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

README.md

@@ -23,6 +23,26 @@ The mandatory deterministic backbone (validator + budget meter + provenance) is
 - **Step 7 — async file feedback loop (wired).** `run_project(..., verdict_dir=...)` adds the long feedback timescale (target picture §3/§7): an expert/persona drops a verdict file (plain JSON — the raw output layer, §10 R2) into an inbox folder *after* a run, and a separate, later run ingests it — merged into the store *before* the Step-1 fold — so a verdict that landed out of band reaches the next hypothesis. The loop is fully resumable across runs separated in time; no live session is assumed. The system *reads* the folder, the expert/persona *writes* it (§3 role split), so `run_project` deliberately does **not** persist its own captured verdict back (that is the outbox / Step-8 concern). Ingestion is tolerant (a missing folder, foreign or half-written files are skipped, not raised) and **merges** (never replaces), preserving `run_portfolio`'s cross-project store. Reachable from the CLI via `--bundle-dir --verdict-dir`. Load-bearing: `tests/test_step7_async_loop_loadbearing.py` — a verdict dropped after run A must reach run B's prompt (run B uses a *fresh* store, so the transfer is the file loop, not in-memory carryover), with an empty-inbox control proving causality.
 - **Step 8 — gated wiki promotion (wired).** When an expert/persona **approves** an outcome, `verdicts.promote_verdict` lifts it from the raw output layer into the context layer (the OKF bundle) as a `type: verdict` concept file, navigable by the next run's `seed_store_from_bundle` (target picture §3/§6/§7). The **gate** is fail-closed: a verdict whose decision is not an approval raises `PromotionRefused` and writes/links nothing — only human/persona-approved knowledge enters the wiki, never raw agent output (self-contamination). The promotion is provenance-stamped (who approved / which experiment / when — `timestamp` is a required keyword, no wall-clock default). The OKF writer lives in `okf.py` and stays pure stdlib (D7-portable, MAF-free). **R4 = optional + gated:** `promote_verdict` is a public opt-in primitive, deliberately **not** wired into `run_project` (mirrors `write_verdict` — the system reads context; the gate/persona promotes). Two honesty limits: the promoted file is *minimal* (it carries the learning signal only as `description`/body prose — it does not reproduce the hand-authored seed's structured `realization_rate` etc.), and because the verdict id is the learning key, two approvals about the same candidate share a filename (last-write-wins, like `write_verdict`) — the wiki grows one curated file per distinct candidate, not per verdict event. Load-bearing trio (`tests/test_step8_promotion_loadbearing.py`): the gate refuses a non-approved verdict (red if the gate is removed), the approved verdict is navigable (red if `link_in_index` is detached), and the promoted signal stays out of `bundle_context` — reaching a prompt only via the gated ExpeL fold (red if a descriptive index label leaks it into the read-context).
 
+## Offline simulation — the end-to-end proof
+
+The loop is proven end to end **offline**, with no real model. `portfolio_optimiser.simulation`
+drives `run_project` with a **scripted** synthetic chat client (network-free) across two runs
+separated by a promotion, and shows the learning loop close: Run A produces a validated,
+persona-approved verdict carrying a realization marker absent from the bundle; `promote_verdict`
+lifts it into the OKF wiki; a re-seed picks it up; **Run B's hypothesis prompt then carries the
+marker** (an empty-wiki control on Run A proves causality). Run it:
+
+```
+uv run python -m portfolio_optimiser.simulation
+```
+
+This is a deliberate, cost-driven substitution for a real-model run (target picture §11 step 8):
+it proves the plumbing, the deterministic spine, and that the **learning dataflow closes**. It does
+**not** prove that a live LLM would *produce* the proposal or verdict — those are scripted stand-ins
+for the swarm and the expert persona (honesty per §1). The scripted client is MAF-side scaffolding,
+not part of the framework-neutral `shared/` core. Load-bearing: `tests/test_simulation_loadbearing.py`
+goes red the moment promotion is detached (the marker never crosses into Run B).
+
 ## Docs
 
 - [`docs/plan/2026-06-26-maalbilde-agentic-loop.md`](docs/plan/2026-06-26-maalbilde-agentic-loop.md) — target picture: the agentic cost-saving loop + OKF knowledge architecture (north star).