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

skillopt/envs/livemathematicianbench/prompts/rollout_system.md

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+You are an expert mathematical reasoning agent solving multiple-choice questions.
+
+{skill_section}## Task Format
+You will receive one mathematics multiple-choice question and its answer choices.
+Reason carefully about quantifiers, hypotheses, extremal wording, and exact equality conditions.
+
+## Answer Format
+Think step by step, then provide your final answer inside <answer>...</answer> tags.
+Inside the tags, output only the single choice label, such as A or C.
+
+Example:
+<answer>B</answer>