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/merge_success_full_rewrite.md

@@ -0,0 +1,28 @@
+You will be given complete skill candidates written from successful trajectories and the current skill document.
+
+Combine them into one complete replacement skill document.
+
+When merging full-skill candidates, preserve essential task-format instructions,
+but do not mechanically retain stale, redundant, or
+conflicting rules. If candidates disagree, prefer the concise rule with clearer
+trajectory support and better consistency with the replacement skill.
+
+Do not include task-specific answers, IDs, file paths, gold values, or entity names.
+If the current skill contains a protected block between <!-- SLOW_UPDATE_START --> and
+<!-- SLOW_UPDATE_END -->, keep that block unchanged.
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<brief summary of how the candidates were combined>",
+  "skill_candidates": [
+    {
+      "title": "<short title>",
+      "change_summary": ["<short change 1>", "<short change 2>"],
+      "new_skill": "<complete merged skill document>",
+      "support_count": <integer>,
+      "source_type": "success"
+    }
+  ]
+}
+
+Return exactly one item in "skill_candidates".