| name | simtrain-drill-exercise-coordinator |
| description | Anti-deskilling / keep-warm role: **Drill & exercise coordinator** (Human oversight role (accountability boundary)) — schedules and runs manual-reversion drills and full-scale 'automation-off' exercises. Part of the layer that uses **job and role simulators** to keep humans current, rebuild the learning ladder, and capture tacit knowledge — reusing the world models and simulators built to train the machines. Use this skill when designing or running human upskilling, drills, certification, or fallback-readiness, even if the user only describes the underlying need. Works under a resilience / operations lead. |
Simulation & Keep-Warm — Drill & exercise coordinator
Layer: Anti-deskilling / keep-warm (job & role simulators for humans) · Type: Human oversight role (accountability boundary)
Human supervisor: resilience / operations lead · Reuses: ../embodied-ai-stack/ and ../capability-optimization/ sim infrastructure · Reference: docs/role-simulation-and-keepwarm.md
What this role is
The Drill & exercise coordinator schedules and runs manual-reversion drills and full-scale 'automation-off' exercises. The human-accountable owner of keep-warm cadence; ensures the fallback is actually rehearsed under realistic, degraded conditions. Works with OS 22 (Resilience).
Why this layer exists
Automating routine cases erodes three things: the human fallback bench, tacit / craft judgment, and the learning ladder. Job and role simulators are the most effective countermeasure — and the same world models and simulators built to train the machines double as the environments that keep humans current (one simulation substrate, two students). This role owns the part of that program described above.
When to use this skill
Use it when a task calls for this work: schedules and runs manual-reversion drills and full-scale 'automation-off' exercises. Pair with OS 22 (Resilience), the sector skills' Deskilling watch & keep-warm sections, and the sim infrastructure in _catalogs/embodied-ai-stack/ and _catalogs/capability-optimization/.
Decision rights & accountability
- Owns and is accountable for the keep-warm cadence and that the fallback is genuinely rehearsed.
- Escalates thin benches and failed drills as a safety/continuity risk.
- Cannot let throughput pressure quietly cancel the practice that prevents deskilling.
Fit by domain (where simulators transfer well — and don't)
- High fit: procedural, high-consequence domains (aviation, grid, nuclear, water/chemical, emergency, defense, acute medicine). Sim transfer is well-proven.
- Medium fit: craft and dexterity (manufacturing, construction, surgery) — needs physical or hardware-in-the-loop rigs, not just screens.
- Lower fit: relational, embodied, social-trust work (eldercare, teaching, social work, editorial) — role-play and standardized-patient methods help at the margins, but real human contact still does much of the forming.
Failure modes and safeguards
- Sim-to-real (and sim-to-human) gap — training people to be good at the simulator, not the world. Mitigation: anchor with periodic real practice; measure transfer.
- Encoding the automation's worldview — a sim that bakes in the model's assumptions teaches the model's world. Mitigation: adversarial and out-of-distribution scenarios, real-incident mining.
- Practice cut under throughput pressure — keep-warm is "inefficient" time and gets cancelled first. Mitigation: mandate, schedule, and metrics owned by an accountable human.
Adapting to any nation (context modifiers)
Simulators are cheaper and more scalable than real practice, which makes them a leapfrog opportunity for lower-resource settings; fidelity and access still vary. Re-read through:
- Scale (city-state → federation): whether this role is unified or layered across local/regional/national tiers.
- State capacity (fragile → high-capacity): whether the owning institution exists and can be held to account, or the job is met by markets, households, NGOs, or donors.
- Income level (low → high): affordability of automation and the balance of subsistence vs. wage work.
- Formality (informal → formal): whether the people and assets this role acts on appear in any registry at all.
- Resource & geography: which hazards and dependencies dominate (water-scarce, flood-prone, landlocked, trade-dependent).
- Political system & legitimacy: where the human-accountability boundary actually binds and who may hold power to account.
Operating procedure
- Identify the skill at risk of erosion and the scenario that exercises it (especially the rare, degraded, manual-reversion case).
- Build or reuse the simulator (prefer the sector's existing machine-training world models); set fidelity to the skill.
- Run the drill/curriculum; inject automation-failure scenarios to train oversight.
- Assess competency, log bench-readiness metrics, and escalate gaps to the accountable human.
