Initial implementation of generic Excel-to-DB import tool

Supports .xls and .xlsx, Oracle and PostgreSQL via SQLAlchemy.
Includes CLI (run/inspect/generate-config), YAML config, auto schema
detection, and append/replace/upsert modes.

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

excel_import/config.py

@@ -0,0 +1,48 @@
+from __future__ import annotations
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Literal
+import yaml
+
+
+@dataclass
+class ColumnMapping:
+    source: str
+    target: str
+    dtype: str | None = None  # override detected type, e.g. "VARCHAR(100)", "NUMBER"
+    skip: bool = False
+
+
+@dataclass
+class SheetConfig:
+    sheet: str | int = 0          # sheet name or index
+    header_row: int = 0           # 0-based row index of the header
+    skip_rows: int = 0            # rows to skip before header
+    target_table: str = ""
+    columns: list[ColumnMapping] = field(default_factory=list)
+    mode: Literal["append", "replace", "upsert"] = "append"
+    upsert_keys: list[str] = field(default_factory=list)  # column names for upsert PK
+
+
+@dataclass
+class ImportConfig:
+    dsn: str                        # SQLAlchemy DSN
+    sheets: list[SheetConfig] = field(default_factory=list)
+    default_varchar_length: int = 255
+
+    @classmethod
+    def from_yaml(cls, path: str | Path) -> "ImportConfig":
+        with open(path) as f:
+            raw = yaml.safe_load(f)
+
+        sheets = []
+        for s in raw.get("sheets", []):
+            columns = [ColumnMapping(**c) for c in s.pop("columns", [])]
+            upsert_keys = s.pop("upsert_keys", [])
+            sheets.append(SheetConfig(**s, columns=columns, upsert_keys=upsert_keys))
+
+        return cls(
+            dsn=raw["dsn"],
+            default_varchar_length=raw.get("default_varchar_length", 255),
+            sheets=sheets,
+        )