feat(context): S7 — D7 context seam: OKF navigation + gated ExpeL fold

Step 1 of the loop, built from method-spec §3 alone:
- okf.py (pure stdlib): frontmatter parse, deterministic/tolerant/boundary-checked
  index navigation, bundle_context rendering with type:verdict exclusion
- experience.py: CandidateFeatures from the IR projection, §4.2 id minting,
  structural ranking (0.60·Jaccard + 0.25·type + 0.15·magnitude bucket),
  first-write-wins store, bundle seeding with the realization marker,
  fold-before-generation (empty retrieval → base unchanged)

Load-bearing (§11), each proved RED on detach: verdict-layer exclusion, fold,
seed learning fields, agent-toolkit import guard. 76/76 without an API key;
ruff + mypy --strict clean.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QdSfQdND84oeq2mbjueLTS

tests/test_step1_expel_loadbearing.py

@@ -0,0 +1,248 @@
+"""Step-1 experience fold (ExpeL) — LOAD-BEARING (method-spec §3 Step 1, §11).
+
+The seam this file keeps alive: a prior verdict reaches the next hypothesis prompt
+ONLY via seed → store → structural retrieval → fold. The realization marker test is
+RED when the fold is detached; the empty-store control proves the fold is what
+changes the outcome signal.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any
+
+import pytest
+
+from portfolio_optimiser_claude.experience import (
+    CandidateFeatures,
+    VerdictRecord,
+    VerdictStore,
+    fold_experience,
+    mint_verdict_id,
+    seed_store_from_bundle,
+    similarity,
+)
+from portfolio_optimiser_claude.ir import load_validator_input
+
+BUNDLE = Path(__file__).resolve().parents[1] / "shared" / "examples" / "bygg-energi-mikro"
+
+BASE_PROMPT = "Propose exactly one cost-saving measure for this project."
+
+
+def _features(
+    codes: frozenset[str] = frozenset({"A", "B"}),
+    measure_type: str = "M",
+    claimed: float = 30_000.0,
+) -> CandidateFeatures:
+    return CandidateFeatures(
+        affected_codes=codes, measure_type=measure_type, claimed_saving_nok=claimed
+    )
+
+
+def _record(
+    verdict_id: str,
+    features: CandidateFeatures,
+    decision: str = "approved",
+    rationale: str = "fine",
+) -> VerdictRecord:
+    return VerdictRecord(
+        verdict_id=verdict_id, decision=decision, rationale=rationale, features=features
+    )
+
+
+@pytest.fixture(scope="module")
+def golden_surface() -> dict[str, Any]:
+    raw: dict[str, Any] = json.loads((BUNDLE / "golden.json").read_text(encoding="utf-8"))
+    surface: dict[str, Any] = raw["learning_surface"]
+    return surface
+
+
+@pytest.fixture()
+def seeded_store() -> VerdictStore:
+    store = VerdictStore()
+    seed_store_from_bundle(store, BUNDLE)
+    return store
+
+
+class TestSeedFromBundle:
+    """§3 Step 1: every navigable ``type: verdict`` file becomes a store entry."""
+
+    def test_shared_bundle_seeds_one_verdict(self, seeded_store: VerdictStore) -> None:
+        assert len(seeded_store) == 1
+
+    def test_seed_carries_decision_and_learning_fields(
+        self, seeded_store: VerdictStore, golden_surface: dict[str, Any]
+    ) -> None:
+        # The rationale is the carrier of the learning signal: description plus
+        # the structured learning fields, anchored to the golden learning surface.
+        features = CandidateFeatures.from_proposal(load_validator_input(BUNDLE))
+        (record,) = seeded_store.retrieve(features, k=1)
+        assert record.decision == "approved_with_adjustment"
+        marker = (
+            f"[realiseringsgrad={golden_surface['realization_rate']}; "
+            f"forventet_faktisk_NOK={golden_surface['expected_actual_saving_nok']}]"
+        )
+        assert marker in record.rationale
+        assert "ekspert-dom" in record.rationale  # the description prose survives
+
+    def test_seed_decision_defaults_to_approved(self, tmp_path: Path) -> None:
+        _make_micro_bundle(
+            tmp_path, index_links=["v.md"], verdict="---\ntype: verdict\ntitle: V\n---\nBody."
+        )
+        store = VerdictStore()
+        seed_store_from_bundle(store, tmp_path)
+        features = CandidateFeatures.from_proposal(load_validator_input(tmp_path))
+        (record,) = store.retrieve(features, k=1)
+        assert record.decision == "approved"
+
+    def test_unlinked_verdict_file_is_unreachable(self, tmp_path: Path) -> None:
+        # §6: navigation follows only index cross-links — an unlinked verdict
+        # file must not seed the store.
+        _make_micro_bundle(
+            tmp_path, index_links=[], verdict="---\ntype: verdict\ntitle: V\n---\nBody."
+        )
+        store = VerdictStore()
+        seed_store_from_bundle(store, tmp_path)
+        assert len(store) == 0
+
+
+class TestStructuralRanking:
+    """§3 Step 1: ranking is structural, never textual — frozen weights."""
+
+    def test_identical_features_score_one(self) -> None:
+        assert similarity(_features(), _features()) == pytest.approx(1.0)
+
+    def test_weights_decompose_per_spec(self) -> None:
+        base = _features()
+        # Disjoint code sets, same type, same magnitude bucket → 0.25 + 0.15.
+        disjoint = _features(codes=frozenset({"X"}))
+        assert similarity(base, disjoint) == pytest.approx(0.40)
+        # Same codes, different type, same bucket → 0.60 + 0.15.
+        other_type = _features(measure_type="OTHER")
+        assert similarity(base, other_type) == pytest.approx(0.75)
+        # Same codes, same type, different magnitude bucket → 0.60 + 0.25.
+        other_bucket = _features(claimed=150_000.0)
+        assert similarity(base, other_bucket) == pytest.approx(0.85)
+
+    def test_jaccard_of_two_empty_sets_is_one(self) -> None:
+        a = _features(codes=frozenset())
+        b = _features(codes=frozenset())
+        assert similarity(a, b) == pytest.approx(1.0)
+
+    def test_magnitude_buckets_are_the_frozen_edges(self) -> None:
+        # [0, 1e5), [1e5, 5e5), [5e5, 1e6), [1e6, ∞) — boundary values fall right.
+        for claimed, other, same in (
+            (99_999.0, 5_000.0, True),
+            (99_999.0, 100_000.0, False),
+            (100_000.0, 499_999.0, True),
+            (500_000.0, 999_999.0, True),
+            (999_999.0, 1_000_000.0, False),
+            (1_000_000.0, 9e9, True),
+        ):
+            got = similarity(_features(claimed=claimed), _features(claimed=other))
+            assert got == pytest.approx(1.0 if same else 0.85), (claimed, other)
+
+    def test_surface_text_never_contributes(self) -> None:
+        # Two records differing ONLY in rationale text rank identically —
+        # deterministic tie-break is by verdict id ascending.
+        store = VerdictStore()
+        store.add(_record("bbb", _features(), rationale="LED retrofit — perfect match text"))
+        store.add(_record("aaa", _features(), rationale="unrelated prose"))
+        ranked = store.retrieve(_features(), k=2)
+        assert [r.verdict_id for r in ranked] == ["aaa", "bbb"]
+
+    def test_top_k_by_similarity(self) -> None:
+        store = VerdictStore()
+        store.add(_record("far", _features(codes=frozenset({"X"}), measure_type="OTHER")))
+        store.add(_record("near", _features()))
+        assert [r.verdict_id for r in store.retrieve(_features(), k=1)] == ["near"]
+
+    def test_k_must_be_positive(self) -> None:
+        store = VerdictStore()
+        with pytest.raises(ValueError):
+            store.retrieve(_features(), k=0)
+
+
+class TestStoreSemantics:
+    """§4.2: the in-memory store is FIRST-write-wins per id (idempotent merges)."""
+
+    def test_first_write_wins_per_id(self) -> None:
+        store = VerdictStore()
+        store.add(_record("same-id", _features(), rationale="first"))
+        store.add(_record("same-id", _features(), rationale="second"))
+        assert len(store) == 1
+        (record,) = store.retrieve(_features(), k=1)
+        assert record.rationale == "first"
+
+
+class TestIdMinting:
+    """§4.2: the id keys on the candidate measure, not the verdict event."""
+
+    def test_structurally_identical_candidates_share_an_id(self) -> None:
+        a = _features(codes=frozenset({"B", "A"}))
+        b = _features(codes=frozenset({"A", "B"}))
+        minted = mint_verdict_id(a)
+        assert minted == mint_verdict_id(b)
+        assert len(minted) == 16
+        assert set(minted) <= set("0123456789abcdef")
+
+    def test_number_formatting_participates_in_the_hash(self) -> None:
+        # 30000 vs 30000.0 differ — the reason loaded ids are kept verbatim.
+        as_int = _features(claimed=30_000)
+        as_float = _features(claimed=30_000.0)
+        assert mint_verdict_id(as_int) != mint_verdict_id(as_float)
+
+
+class TestExperienceFold:
+    """LOAD-BEARING (§11): the realization marker reaches the prompt via the fold."""
+
+    def test_realization_marker_reaches_the_hypothesis_prompt(
+        self, seeded_store: VerdictStore, golden_surface: dict[str, Any]
+    ) -> None:
+        # Seed → store → structural retrieval → fold: the expert's realization
+        # correction must reach the next hypothesis prompt. RED when the fold is
+        # detached (nothing prepended) or seeding drops the learning fields.
+        features = CandidateFeatures.from_proposal(load_validator_input(BUNDLE))
+        prompt = fold_experience(seeded_store, features, BASE_PROMPT, k=3)
+        assert f"realiseringsgrad={golden_surface['realization_rate']}" in prompt
+        assert prompt.endswith(BASE_PROMPT)  # prepended, never replacing the task
+
+    def test_fold_lines_carry_id_decision_and_rationale(self, seeded_store: VerdictStore) -> None:
+        features = CandidateFeatures.from_proposal(load_validator_input(BUNDLE))
+        (record,) = seeded_store.retrieve(features, k=1)
+        prompt = fold_experience(seeded_store, features, BASE_PROMPT, k=1)
+        for part in (record.verdict_id, record.decision, record.rationale):
+            assert part in prompt
+
+    def test_empty_store_control_changes_the_outcome_signal(
+        self, seeded_store: VerdictStore, golden_surface: dict[str, Any]
+    ) -> None:
+        # Control (§11): with an empty store the prompt is the unchanged base —
+        # no marker, no verdict lines. The fold is what makes the difference.
+        features = CandidateFeatures.from_proposal(load_validator_input(BUNDLE))
+        unfolded = fold_experience(VerdictStore(), features, BASE_PROMPT, k=3)
+        folded = fold_experience(seeded_store, features, BASE_PROMPT, k=3)
+        assert unfolded == BASE_PROMPT
+        assert f"realiseringsgrad={golden_surface['realization_rate']}" not in unfolded
+        assert folded != unfolded
+
+
+def _make_micro_bundle(tmp_path: Path, index_links: list[str], verdict: str) -> None:
+    links = " ".join(f"[{name}]({name})" for name in index_links)
+    (tmp_path / "index.md").write_text(
+        f"---\ntype: index\ntitle: Micro\n---\nSummary. {links}\n", encoding="utf-8"
+    )
+    (tmp_path / "v.md").write_text(verdict, encoding="utf-8")
+    (tmp_path / "validator-input.json").write_text(
+        json.dumps(
+            {
+                "project_id": "P1",
+                "measure": "M",
+                "affected_items": [{"code": "A", "quantity": 10, "unit_cost": 100.0}],
+                "claimed_saving_nok": 300,
+                "assumptions": {},
+            }
+        ),
+        encoding="utf-8",
+    )