feat(mail): full Gmail+OnlyOffice sync, import, and brain indexing

- bin/mail/sync.go: async Go sync engine (8 workers, paginated Gmail via
  API + OnlyOffice IMAP); Gmail attachments key off body.attachmentId, not
  MIME partId; ICS sidecars Latin-1->UTF-8 normalized (TestICSToMarkdownNormalizesLatin1)
- bin/mail/import: message.json -> markdown; PDFs via pdftotext -layout
  fast path with docling subprocess fallback for the ~5% textless files
- bin/mail/index_mail: fresh-rebuild indexer (repo corpus + mail) avoiding
  ladybug WAL corruption on bulk-insert into indexed DBs; split from import
- bin/kb/index: keep FTS/VECTOR indexes across incremental runs (drop+recreate
  leaves stale backing tables killing the vector index)
- docs: README/PLAN/AGENTS cover the mail pipeline

Result: 17,835 messages -> 28,918 info leafs, FTS+HNSW healthy.

bin/mail/index_mail

@@ -0,0 +1,136 @@
+#!/usr/bin/env python3
+"""mail/index_mail - rebuild the brain with every markdown under var/mail.
+
+Ladybug corrupts its WAL when brand-new leafs are bulk-inserted while the
+FTS/VECTOR indexes already exist, so indexing ALWAYS runs as a fresh rebuild
+(repo corpus + var/mail), matching the proven-safe `kb/index --rebuild` path.
+Conversion and indexing stay separate: conversion can crash in native docling
+and must not leave the brain DB mid-transaction.
+
+  bin/mail/index_mail                 rebuild the index incl. all mail
+  bin/mail/index_mail --dry-run       count without writing
+  bin/mail/index_mail --limit N       cap messages included
+  bin/mail/index_mail --since D       only messages dated >= D (YYYY-MM-DD)
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(ROOT / "bin" / "tools"))
+
+from kblib import DB_PATH, VAR, connect, create_fts_and_vector, init_schema, stats, upsert_leaf  # noqa: E402
+from mdleaves import read_markdown, to_all, walk_markdown  # noqa: E402
+
+
+def msg_date(md: Path) -> str:
+    j = md.parent / "message.json"
+    try:
+        d = json.loads(j.read_text(encoding="utf-8"))
+        return (d.get("receivedDate") or d.get("receivedAt") or "")[:10]
+    except Exception:
+        return ""
+
+
+def mail_leafs(limit: int, since: str, repo: str = "ooMail") -> list[dict]:
+    root = ROOT / "var" / "mail"
+    mds = sorted(root.rglob("message.md"))
+    if since:
+        mds = [m for m in mds if msg_date(m) >= since]
+    if limit:
+        mds = mds[:limit]
+    leafs: list[dict] = []
+    for md in mds:
+        files = [md] + sorted((md.parent / "attachments").glob("*.md"))
+        for f in files:
+            if not f.exists():
+                continue
+            for lf in to_all(read_markdown(f), f, repo=repo):
+                lf["source"] = f"ooMail:{md.parent.name}:{f.name}"
+                lf["how"] = "mail/import"
+                leafs.append(lf)
+    return leafs
+
+
+def main(argv: list[str]) -> int:
+    p = argparse.ArgumentParser(description="rebuild the brain incl. all mail")
+    p.add_argument("--dry-run", action="store_true", help="count only, write nothing")
+    p.add_argument("--limit", type=int, default=0, help="cap messages included")
+    p.add_argument("--since", default="", help="only messages dated >= YYYY-MM-DD")
+    p.add_argument("--json", action="store_true")
+    a = p.parse_args(argv)
+
+    mail = mail_leafs(a.limit, a.since)
+    if a.dry_run:
+        print(f"mail/index_mail: {len(mail)} mail leafs would be indexed")
+        return 0
+
+    # Fresh rebuild: delete DB, index repo corpus + mail, create indexes once
+    # at the end. Never insert into an already-indexed DB (WAL corruption).
+    VAR.mkdir(exist_ok=True)
+    if DB_PATH.exists():
+        DB_PATH.unlink()
+
+    corpus = _load_corpus()
+    leafs = corpus + mail
+
+    db, conn = connect(DB_PATH, read_only=False)
+    init_schema(conn)
+    embed = _embedder()
+    done, total = _index_leafs(conn, leafs, embed)
+    create_fts_and_vector(conn, force=True)
+    s = stats(conn)
+    conn.close()
+    db.close()
+
+    result = {"indexed": done, "corpus_total": total, "mail_leafs": len(mail),
+              **{k: v for k, v in s.items() if k in ("total", "by_root")}}
+    print(json.dumps(result, indent=2) if a.json else
+          f"mail/index_mail: indexed {done}/{total} leafs (mail={len(mail)}); db total {s['total']}")
+    return 0
+
+
+CORPUS_DEFAULTS = ["README.md", "PLAN.md", "AGENTS.md", "docs", "skills"]
+
+
+def _load_corpus() -> list[dict]:
+    files: list[Path] = []
+    for entry in CORPUS_DEFAULTS:
+        p = ROOT / entry
+        if p.is_file():
+            files.append(p)
+        elif p.is_dir():
+            files.extend(walk_markdown(p))
+    leafs: list[dict] = []
+    for path in files:
+        try:
+            leafs.extend(to_all(read_markdown(path), path, repo="eSlider/2dph"))
+        except OSError as e:
+            print(f"mail/index_mail: skip {path}: {e}", file=sys.stderr)
+    return leafs
+
+
+def _index_leafs(conn, leafs: list[dict], embed_fn) -> tuple[int, int]:
+    count = 0
+    for lf in leafs:
+        query = f"{lf['heading']}\n\n{lf['text']}"
+        emb = embed_fn(lf["text"]) if lf["text"] else None
+        upsert_leaf(conn, text=query, root="info", confidence="confirmed",
+                    source=lf["source"], source_rev="mail" if lf.get("how") == "mail/import" else "working-tree",
+                    how=lf.get("how", "kb/index"), loc=lf["source"], type_=lf.get("type", "reference"),
+                    embedding=emb)
+        count += 1
+    return count, len(leafs)
+
+
+def _embedder():
+    from model2vec import StaticModel
+    model = StaticModel.from_pretrained("minishlab/potion-multilingual-128M")
+    return lambda text: model.encode([text])[0].astype(float).tolist()
+
+
+if __name__ == "__main__":
+    sys.exit(main(sys.argv[1:]))