feat(fase2): wire Step-1 ExpeL retrieval into the hypothesis prompt

Closes maalbilde §5 gap #1 (the one missing "feedback-into-prompt" dataflow)
for the OKF-bundle path. Before, ExpeL was computed AFTER generation into a
discarded SessionContext, so a prior verdict could not influence any hypothesis
(context_providers=0).

- New okf.py: framework-neutral OKF bundle navigation (index + frontmatter +
  cross-links), pure stdlib, no agent_framework/mcp (D7-portable), enforced by
  test_okf_is_maf_free.
- verdicts.py: seed_store_from_bundle + bundle_candidate_features build the
  ExpeL substrate + the pre-hypothesis query key from a bundle.
- run_project(bundle_dir=...): folds the candidate's prior verdicts into the
  generation context BEFORE generate_via_llm; the road path is unchanged.

Load-bearing (maalbilde §7): test_step1_expel_loadbearing proves a prior verdict
reaches the hypothesis prompt and goes RED when the fold is detached (shown via
TDD red->green). The marker is the minted verdict id (content hash) because
docs_dir==bundle_dir lets keyword chunk-stuffing leak the realization rate;
clean layer separation is Fase 2b.

Suite 121->133 passed; mypy + ruff check clean.

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

README.md

@@ -2,7 +2,7 @@
 
 Generic, open framework on **Microsoft Agent Framework (MAF)** for finding cost-savings / efficiency proposals *within* each project of a portfolio of independent projects. Multiple agents collaborate to generate candidate proposals; a mandatory deterministic validator (solver + Monte Carlo) decides the numbers; domain experts review via human-in-the-loop, and the system learns from their verdicts.
 
-> **Status:** Early development (plan phase). Not yet usable.
+> **Status:** Early development. The deterministic backbone is solid; the agentic learning loop is being wired one load-bearing seam at a time — **Step 1 (OKF context → hypothesis) is wired** (see below). Not yet end-to-end usable.
 
 > **Disclaimer — technical framework only.** This project is a *technical framework*. Organizations that deploy it are themselves responsible for ensuring a valid processing purpose and for any required assessments (DPIA, risk/ROS, security reviews, etc.). The framework ships technical affordances (local-only mode, provenance/audit logging, no silent data egress) to *enable* compliant use, but makes no compliance guarantees.
 
@@ -10,6 +10,13 @@ Generic, open framework on **Microsoft Agent Framework (MAF)** for finding cost-
 
 The result will never fit any single customer 100%. The goal is a **~90% genuinely generic core plus clear extension points**, so competent people can configure the last mile per customer. We deliberately do not chase the final 10%.
 
+## Agentic loop — wiring status
+
+The mandatory deterministic backbone (validator + budget meter + provenance) is solid and load-bearing. The agentic learning loop (see the [target picture](docs/plan/2026-06-26-maalbilde-agentic-loop.md) §11) is wired one seam at a time:
+
+- **Step 1 — OKF context → hypothesis (wired).** `run_project(..., bundle_dir=...)` navigates a project's [OKF bundle](shared/examples/bygg-energi-mikro/) and folds the candidate's prior expert verdicts (ExpeL retrieval) into the hypothesis prompt *before* generation — so a prior verdict provably influences the next hypothesis. Guarded by a load-bearing test that fails when the seam is detached (`tests/test_step1_expel_loadbearing.py`).
+- **Steps 3–8** (checker gating, informed refinement, async file feedback, gated wiki promotion) — not yet wired.
+
 ## 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).