feat: integrate MiniMax into model router, backend config, and common

common.py:
- Add minimax_chat → MiniMax/MiniMax-Text-01 to _BACKEND_DEFAULT_MODELS
- Add minimax/minimax_chat aliases to _BACKEND_ALIASES

backend_config.py:
- Add minimax_chat to set_optimizer_backend() valid set
- Add minimax_chat to set_target_backend() valid set
- Add minimax_chat to is_optimizer_chat_backend()
- Add minimax_chat to is_target_chat_backend()

__init__.py:
- Import minimax_backend as _minimax
- Add minimax_chat to set_backend() legacy handler
- Add minimax_chat to get_backend_name() reporting
- Route chat_target() and chat_target_messages() to _minimax
- Update NotImplementedError messages to list minimax_chat
- Aggregate _minimax into get_token_summary()
- Add _minimax.reset_token_tracker()
- Add configure_minimax_chat() delegator
- Add _minimax to set_reasoning_effort() and set_target_deployment()

skillopt/model/__init__.py

@@ -6,6 +6,7 @@ from typing import Any
 
 from skillopt.model import azure_openai as _openai
 from skillopt.model import claude_backend as _claude
+from skillopt.model import minimax_backend as _minimax
 from skillopt.model import qwen_backend as _qwen
 from skillopt.model.backend_config import (  # noqa: F401
     configure_claude_code_exec,
@@ -50,6 +51,10 @@ def set_backend(name: str | None) -> str:
         set_optimizer_backend("openai_chat")
         set_target_backend("qwen_chat")
         return "qwen_chat"
+    if normalized in {"minimax", "minimax_chat"}:
+        set_optimizer_backend("openai_chat")
+        set_target_backend("minimax_chat")
+        return "minimax_chat"
     raise ValueError(f"Unsupported legacy backend: {name!r}")
 
 
@@ -65,6 +70,8 @@ def get_backend_name() -> str:
         return "codex"
     if optimizer == "openai_chat" and target == "qwen_chat":
         return "qwen_chat"
+    if optimizer == "openai_chat" and target == "minimax_chat":
+        return "minimax_chat"
     return f"{optimizer}+{target}"
 
 
@@ -124,9 +131,18 @@ def chat_target(
             stage=stage,
             reasoning_effort=reasoning_effort,
         )
+    if get_target_backend() == "minimax_chat":
+        return _minimax.chat_target(
+            system=system,
+            user=user,
+            max_completion_tokens=max_completion_tokens,
+            retries=retries,
+            stage=stage,
+            reasoning_effort=reasoning_effort,
+        )
     if not is_target_chat_backend():
         raise NotImplementedError(
-            "chat_target is only supported with target_backend=openai_chat, claude_chat, or qwen_chat. "
+            "chat_target is only supported with target_backend=openai_chat, claude_chat, qwen_chat, or minimax_chat. "
             "Exec backends are handled in environment-specific rollout code."
         )
     return _openai.chat_target(
@@ -210,9 +226,20 @@ def chat_target_messages(
             tool_choice=tool_choice,
             return_message=return_message,
         )
+    if get_target_backend() == "minimax_chat":
+        return _minimax.chat_target_messages(
+            messages=messages,
+            max_completion_tokens=max_completion_tokens,
+            retries=retries,
+            stage=stage,
+            reasoning_effort=reasoning_effort,
+            tools=tools,
+            tool_choice=tool_choice,
+            return_message=return_message,
+        )
     if not is_target_chat_backend():
         raise NotImplementedError(
-            "chat_target_messages is only supported with target_backend=openai_chat, claude_chat, or qwen_chat. "
+            "chat_target_messages is only supported with target_backend=openai_chat, claude_chat, qwen_chat, or minimax_chat. "
             "Exec backends are handled in environment-specific rollout code."
         )
     return _openai.chat_target_messages(
@@ -301,6 +328,17 @@ def get_token_summary() -> dict:
         summary[stage]["prompt_tokens"] += values["prompt_tokens"]
         summary[stage]["completion_tokens"] += values["completion_tokens"]
         summary[stage]["total_tokens"] += values["total_tokens"]
+    minimax_summary = _minimax.get_token_summary()
+    for stage, values in minimax_summary.items():
+        if stage == "_total":
+            continue
+        if stage not in summary:
+            summary[stage] = values
+            continue
+        summary[stage]["calls"] += values["calls"]
+        summary[stage]["prompt_tokens"] += values["prompt_tokens"]
+        summary[stage]["completion_tokens"] += values["completion_tokens"]
+        summary[stage]["total_tokens"] += values["total_tokens"]
     total = {
         "calls": 0,
         "prompt_tokens": 0,
@@ -322,6 +360,7 @@ def reset_token_tracker() -> None:
     _openai.reset_token_tracker()
     _claude.reset_token_tracker()
     _qwen.reset_token_tracker()
+    _minimax.reset_token_tracker()
 
 
 def configure_azure_openai(
@@ -386,16 +425,37 @@ def configure_qwen_chat(
     )
 
 
+def configure_minimax_chat(
+    *,
+    base_url: str | None = None,
+    api_key: str | None = None,
+    temperature: float | str | None = None,
+    timeout_seconds: float | str | None = None,
+    max_tokens: int | str | None = None,
+    enable_thinking: bool | str | None = None,
+) -> None:
+    _minimax.configure_minimax_chat(
+        base_url=base_url,
+        api_key=api_key,
+        temperature=temperature,
+        timeout_seconds=timeout_seconds,
+        max_tokens=max_tokens,
+        enable_thinking=enable_thinking,
+    )
+
+
 def set_reasoning_effort(effort: str | None) -> None:
     _openai.set_reasoning_effort(effort)
     _claude.set_reasoning_effort(effort)
     _qwen.set_reasoning_effort(effort)
+    _minimax.set_reasoning_effort(effort)
 
 
 def set_target_deployment(deployment: str) -> None:
     _openai.set_target_deployment(deployment)
     _claude.set_target_deployment(deployment)
     _qwen.set_target_deployment(deployment)
+    _minimax.set_target_deployment(deployment)
 
 
 def set_optimizer_deployment(deployment: str) -> None: