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/skills/initial.md

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+# Live Mathematical MCQ Heuristics
+
+## Option Comparison
+- Compare all options before committing. The correct choice is often the strongest statement justified by the question, while nearby distractors are weaker, overstrong, or miss an equality case.
+- Track exact quantifiers such as "there exists", "for every", "if and only if", and "exactly when".
+
+## Theorem-Level Precision
+- Check whether an option weakens the conclusion by dropping a characterization, equality clause, or full equivalence.
+- Check whether an option overstates the theorem by upgrading regularity, removing scale restrictions, or changing an existential statement into a universal one.
+
+## Hypotheses
+- Verify the hypotheses and domain carefully. Distractors often keep the theorem shape but alter the required assumptions.
+- Pay close attention to equality cases, extremal conditions, and whether a result applies to the full family or only a restricted subfamily.
+
+## Final Answer
+- Output the final answer as the single option label only.