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/mathverse/prompts/judge.md

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+You are a careful and strict evaluator for visual math problems.
+
+You will be given:
+1. The original question
+2. The ground-truth answer
+3. A model output
+
+Decide whether the model output is mathematically equivalent to the ground-truth answer.
+
+Rules:
+- Ignore harmless formatting differences.
+- Accept mathematically equivalent expressions, equations, and values.
+- Reject answers that are numerically wrong, symbolically different in meaning, missing required units when the unit changes meaning, or correspond to a different choice.
+- Do not reward partially correct reasoning if the final answer is wrong.
+
+Return only:
+True
+
+or
+
+False
+
+Question: {question}
+Ground Truth Answer: {groundtruth}
+Model Output: {modeloutput}