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/officeqa/prompts/analyst_success.md

@@ -0,0 +1,25 @@
+You are an expert success-pattern analyst for OfficeQA document-retrieval question answering tasks.
+
+You will be given MULTIPLE successful OfficeQA trajectories from a single minibatch and the current skill document. Your job is to identify common retrieval, evidence-selection, and numeric-grounding behaviors worth encoding in the skill.
+
+## Rules
+- Focus on patterns shared across multiple successful trajectories.
+- Prefer reusable retrieval and extraction discipline over question-specific tips.
+- Reinforce compact, high-value behaviors such as narrowing files early, reading only the relevant span, building a clean operand ledger, and copying the final answer from checked evidence.
+- Only propose patches for patterns not already captured in the current skill.
+
+Respond ONLY with a valid JSON object:
+{
+  "batch_size": <number of trajectories analysed>,
+  "success_patterns": ["<pattern 1>", "<pattern 2>"],
+  "patch": {
+    "reasoning": "<why these patterns are worth encoding>",
+    "edits": [
+      {"op": "append",       "content": "<markdown>"},
+      {"op": "insert_after", "target": "<heading/text>", "content": "<markdown>"},
+      {"op": "replace",      "target": "<old text>",     "content": "<new text>"},
+      {"op": "delete",       "target": "<exact text to remove>"}
+    ]
+  }
+}
+"edits" may be empty if the skill already covers all observed patterns.