fix(minimax): wire YAML / CLI config through to backend

PR #26 added a MiniMax chat backend but left three loose ends that
silently dropped any YAML / CLI configuration of minimax_* keys: only
the environment-variable path worked.

- skillopt/config.py: add 6 model.minimax_* entries to _FLATTEN_MAP so
  the keys declared in configs/_base_/default.yaml actually survive
  flatten_config() (mirroring the existing model.qwen_chat_* block).
- skillopt/engine/trainer.py: import configure_minimax_chat and call
  it alongside configure_qwen_chat, so cfg-supplied credentials,
  temperature, max_tokens, and enable_thinking reach the backend. Also
  apply cfg["minimax_model"] via set_target_deployment when the active
  target backend is minimax_chat.
- scripts/train.py: add 6 --minimax_* CLI flags + the corresponding
  _CLI_TO_YAML entries, add 'minimax' / 'minimax_chat' to the --backend
  choices, auto-route to target_backend=minimax_chat, and pick the
  right default target_model for the new backend.

Default behavior on existing backends (openai, claude, qwen, codex,
claude_code_exec) is unchanged; all 8 shipped configs continue to load
with gate_metric falling back to 'hard' for paper reproduction.

skillopt/config.py

@@ -79,6 +79,12 @@ _FLATTEN_MAP: dict[str, str] = {
     "model.qwen_chat_timeout_seconds": "qwen_chat_timeout_seconds",
     "model.qwen_chat_max_tokens": "qwen_chat_max_tokens",
     "model.qwen_chat_enable_thinking": "qwen_chat_enable_thinking",
+    "model.minimax_base_url": "minimax_base_url",
+    "model.minimax_api_key": "minimax_api_key",
+    "model.minimax_model": "minimax_model",
+    "model.minimax_temperature": "minimax_temperature",
+    "model.minimax_max_tokens": "minimax_max_tokens",
+    "model.minimax_enable_thinking": "minimax_enable_thinking",
     "train.num_epochs": "num_epochs",
     "train.train_size": "train_size",
     "train.steps_per_epoch": "steps_per_epoch",