chore: initialize EverOS 1.0.0

md-first memory extraction framework for AI agents.

Markdown is the single source of truth; SQLite holds state and LanceDB
provides the rebuildable vector + BM25 + scalar index. The codebase follows
a single-direction DDD layering (entrypoints -> service -> memory -> infra,
with component / core / config cross-cutting) enforced by import-linter.

Engineering surface:
- Coding conventions in .claude/rules/ (path-scoped) and workflows in
  .claude/skills/ (/commit, /new-branch, /pr).
- GitHub Actions CI runs make lint + test + integration; pre-commit mirrors
  the gates locally (ruff, hygiene hooks, gitlint commit-msg).
- Commit messages follow Conventional Commits, enforced by gitlint.
- make lint also enforces datetime two-zone discipline and OpenAPI drift.

tests/unit/test_memory/test_search/test_shaper.py

@@ -0,0 +1,214 @@
+"""Unit tests for ``memory.search.shaper``.
+
+Tests are pure: no LanceDB, no everalgo, just dataclass-in / DTO-out.
+"""
+
+from __future__ import annotations
+
+import datetime as _dt
+
+from everalgo.types import Candidate, ScoredItem
+
+from everos.memory.search.shaper import (
+    reshape_hybrid_output,
+    shape_agent_case_from_candidate,
+    shape_agent_skill_from_candidate,
+    shape_atomic_fact_from_candidate,
+    shape_episode_from_candidate,
+)
+
+# ── Fixtures ────────────────────────────────────────────────────────────
+
+
+def _ts(year: int = 2026) -> _dt.datetime:
+    return _dt.datetime(year, 1, 1, tzinfo=_dt.UTC)
+
+
+def _episode_candidate(*, id: str = "alice_ep_1", score: float = 0.9) -> Candidate:
+    return Candidate(
+        id=id,
+        score=score,
+        source="vector",
+        metadata={
+            "owner_id": "alice",
+            "owner_type": "user",
+            "session_id": "sess_a",
+            "timestamp": _ts(),
+            "sender_ids": ["alice", "assistant_1"],
+            "subject": "Coffee chat",
+            "summary": "Discussed coffee preferences.",
+            "episode": "Alice said she prefers oat milk.",
+        },
+    )
+
+
+def _agent_case_candidate() -> Candidate:
+    return Candidate(
+        id="agent_a_case_1",
+        score=0.8,
+        source="keyword",
+        metadata={
+            "owner_id": "agent_a",
+            "owner_type": "agent",
+            "session_id": "sess_a",
+            "timestamp": _ts(),
+            "task_intent": "Draft a follow-up email",
+            "approach": "1. summarise...",
+            "quality_score": 0.92,
+            "key_insight": "User prefers brief tone",
+        },
+    )
+
+
+def _agent_skill_candidate() -> Candidate:
+    return Candidate(
+        id="agent_a_skill_1",
+        score=0.7,
+        source="keyword",
+        metadata={
+            "owner_id": "agent_a",
+            "owner_type": "agent",
+            "name": "contract_redline",
+            "description": "Spot risky clauses",
+            "content": "Step 1: ...",
+            "confidence": 0.9,
+            "maturity_score": 0.5,
+            "source_case_ids": ["agent_a_case_1"],
+        },
+    )
+
+
+# ── Episode shaping ─────────────────────────────────────────────────────
+
+
+def test_shape_episode_basic() -> None:
+    item = shape_episode_from_candidate(_episode_candidate())
+    assert item is not None
+    assert item.id == "alice_ep_1"
+    assert item.user_id == "alice"
+    assert item.type == "Conversation"
+    assert item.score == 0.9
+    assert item.atomic_facts == []
+    assert item.sender_ids == ["alice", "assistant_1"]
+
+
+def test_shape_episode_drops_when_owner_type_wrong() -> None:
+    cand = _episode_candidate()
+    cand.metadata["owner_type"] = "agent"
+    assert shape_episode_from_candidate(cand) is None
+
+
+def test_shape_episode_drops_when_timestamp_missing() -> None:
+    cand = _episode_candidate()
+    del cand.metadata["timestamp"]
+    assert shape_episode_from_candidate(cand) is None
+
+
+def test_shape_episode_attaches_facts() -> None:
+    facts = [
+        shape_atomic_fact_from_candidate(
+            Candidate(
+                id="f1",
+                score=0.5,
+                source="other",
+                metadata={"fact": "Alice prefers oat milk"},
+            )
+        )
+    ]
+    item = shape_episode_from_candidate(_episode_candidate(), atomic_facts=facts)
+    assert item is not None
+    assert len(item.atomic_facts) == 1
+    assert item.atomic_facts[0].content == "Alice prefers oat milk"
+
+
+# ── Agent case / skill shaping ──────────────────────────────────────────
+
+
+def test_shape_agent_case_basic() -> None:
+    item = shape_agent_case_from_candidate(_agent_case_candidate())
+    assert item is not None
+    assert item.agent_id == "agent_a"
+    assert item.task_intent == "Draft a follow-up email"
+    assert item.quality_score == 0.92
+    assert item.key_insight == "User prefers brief tone"
+
+
+def test_shape_agent_case_drops_when_owner_type_wrong() -> None:
+    cand = _agent_case_candidate()
+    cand.metadata["owner_type"] = "user"
+    assert shape_agent_case_from_candidate(cand) is None
+
+
+def test_shape_agent_skill_basic() -> None:
+    item = shape_agent_skill_from_candidate(_agent_skill_candidate())
+    assert item is not None
+    assert item.name == "contract_redline"
+    assert item.maturity_score == 0.5
+    assert item.source_case_ids == ["agent_a_case_1"]
+
+
+# ── Hybrid reshape ──────────────────────────────────────────────────────
+
+
+def _scored_episode(eid: str, score: float) -> ScoredItem:
+    return ScoredItem(
+        id=eid,
+        score=score,
+        item_type="episode",
+        metadata={
+            "owner_id": "alice",
+            "owner_type": "user",
+            "session_id": "s1",
+            "timestamp": _ts(),
+            "sender_ids": ["alice"],
+            "subject": "subj",
+            "summary": "summ",
+            "episode": "body",
+        },
+    )
+
+
+def _scored_fact(fid: str, parent: str, score: float) -> ScoredItem:
+    return ScoredItem(
+        id=fid,
+        score=score,
+        item_type="atomic_fact",
+        parent_episode_id=parent,
+        metadata={"fact": f"fact text {fid}"},
+    )
+
+
+def test_reshape_hybrid_nests_facts_under_kept_episode() -> None:
+    scored = [
+        _scored_episode("ep_1", 0.9),
+        _scored_fact("f_1", "ep_1", 0.95),
+        _scored_fact("f_2", "ep_1", 0.85),
+    ]
+    out = reshape_hybrid_output(scored, episode_pool={})
+    assert len(out) == 1
+    assert out[0].id == "ep_1"
+    # Facts sorted descending by score.
+    assert [f.id for f in out[0].atomic_facts] == ["f_1", "f_2"]
+
+
+def test_reshape_hybrid_backfills_evicted_episode_from_pool() -> None:
+    # Episode ep_2 was evicted (only facts present),
+    # but it is in episode_pool — should be restored as a result.
+    scored = [
+        _scored_episode("ep_1", 0.7),
+        _scored_fact("f_a", "ep_2", 0.95),
+    ]
+    pool_episode = _episode_candidate(id="ep_2", score=0.0)
+    out = reshape_hybrid_output(scored, episode_pool={"ep_2": pool_episode})
+    assert len(out) == 2
+    # Output sorted by score descending — ep_2 takes fact's max score (0.95).
+    assert out[0].id == "ep_2"
+    assert out[0].score == 0.95
+    assert len(out[0].atomic_facts) == 1
+    assert out[1].id == "ep_1"
+
+
+def test_reshape_hybrid_drops_orphan_facts_with_no_pool_parent() -> None:
+    scored = [_scored_fact("f_x", "ep_missing", 0.5)]
+    out = reshape_hybrid_output(scored, episode_pool={})
+    assert out == []