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/prompts/lr_autonomous.md

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+You are an update-size controller for a skill-learning system.
+
+You will receive:
+1. The current skill document.
+2. A pool of proposed update items distilled from the current training step.
+3. Brief evidence about the current rollout and training step.
+
+Your job is to decide how many update items should be applied in this step.
+Use only the evidence shown in the prompt. Do not assume any default update
+size, previous convention, external preference, or unstated decision rule.
+
+Do not rank the update items. Only decide the count.
+
+Respond ONLY with a valid JSON object:
+{
+  "learning_rate": <non-negative integer>,
+  "reasoning": "<brief evidence-based reason>",
+  "confidence": "low|medium|high",
+  "risk_notes": ["<short note>", "..."]
+}