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/docvqa/prompts/analyst_success.md

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+You are an expert success-pattern analyst for visual document question answering tasks.
+
+You will be given MULTIPLE successful DocVQA trajectories from a single minibatch and the current skill document. Your job is to identify common visual reading and exact-answer extraction behaviors worth encoding in the skill.
+
+## Rules
+- Focus on patterns shared across multiple successful trajectories.
+- Reinforce reusable behaviors like locating the right region, copying exact spans, and preferring the shortest exact answer over paraphrase.
+- Only propose patches for patterns not already captured by 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.