery session, built on the Mercury Notebook Deliverable Pattern. Unlike the TEI business-case studies, this deliverable computes no dollars — its "numbers" are topic status and progress. The Mercury stage is the visual you share on a call or in a workshop while you work through the questions; the question script and captured notes live backstage and export as LLM input for the survey write-up or a downstream business case.

What the client sees (the stage)

A calm topic board: each topic, its one-line scope, a status glyph, and — for the topic under discussion — a live sub-topic checklist, above a progress bar ("3/8 topics complete"). No wall of questions: you ask those.

You drive it from the sidebar — a status selector and a notes box per topic, plus a checkbox per sub-topic. As the conversation moves you set a topic In progress, tick sub-topics as you cover them, mark it Complete (or Skipped), and jot answers. Every change re-renders the board and progress bar (Mercury re-runs the cells below the widgets).

What you work from (backstage)

JupyterLab and the exports carry the facilitator question script (all 95 prompts, grouped by topic → sub-topic) and the captured-session appendix (status + notes as a markdown table and one JSON block). Neither shows on the Mercury stage. The 8 topics / 26 sub-topics / ~110-minute agenda are the structured form of the source survey.

Where the content lives: in the notebook

Two tagged cells of notebooks/cx_discovery.ipynb carry everything a consultant edits — in Jupyter, never in a .py file:

• topic-bank — the workshop content: every topic, sub-topic, and facilitator prompt. It is the study's verbatim anchor: wording tracks the source survey docs/cx_discovery_survey.md (the original cxxm.md), and the key slugs are stable identities the sidebar widgets, captured notes, and JSON export all key off — never renumber or rename them casually. The cell's own comment block carries the full editing rules.
• engagement-data — the client facts (client, workshop date, facilitator, attendees). Placeholders in this master; filled in the engagement copy. The values flow to the stage board header, the export preamble, and the JSON appendix meta.

discoverylib/ holds code only: the Topic/SubTopic schema, the status vocabulary, and the session engine — every engine function takes the bank as its first argument. The ~42 sidebar widgets are built by a runtime loop over TOPICS, so board, checklist, script, gate, and export all pick up a content edit automatically. The test suite reads the tagged cells straight out of the notebook (no kernel) and pins content shape, so pytest guards the bank exactly as shipped.

Layout

notebooks/cx_discovery.ipynb   # THE deliverable — content + data + presentation
                               #   tagged cells: topic-bank · engagement-data ·
                               #   presentation ×4 · gate · data-appendix
                               #   stage shows ONLY: title · progress line · board
discoverylib/           # the engine — code only, no content
  session.py            #   Topic/SubTopic schema, status vocabulary, progress, export payload
  staging.py            #   stage/backstage detection (+ backstage_md for the appendix)
scripts/export_report.py       # execute once → exports/*.html + LLM-ready *.md
tests/                  # content + engagement pins (read from the notebook), engine pins, staging
docs/cx_discovery_survey.md    # source survey (the original cxxm.md)
exports/                # generated report sources (never committed)

Run

python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

mercury --working-dir .                # serve the stage (share this screen)
jupyter lab                            # analyst / facilitator view
pytest                                 # content + engagement + engine + staging pins
jupyter nbconvert --to notebook --execute --inplace notebooks/cx_discovery.ipynb   # gate
python scripts/export_report.py        # exports/*.html + *.md for the LLM handoff

The .md export opens with a generated preamble (what the document is, the engagement line, how to read it) and ends with the Session state (JSON) block — the machine source of truth for the write-up. Presentation cells (setup, the facilitator run-book, widgets, board) are stripped from it; the HTML export keeps the full presentation for human review.

Extending

New or reshaped discovery content is an edit to the notebook's topic-bank cell in JupyterLab, then:

1. Re-run the notebook — the in-notebook gate recounts the bank (topics / sub-topics / prompts / key order / agenda minutes); update its pins if the change is deliberate.
2. pytest — tests/test_topics.py pins the same shape from outside the kernel; re-pin the counts there too.

Add a topic and the sidebar controls, board, checklist, script, gate, and export all pick it up — they're all derived from TOPICS at runtime. New logic (not content) goes in discoverylib/session.py with pins in tests/test_session.py.

Running this for a client? Don't fill client data into this master — follow the copy-out checklist in docs/Assessment_Pattern_V1-00.md: copy the directory out of Palladium, stamp it YYYYMM_Client_CX_Discovery, fill the engagement-data cell there, and treat the copy as confidential.