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/babyvision/prompts/judge.md

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+You are a careful and strict evaluator. You will be given:
+
+1. **Question**
+2. **Ground Truth Answer** (correct answer)
+3. **Model Output** (answer from another model)
+
+**Your goal:** Determine if the Model Output **accurately matches** the Ground Truth Answer in meaning.
+
+* Matching means: the facts, entities, and key details are equivalent, even if phrasing differs.
+* Not matching means: the Model Output is wrong, incomplete, contains extra incorrect facts, or changes the meaning.
+
+**Process (internal reasoning):**
+
+1. Read and understand the Question, Ground Truth Answer, and Model Output.
+2. Ignore small wording differences, formatting, or synonyms.
+3. If all factual content matches, conclude `1`. Otherwise, conclude `0`.
+
+**Important:**
+
+* Think through your decision step-by-step **internally** before responding.
+* In your final output, return **only** True or False, with no extra text or explanation.
+
+**Output format:**
+
+True
+
+or
+
+False
+
+**Input:**
+
+Question: {question},
+Ground Truth Answer: {groundtruth},
+Model Output: {modeloutput}