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.
- Skill optimization framework with training loop analogy
- 11 benchmarks, 4 model backends (Azure OpenAI, Claude, Codex, Qwen)
- WebUI for browser-based training control
- Pluggable architecture for extending benchmarks and backends
