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/guide/installation.md

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+# Installation
+
+## Requirements
+
+- Python ≥ 3.10
+- At least one model API key (Azure OpenAI, OpenAI, Anthropic, or local Qwen)
+
+## Quick Install
+
+```bash
+git clone https://github.com/microsoft/SkillOpt.git
+cd SkillOpt
+pip install -e .
+```
+
+## Optional Dependencies
+
+Install extras for specific benchmarks or backends:
+
+=== "ALFWorld"
+
+    ```bash
+    pip install -e ".[alfworld]"
+    ```
+
+=== "Claude Backend"
+
+    ```bash
+    pip install -e ".[claude]"
+    ```
+
+=== "Qwen (Local)"
+
+    ```bash
+    pip install -e ".[qwen]"
+    ```
+
+=== "WebUI"
+
+    ```bash
+    pip install -e ".[webui]"
+    ```
+
+=== "Development"
+
+    ```bash
+    pip install -e ".[dev]"
+    ```
+
+=== "All"
+
+    ```bash
+    pip install -e ".[alfworld,claude,qwen,webui,dev]"
+    ```
+
+## Environment Variables
+
+Copy the example `.env` file and fill in your credentials:
+
+```bash
+cp .env.example .env
+```
+
+Edit `.env` with your API keys:
+
+```ini
+# Azure OpenAI (default backend)
+AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
+AZURE_OPENAI_API_KEY=your-key
+
+# Or use OpenAI directly
+OPENAI_API_KEY=sk-...
+
+# Or Anthropic Claude
+ANTHROPIC_API_KEY=sk-ant-...
+```
+
+!!! tip
+    You only need credentials for the backend you plan to use. Azure OpenAI is the default.
+
+## Verify Installation
+
+```bash
+python -c "import skillopt; print('SkillOpt ready!')"
+```
+
+## Next Steps
+
+→ [Run your first experiment](first-experiment.md)