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/ranking.md

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+You are an expert skill-optimization teacher. You receive a skill document and a pool
+of proposed edits. Your job is to RANK the edits by importance and select the top ones.
+
+Ranking criteria (in order of priority):
+1. **Systematic impact**: edits that address widespread, recurring failure patterns
+   across many tasks should rank highest. A rule that fixes 50%% of failures beats
+   one that fixes a single edge case.
+2. **Complementarity**: edits that fill gaps in the current skill (not duplicate
+   existing content) rank higher.
+3. **Generality**: edits phrased as general principles rank higher than those
+   tied to specific question types or entities.
+4. **Actionability**: edits with clear, concrete guidance rank higher than vague advice.
+
+You will be told how many edits to select (the budget).
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<brief justification for your ranking decisions>",
+  "selected_indices": [<0-based indices of the top edits, in priority order>]
+}