refactor: context_builder 按 slice 拆到 src/llm/slices/ 包

单文件拆分(仅搬迁无逻辑修改):
- common.py    共享类型/头信息/_outcome/_is_stats_available
- form.py      form_slice + _get_form
- h2h.py       h2h_slice + _get_h2h
- stats.py     stats_slice(复用 form._get_form)
- home_away.py home_away_slice + _get_home_away
- standings.py standings_slice
- aggregate.py build_context

context_builder.py 改为纯 re-export 门面,公开签名不变。
同步修复测试 patch 目标(p0_home_away/h2h_perspective/multi_agent_cutoff)
与 regressions 源码断言(读 slices/*.py)。

全量测试 270 通过。

src/llm/slices/h2h.py

@@ -0,0 +1,88 @@
+"""E - 历史交锋切片: 交手史与胜负规律(h2h)。"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from sqlalchemy import select
+from sqlalchemy.orm import selectinload
+
+from src.db.base import AsyncSessionLocal
+from src.db.models import Match
+from src.llm.slices.common import MatchHeader, SliceResult
+
+if TYPE_CHECKING:
+    from sqlalchemy.ext.asyncio import AsyncSession
+
+
+async def h2h_slice(header: MatchHeader, *, limit: int = 8, before=None, db: AsyncSession | None = None) -> SliceResult:
+    """E - 历史交锋切片: 过去数年 + 近期交手数据,提取交手规律。before=match_date 用于回测。
+
+    db: 可选共享 session,避免每个切片独立建连(见 context_builder 模块 docstring)。
+    """
+    if db is not None:
+        h2h = await _get_h2h(db, header.home_team_id, header.away_team_id, before=before, limit=limit)
+    else:
+        async with AsyncSessionLocal() as new_db:
+            h2h = await _get_h2h(new_db, header.home_team_id, header.away_team_id, before=before, limit=limit)
+    lines = [f"── 历史交锋(近 {limit} 次) ──"]
+    n_with_score = 0
+    if h2h:
+        # 从当前主队视角统计:判断当前主队在每场交锋中是主是客
+        current_home_wins = current_home_draws = current_home_losses = 0
+        for hm in h2h:
+            d = hm.match_date.strftime("%Y-%m") if hm.match_date else "?"
+            if hm.home_goals is not None:
+                n_with_score += 1
+                # 判断当前主队当时是主队还是客队
+                if hm.home_team_id == header.home_team_id:
+                    # 当前主队当时是主队
+                    if hm.home_goals > hm.away_goals:
+                        current_home_wins += 1
+                    elif hm.home_goals == hm.away_goals:
+                        current_home_draws += 1
+                    else:
+                        current_home_losses += 1
+                else:
+                    # 当前主队当时是客队(从客队视角看赛果)
+                    if hm.away_goals > hm.home_goals:
+                        current_home_wins += 1
+                    elif hm.away_goals == hm.home_goals:
+                        current_home_draws += 1
+                    else:
+                        current_home_losses += 1
+                lines.append(f"  {d}: {hm.home_team.name} {hm.home_goals}-{hm.away_goals} {hm.away_team.name}")
+            else:
+                lines.append(f"  {d}: {hm.home_team.name} vs {hm.away_team.name} (无比分)")
+        total = current_home_wins + current_home_draws + current_home_losses
+        if total:
+            lines.append(
+                f"  总计 {total} 场(从当前主队 {header.home_name} 视角): "
+                f"{current_home_wins}胜 {current_home_draws}平 {current_home_losses}负"
+            )
+    else:
+        lines.append("  无数据")
+    # has_data 以「有比分的交锋」为准:仅有对阵无比分时不足以支撑分析
+    return SliceResult(text="\n".join(lines), has_data=n_with_score > 0, n_records=n_with_score)
+
+
+async def _get_h2h(db, home_id: int, away_id: int, before, *, limit: int = 5) -> list[Match]:
+    """两队交锋史。需预加载 home_team / away_team(切片输出队名)。"""
+    stmt = (
+        select(Match)
+        .options(
+            selectinload(Match.home_team),
+            selectinload(Match.away_team),
+        )
+        .where(Match.match_status == "finished")
+        .where(Match.home_goals.is_not(None))
+        .where(
+            ((Match.home_team_id == home_id) & (Match.away_team_id == away_id))
+            | ((Match.home_team_id == away_id) & (Match.away_team_id == home_id))
+        )
+        .order_by(Match.match_date.desc())
+        .limit(limit)
+    )
+    if before is not None:
+        stmt = stmt.where(Match.match_date < before)
+    result = await db.execute(stmt)
+    return list(result.scalars().all())