feat(sim): offline end-to-end simulation proving the learning loop closes

The primary method proof, offline — a deliberate, cost-driven substitution
for målbilde §11.8's real-model run (the operator runs MAF against no real
model; API for both repos is too costly privately).

`portfolio_optimiser.simulation` drives `run_project` with a scripted
synthetic chat client across two runs separated by a promotion, and shows
the learning loop close end to end:

- ScriptedChatClient subclasses the LAYERED OpenAIChatCompletionClient (not
  bare BaseChatClient — else the always-attached BudgetMiddleware no-ops),
  constructs offline (loopback url + dummy key), role-keys proposer/checker
  replies, and records every prompt into a shared sink.
- simulate_learning_loop: Run A (fresh wiki) -> validated, persona-approved
  verdict carrying a realization marker absent from the bundle -> promote_verdict
  into the OKF wiki -> seed_store_from_bundle re-reads it -> Run B's hypothesis
  prompt carries the marker. An empty-wiki control on Run A proves causality.
- `python -m portfolio_optimiser.simulation` prints an honest trace.

Honesty (§1): this proves the plumbing, the deterministic spine, and that the
learning dataflow closes — NOT that a live LLM would produce the proposal or
verdict (scripted stand-ins). The genuine model-behaviour comparison lives on
the Claude-SDK side (a minimal API run); the scripted client is MAF-side
scaffolding, not part of the framework-neutral shared/ core.

Load-bearing: tests/test_simulation_loadbearing.py goes red when promotion is
detached (the marker never crosses into Run B). Suite 148->149.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MHR8iKxJRxDiDfNw8HZmWE

CLAUDE.md

@@ -88,6 +88,18 @@ Python ≥3.10. MAF (`agent-framework-core` 1.9.0). Pakkehåndtering: `uv`. To b
   dom er navigerbar (RØD når `link_in_index` detaches); promotert signal holdes ute av `bundle_context`
   (RØD når en beskrivende index-label lekker det inn). Index-RMW er ikke-atomisk (enprosess-MVP).
 - **Kostnadsdisiplin:** utvikle primært på lokal profil (gratis); Foundry/Azure (privat tenant finnes) kun til målrettet, minimal verifisering; billigste modeller + små syntetiske data + harde token-tak. Ingen tunge test-kjøringer.
+- **Offline simulering = primært metode-bevis (kostnadsdrevet, erstatter §11.8):** operatøren kjører
+  IKKE MAF mot ekte modell (verken Azure/Foundry eller Ollama — API for begge repoene er for kostbart
+  privat). `portfolio_optimiser.simulation` driver `run_project` med en SKRIPTET syntetisk chat-klient
+  (`ScriptedChatClient` på `OpenAIChatCompletionClient` — IKKE bare `BaseChatClient`, ellers no-op-er
+  `BudgetMiddleware`) over to kjøringer adskilt av en promotering, og viser at læringssløyfa lukkes:
+  Run A's godkjente persona-dom (markør fraværende fra bundelen) → `promote_verdict` → re-seed → Run B's
+  hypotese-prompt bærer markøren (tom-wiki-kontroll på Run A beviser kausalitet). **Ærlighet (§1,
+  ufravikelig):** beviser plumbing + deterministisk ryggrad + at dataflyten lukkes — IKKE at en levende
+  LLM ville produsert forslaget/dommen (skriptede stand-ins). Den genuine modell-atferd-sammenligningen
+  lever på Claude-SDK-siden (minimal API-kjøring). Skriptet klient = MAF-side stillas, IKKE delt
+  (`shared/` forblir framework-nøytralt). Kjøres `uv run python -m portfolio_optimiser.simulation`.
+  Load-bearing: `tests/test_simulation_loadbearing.py` blir RØD når promoteringen detaches.
 - **STATE.md er local-only** (gitignored). Voyage session-state er efemert; STATE.md er kanonisk kontinuitet.
 - Prosess: Voyage-plugin (`/trekbrief → /trekplan → /trekexecute → /trekreview`) per større fase.