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.

src/everos/core/observability/metrics/histogram.py

@@ -0,0 +1,102 @@
+"""Histogram wrapper around ``prometheus_client.Histogram``."""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from typing import Any
+
+from prometheus_client import Histogram as PromHistogram
+
+from .registry import get_metrics_registry
+
+
+class HistogramBuckets:
+    """Predefined bucket configurations for common workloads."""
+
+    DEFAULT: tuple[float, ...] = (
+        0.005,
+        0.01,
+        0.025,
+        0.05,
+        0.1,
+        0.25,
+        0.5,
+        1.0,
+        2.5,
+        5.0,
+        10.0,
+    )
+    FAST: tuple[float, ...] = (0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, 0.5)
+    API_CALL: tuple[float, ...] = (
+        0.01,
+        0.05,
+        0.1,
+        0.25,
+        0.5,
+        1.0,
+        2.0,
+        5.0,
+        10.0,
+        30.0,
+    )
+    BATCH: tuple[float, ...] = (0.1, 0.5, 1.0, 5.0, 10.0, 30.0, 60.0)
+    DATABASE: tuple[float, ...] = (
+        0.001,
+        0.005,
+        0.01,
+        0.025,
+        0.05,
+        0.1,
+        0.25,
+        0.5,
+        1.0,
+        2.5,
+        5.0,
+    )
+
+
+class Histogram:
+    """Distribution of observed values (latency, sizes)."""
+
+    def __init__(
+        self,
+        name: str,
+        description: str,
+        labelnames: Sequence[str] = (),
+        namespace: str = "",
+        subsystem: str = "",
+        unit: str = "",
+        buckets: Sequence[float] = HistogramBuckets.DEFAULT,
+    ) -> None:
+        self._histogram = PromHistogram(
+            name=name,
+            documentation=description,
+            labelnames=labelnames,
+            namespace=namespace,
+            subsystem=subsystem,
+            unit=unit,
+            buckets=tuple(buckets),
+            registry=get_metrics_registry(),
+        )
+
+    def labels(self, **labels: str) -> LabeledHistogram:
+        return LabeledHistogram(self._histogram.labels(**labels))
+
+    def observe(self, amount: float) -> None:
+        self._histogram.observe(amount)
+
+    def time(self) -> Any:
+        return self._histogram.time()
+
+
+class LabeledHistogram:
+    """Histogram slice with labels applied."""
+
+    def __init__(self, labeled: Any) -> None:
+        self._labeled = labeled
+
+    def observe(self, amount: float) -> None:
+        self._labeled.observe(amount)
+
+    def time(self) -> Any:
+        return self._labeled.time()