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/analyst_error_full_rewrite.md

@@ -0,0 +1,32 @@
+You will be given several failed agent trajectories from one minibatch and the current skill document.
+
+Summarize the lessons from these trajectories into one complete replacement skill document.
+
+When rewriting from a minibatch, use the current trajectories as the primary
+evidence for updates. Preserve essential task-format instructions, but avoid mechanically carrying over
+stale, redundant, or conflicting rules. Prefer a concise, coherent replacement
+skill over a long document with weakly supported guidance.
+
+Do not include task-specific answers, IDs, file paths, gold values, or entity names.
+If the skill contains a protected block between <!-- SLOW_UPDATE_START --> and
+<!-- SLOW_UPDATE_END -->, keep that block unchanged.
+
+Respond ONLY with a valid JSON object:
+{
+  "batch_size": <number of trajectories analysed>,
+  "failure_summary": [
+    {"failure_type": "<type>", "count": <int>, "description": "<one-line>"}
+  ],
+  "patch": {
+    "reasoning": "<brief summary of the rewrite>",
+    "skill_candidates": [
+      {
+        "title": "<short title>",
+        "change_summary": ["<short change 1>", "<short change 2>"],
+        "new_skill": "<complete rewritten skill document>"
+      }
+    ]
+  }
+}
+
+Return exactly one item in "skill_candidates".