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_infra/test_lancedb/test_repos/test_agent_skill.py

@@ -0,0 +1,153 @@
+"""Tests for :class:`everos.infra.persistence.lancedb._AgentSkillRepo`.
+
+Real LanceDB under ``tmp_path`` (no mocks) — these tests exercise the
+SQL ``where`` predicate, cosine ``distance_type`` ranking, and
+``_distance`` stripping that the repo owns. Strategy-level routing
+across these methods is covered separately in
+``tests/unit/test_memory/test_strategies/test_extract_agent_skill.py``.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import pytest
+
+from everos.infra.persistence.lancedb import (
+    AgentSkill as LanceAgentSkill,
+)
+from everos.infra.persistence.lancedb import (
+    agent_skill_repo,
+    lancedb_manager,
+)
+
+
+def _skill_row(
+    *,
+    name: str,
+    owner_id: str,
+    cluster_id: str,
+    vector: list[float],
+) -> LanceAgentSkill:
+    """Minimal AgentSkill row sufficient to land in LanceDB for repo tests."""
+    return LanceAgentSkill(
+        id=f"{owner_id}_{name}",
+        owner_id=owner_id,
+        owner_type="agent",
+        name=name,
+        description=f"desc {name}",
+        description_tokens=f"desc {name}",
+        content=f"body of {name}",
+        content_tokens=f"body of {name}",
+        confidence=0.7,
+        maturity_score=0.6,
+        source_case_ids=[],
+        cluster_id=cluster_id,
+        md_path=f"agents/{owner_id}/skills/{name}/SKILL.md",
+        content_sha256="x" * 64,
+        vector=vector,
+    )
+
+
+@pytest.fixture
+async def _real_lancedb(tmp_path: Path, monkeypatch: pytest.MonkeyPatch):
+    """Spin up a clean LanceDB rooted under ``tmp_path`` for one test."""
+    monkeypatch.setenv("EVEROS_MEMORY__ROOT", str(tmp_path))
+    lancedb_manager._conn = None
+    lancedb_manager._tables.clear()
+    yield
+    await lancedb_manager.dispose_connection()
+
+
+async def test_count_in_cluster_isolates_owner_and_cluster(
+    _real_lancedb: None,
+) -> None:
+    """``count_in_cluster`` returns only rows matching both filters."""
+    await agent_skill_repo.upsert(
+        [
+            _skill_row(name="s1", owner_id="a", cluster_id="cl_x", vector=[0.1] * 1024),
+            _skill_row(name="s2", owner_id="a", cluster_id="cl_x", vector=[0.2] * 1024),
+            _skill_row(
+                name="other_cluster",
+                owner_id="a",
+                cluster_id="cl_y",
+                vector=[0.3] * 1024,
+            ),
+            _skill_row(
+                name="other_owner",
+                owner_id="b",
+                cluster_id="cl_x",
+                vector=[0.4] * 1024,
+            ),
+        ]
+    )
+
+    assert (
+        await agent_skill_repo.count_in_cluster(owner_id="a", cluster_id="cl_x")
+    ) == 2
+
+
+async def test_find_in_cluster_returns_typed_rows_no_ranking(
+    _real_lancedb: None,
+) -> None:
+    """Scalar fetch within one cluster; capped at ``limit`` regardless of order."""
+    await agent_skill_repo.upsert(
+        [
+            _skill_row(name="s1", owner_id="a", cluster_id="cl_x", vector=[0.1] * 1024),
+            _skill_row(name="s2", owner_id="a", cluster_id="cl_x", vector=[0.2] * 1024),
+            _skill_row(name="s3", owner_id="a", cluster_id="cl_x", vector=[0.3] * 1024),
+            _skill_row(
+                name="other_cluster",
+                owner_id="a",
+                cluster_id="cl_y",
+                vector=[0.4] * 1024,
+            ),
+        ]
+    )
+
+    got = await agent_skill_repo.find_in_cluster(
+        owner_id="a", cluster_id="cl_x", limit=2
+    )
+    assert len(got) == 2
+    assert {s.name for s in got}.issubset({"s1", "s2", "s3"})
+    assert all(s.owner_id == "a" and s.cluster_id == "cl_x" for s in got)
+
+
+async def test_find_topk_relevant_in_cluster_ranks_by_cosine(
+    _real_lancedb: None,
+) -> None:
+    """LanceDB native ``nearest_to + distance_type('cosine')`` ordering."""
+    near = [1.0] + [0.0] * 1023
+    far = [0.0] * 1023 + [1.0]
+    medium = [0.7, 0.7] + [0.0] * 1022
+    await agent_skill_repo.upsert(
+        [
+            _skill_row(name="near", owner_id="a", cluster_id="cl_x", vector=near),
+            _skill_row(name="far", owner_id="a", cluster_id="cl_x", vector=far),
+            _skill_row(name="medium", owner_id="a", cluster_id="cl_x", vector=medium),
+            # Different cluster — must not leak.
+            _skill_row(name="other", owner_id="a", cluster_id="cl_y", vector=near),
+            # Different owner — must not leak either.
+            _skill_row(name="near", owner_id="b", cluster_id="cl_x", vector=near),
+        ]
+    )
+
+    got = await agent_skill_repo.find_topk_relevant_in_cluster(
+        owner_id="a", cluster_id="cl_x", query_vector=near, top_k=2
+    )
+    assert [s.name for s in got] == ["near", "medium"]
+
+
+async def test_find_topk_relevant_in_cluster_raises_on_empty_vector(
+    _real_lancedb: None,
+) -> None:
+    """Empty ``query_vector`` is a caller-side error — the repo refuses."""
+    await agent_skill_repo.upsert(
+        [
+            _skill_row(name="s1", owner_id="a", cluster_id="cl_x", vector=[0.1] * 1024),
+        ]
+    )
+    with pytest.raises(ValueError, match="query_vector must be non-empty"):
+        await agent_skill_repo.find_topk_relevant_in_cluster(
+            owner_id="a", cluster_id="cl_x", query_vector=[], top_k=2
+        )