# Text-based Storyboard — Shift Keeper

> Kit 23 · Shift Keeper · deliverable · designed and built by I Wayan Usada · fictional employer, illustrative figures

## Design premise

Absence is up and every meeting has a favourite fix. Revised after the build so it matches the 2D wall and the 3D week landscape; both read `signals-data.js`.

## Step 1 · Read the wall (all nine, scored)

Each signal is a button. Select it, read it, name its root cause: workload design, management practice or personal circumstance. Each reading that matches the evidence scores 1. **Interventions stay locked until all nine are read** (the earlier build unlocked them after three, contradicting its own "read all nine" rule, and did not score the readings at all although it called them "the designed verdict").

| Signal | Evidence points to |
|---|---|
| Absence curve (Monday clustering) | Workload design |
| Meeting load audit (23 h, 11 without decisions) | Workload design |
| Handover gaps | Workload design |
| Tooling complaints | Workload design |
| Exit interviews (late rosters) | Management practice |
| Rota-swap black market | Management practice |
| Appraisal skips in the highest-absence teams | Management practice |
| The viral yoga poll | Management practice (a vacuum), and a decoy as an intervention |
| Caregiver cluster | Personal circumstance, made worse by policy |

So the evidence is 4 workload, 4 management, 1 personal: eight of nine signals are organisational. The earlier text said "two-thirds … the third family", which did not match its own key.

## Step 2 · Two interventions

Roster redesign, publish-early roster policy and meeting diet score 2 (structural); caregiver adjustment pathway 1 (honest, niche); handover protocol 0 (right fix, wrong size for two picks); resilience training and yoga 0 and count as wellbeing-washing.

## Scoring and pass rule

Nine readings + two picks × 2 = 13. **Pass = 10 or more, at least one structural fix, and no wellbeing-washing pick.**

## 3D week landscape

One warehouse week as terrain (weekdays across, teams deep, height = absence). Each read signal pins onto the feature it explains, coloured by the cause the learner chose. Applying the two picks reshapes the week in an illustrative model: only workload-shaping fixes lower the Monday ridge; wellbeing-washing picks leave a wireframe ghost of the unchanged ridge.