mirror of https://github.com/microsoft/SkillOpt.git synced 2026-07-03 14:02:58 +08:00

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CharlesYang030 244e346b83 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

2026-05-21 17:22:04 +00:00

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Installation

Requirements

Python ≥ 3.10
At least one model API key (Azure OpenAI, OpenAI, Anthropic, or local Qwen)

Quick Install

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:

cp .env.example .env

Edit .env with your API keys:

# 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

python -c "import skillopt; print('SkillOpt ready!')"

Next Steps

→ Run your first experiment

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