One call-time resolver (env PORTFOLIO_SHARED_ROOT, default the in-repo
shared/) consumed by both MAF-side readers of the shared core:
persona._example_path() (the _EXAMPLE_PATH monkeypatch seam is kept) and
simulation._default_bundle_dir() (replaces the _BUNDLE_DIR module global).
De-risks the S4 extraction: re-pointing the commons becomes an env var,
not a code change. Override test proves the marker follows a tmp copy of
the whole shared tree; both detach points proven RED. Suite 155->157.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01AaQCFnfsh3tfq1VfzdJpoi
The expert reviewer was only a hardcoded verdict_input dict inside the offline
simulation. Build it as the real, shared artifact target picture §8 calls for:
shared/skills/expert-reviewer/ — a SKILL.md persona prompt (energy-advisor / M&V
role + the realization-gap methodology the validator cannot compute) plus a
canonical references/example-verdict.json. shared/ stays pure data; the MAF side
reads it via portfolio_optimiser.persona.load_persona_example (call-time,
fail-fast) and the Claude-SDK sibling reads the same JSON with its own loader.
This de-stubs the simulation: its persona judgement (decision + rationale + traced
marker) is now sourced from the artifact at call time, not an inline literal — so
the shared persona is genuinely consumed and cannot rot silently. decision is
binary (approved/rejected, the FeedbackContract the run path accepts);
approved_with_adjustment is rejected there and lives only in the bundle seed
frontmatter + the promotion gate, so the realization correction is carried in the
rationale prose.
Load-bearing trio (tests/test_persona_skill_loadbearing.py), each proven RED on its
own detach: structure + framework-neutrality, the example is valid pipeline input
(incl. FeedbackContract, on a throwaway copy), and the simulation's marker follows
the artifact file. Suite 149->152.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01MHR8iKxJRxDiDfNw8HZmWE
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
