feat: add 19 fast commands (no-LLM) + incremental embeddings indexing

Fast commands for git, email, calendar, notes, search, reminders, and
diagnostics — all execute instantly without Claude CLI. Incremental
embeddings indexing in heartbeat (1h cooldown) + inline indexing after
/note, /jurnal, /email save. Fix Ollama URL (localhost → 10.0.20.161),
fix email_process.py KB path (kb/ → memory/kb/).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

src/memory_search.py

@@ -222,6 +222,48 @@ def reindex() -> dict:
     return {"files": files_count, "chunks": chunks_count}
 
 
+def incremental_index() -> dict:
+    """Index only new or modified .md files. Returns {"indexed": N, "chunks": M}."""
+    conn = get_db()
+    try:
+        # Get latest updated_at per file from DB
+        rows = conn.execute(
+            "SELECT file_path, MAX(updated_at) FROM chunks GROUP BY file_path"
+        ).fetchall()
+        db_times = {}
+        for rel_path, updated_at in rows:
+            try:
+                db_times[rel_path] = datetime.fromisoformat(updated_at)
+            except (ValueError, TypeError):
+                pass
+    finally:
+        conn.close()
+
+    files_indexed = 0
+    chunks_total = 0
+    for md_file in sorted(MEMORY_DIR.rglob("*.md")):
+        rel_path = str(md_file.relative_to(MEMORY_DIR))
+        file_mtime = datetime.fromtimestamp(
+            md_file.stat().st_mtime, tz=timezone.utc
+        )
+        db_time = db_times.get(rel_path)
+        if db_time is not None:
+            # Ensure both are offset-aware for comparison
+            if db_time.tzinfo is None:
+                db_time = db_time.replace(tzinfo=timezone.utc)
+            if file_mtime <= db_time:
+                continue
+        try:
+            n = index_file(md_file)
+            files_indexed += 1
+            chunks_total += n
+            log.info("Incremental indexed %s (%d chunks)", md_file.name, n)
+        except Exception as e:
+            log.warning("Failed to index %s: %s", md_file, e)
+
+    return {"indexed": files_indexed, "chunks": chunks_total}
+
+
 def search(query: str, top_k: int = 5) -> list[dict]:
     """Search for query. Returns list of {"file": str, "chunk": str, "score": float}."""
     query_embedding = get_embedding(query)