SkillOpt v0.1.0: initial release

- 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

docs/reference/api.md

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+# API Reference
+
+## Core Classes
+
+### `EnvAdapter`
+
+Abstract base class for benchmark environments.
+
+```python
+class EnvAdapter(ABC):
+    async def execute(self, item, skill, model) -> TaskResult
+    def evaluate(self, prediction, ground_truth) -> float
+    def build_prompt(self, item, skill) -> str
+```
+
+### `DataLoader`
+
+Abstract base class for data loading and splitting.
+
+```python
+class DataLoader(ABC):
+    def setup(self, cfg: dict) -> None
+    def get_split_items(self, split: str) -> list[DataItem]
+```
+
+### `ModelBackend`
+
+Abstract base class for LLM backends.
+
+```python
+class ModelBackend(ABC):
+    async def generate(self, messages, **kwargs) -> ModelResponse
+    async def generate_with_tools(self, messages, tools, **kwargs) -> ModelResponse
+```
+
+### `Trainer`
+
+Main training loop orchestrator.
+
+```python
+class Trainer:
+    def __init__(self, cfg: dict)
+    async def train(self) -> TrainResult
+    async def evaluate(self, skill: str, split: str) -> EvalResult
+```
+
+## Data Classes
+
+### `DataItem`
+
+```python
+@dataclass
+class DataItem:
+    id: str
+    input: str
+    ground_truth: str
+    metadata: dict = field(default_factory=dict)
+```
+
+### `TaskResult`
+
+```python
+@dataclass
+class TaskResult:
+    item_id: str
+    prediction: str
+    score: float
+    trajectory: list[dict]
+```
+
+### `ModelResponse`
+
+```python
+@dataclass
+class ModelResponse:
+    content: str
+    usage: dict
+    model: str
+```
+
+For detailed source code, see the [`skillopt/`](https://github.com/microsoft/SkillOpt/tree/main/skillopt) directory.