fix(minimax): honour OPTIMIZER_DEPLOYMENT for optimizer-role calls

Follow-up to #116. MiniMax exposed only TARGET_DEPLOYMENT, and
set_optimizer_deployment() never touched MiniMax, so a configured
optimizer_model was ignored: chat_optimizer and the optimizer message path
both sent TARGET_DEPLOYMENT. In mixed optimizer/target setups this could send
the wrong model name to the optimizer endpoint.

Adds OPTIMIZER_DEPLOYMENT and set_optimizer_deployment() to minimax_backend,
wired into the model facade. chat_optimizer and a new chat_optimizer_messages
use it, falling back to TARGET_DEPLOYMENT when unset. Also fixes dict[int]
return annotations to dict[str, int]. Adds routing tests.

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

skillopt/model/__init__.py

@@ -215,7 +215,7 @@ def chat_optimizer_messages(
             timeout=timeout,
         )
     if get_optimizer_backend() == "minimax_chat":
-        return _minimax.chat_target_messages(
+        return _minimax.chat_optimizer_messages(
             messages=messages,
             max_completion_tokens=max_completion_tokens,
             retries=retries,
@@ -540,3 +540,4 @@ def set_optimizer_deployment(deployment: str) -> None:
     _openai.set_optimizer_deployment(deployment)
     _claude.set_optimizer_deployment(deployment)
     _qwen.set_optimizer_deployment(deployment)
+    _minimax.set_optimizer_deployment(deployment)

skillopt/model/minimax_backend.py

@@ -36,6 +36,9 @@ TARGET_DEPLOYMENT = os.environ.get(
     "TARGET_DEPLOYMENT",
     default_model_for_backend("minimax_chat"),
 )
+# Optimizer role can point at a different MiniMax model than the target; falls
+# back to TARGET_DEPLOYMENT when unset so single-model setups are unaffected.
+OPTIMIZER_DEPLOYMENT = os.environ.get("OPTIMIZER_DEPLOYMENT", "")
 
 _config_lock = threading.Lock()
 tracker = TokenTracker()
@@ -222,7 +225,7 @@ def chat_target(
     stage: str = "target",
     reasoning_effort: str | None = None,
     timeout: float | None = None,
-) -> tuple[str, dict[int]]:
+) -> tuple[str, dict[str, int]]:
     del reasoning_effort
     messages = [{"role": "system", "content": system}, {"role": "user", "content": user}]
     return _chat_messages_impl(
@@ -242,7 +245,7 @@ def chat_optimizer(
     stage: str = "optimizer",
     reasoning_effort: str | None = None,
     timeout: float | None = None,
-) -> tuple[str, dict[int]]:
+) -> tuple[str, dict[str, int]]:
     """Optimizer chat call. Backend stores the trained skill; uses the same
     MiniMax-proxied OpenAI-compat endpoint as `chat_target`. Added in the
     parallel-training fix; previously missing in skillopt 0.2.0's
@@ -256,6 +259,7 @@ def chat_optimizer(
         max_completion_tokens,
         retries,
         stage,
+        deployment=OPTIMIZER_DEPLOYMENT or None,
         timeout=timeout,
     )
 
@@ -285,6 +289,35 @@ def chat_target_messages(
     )
 
 
+def chat_optimizer_messages(
+    messages: list[dict[str, Any]],
+    max_completion_tokens: int = 16384,
+    retries: int = 5,
+    stage: str = "optimizer",
+    reasoning_effort: str | None = None,
+    *,
+    tools: list[dict[str, Any]] | None = None,
+    tool_choice: str | dict[str, Any] | None = None,
+    return_message: bool = False,
+    timeout: float | None = None,
+) -> tuple[Any, dict[str, int]]:
+    """Optimizer-role message API. Same endpoint as ``chat_target_messages`` but
+    honours ``OPTIMIZER_DEPLOYMENT`` so optimizer and target can use distinct
+    MiniMax models; falls back to the target deployment when unset."""
+    del reasoning_effort
+    return _chat_messages_impl(
+        messages,
+        max_completion_tokens,
+        retries,
+        stage,
+        tools=tools,
+        tool_choice=tool_choice,
+        return_message=return_message,
+        deployment=OPTIMIZER_DEPLOYMENT or None,
+        timeout=timeout,
+    )
+
+
 def get_token_summary() -> dict[str, dict[str, int]]:
     return tracker.summary()
 
@@ -300,4 +333,10 @@ def set_reasoning_effort(effort: str | None) -> None:
 def set_target_deployment(deployment: str) -> None:
     global TARGET_DEPLOYMENT
     TARGET_DEPLOYMENT = deployment or default_model_for_backend("minimax_chat")
-    os.environ["TARGET_DEPLOYMENT"] = TARGET_DEPLOYMENT
+    os.environ["TARGET_DEPLOYMENT"] = TARGET_DEPLOYMENT
+
+
+def set_optimizer_deployment(deployment: str) -> None:
+    global OPTIMIZER_DEPLOYMENT
+    OPTIMIZER_DEPLOYMENT = deployment or default_model_for_backend("minimax_chat")
+    os.environ["OPTIMIZER_DEPLOYMENT"] = OPTIMIZER_DEPLOYMENT