ai(hermes): steer mastery labs to Codex

services/hermes/skills/master-hermes-on-atlas/SKILL.md

@@ -9,6 +9,15 @@ Act as a demanding, practical coach. Teach the deployed system, not a generic
 Hermes installation. Make Brad perform the work and explain the result; do not
 substitute a lecture for a lab.
 
+## Select the training model
+
+Use `openai-codex/gpt-5.4` for assessments, multi-reference labs, incident
+grading, and skill evaluation. If the active provider is the local
+`gpt-oss:20b`, stop before reading the reference files and ask Brad to select
+Codex in Models or start a Codex-backed session. Continue locally only when Brad
+explicitly asks to evaluate the local model itself or requests a lightweight
+single-question review.
+
 ## Start or resume training
 
 1. Read `references/architecture.md` before teaching deployment-specific facts.

services/hermes/skills/master-hermes-on-atlas/agents/openai.yaml

@@ -1,4 +1,4 @@
 interface:
   display_name: "Master Hermes on Atlas"
   short_description: "Practice Hermes safely on the live Atlas stack"
-  default_prompt: "Use $master-hermes-on-atlas to assess me and begin my next hands-on Hermes lab."
+  default_prompt: "Use $master-hermes-on-atlas to assess me and begin my next hands-on Hermes lab; first stop and tell me to select openai-codex/gpt-5.4 if this session is using the local model."

services/hermes/skills/master-hermes-on-atlas/references/curriculum.md

@@ -4,6 +4,9 @@ Complete labs by evidence, not elapsed time. A focused pass can establish
 operational competence in several days; mastery requires repeating real triage
 and recovery work over multiple incidents.
 
+Run the curriculum on `openai-codex/gpt-5.4`. Use the local model only for a
+deliberate comparison lab; it is not the default coach for multi-reference work.
+
 ## Phase 1: orientation and control
 
 ### Lab 1 — Map the system