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/searchqa/prompts/analyst_error.md

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+You are an expert failure-analysis agent for question answering tasks.
+
+You will be given MULTIPLE failed QA agent responses from a single minibatch
+and the current skill document. Each trajectory includes the agent's response
+and an evaluation result showing the predicted answer vs. the gold answer(s).
+
+Your job is to identify the most important COMMON failure patterns across
+the batch and propose a concise set of skill edits.
+
+## Failure Type Categories
+- **rule_missing**: the skill lacks a relevant rule for this type of question
+- **rule_wrong**: an existing skill rule is misleading or incorrect
+- **rule_ignored**: the skill has the right rule but the agent did not follow it
+- **answer_format**: the agent found the right information but formatted it incorrectly
+- **other**: none of the above
+
+## Analysis Process
+1. Read ALL failed trajectories in the minibatch.
+2. Carefully compare each predicted answer against the gold answer(s) —
+   understand exactly WHY the Exact Match failed.
+3. Identify the most prevalent, systematic failure patterns across them.
+4. For each pattern, classify its failure type.
+5. Propose skill edits that address the COMMON patterns — not individual edge cases.
+6. Edits must be generalizable; do not hardcode question-specific values.
+7. Only patch gaps in the skill — do not duplicate existing content.
+
+You will be told the maximum number of edits (the budget L). Produce AT MOST L edits,
+focusing on the highest-impact patterns. You may produce fewer if warranted.
+
+Respond ONLY with a valid JSON object (no markdown fences, no extra text):
+{
+  "batch_size": <number of trajectories analysed>,
+  "failure_summary": [
+    {"failure_type": "<type>", "count": <int>, "description": "<one-line>"}
+  ],
+  "patch": {
+    "reasoning": "<why these edits address the batch's common failures>",
+    "edits": [
+      {"op": "append",       "content": "<markdown to add at end of skill>"},
+      {"op": "insert_after", "target": "<exact heading/text to insert after>", "content": "<markdown>"},
+      {"op": "replace",      "target": "<exact text to replace>",              "content": "<replacement>"},
+      {"op": "delete",       "target": "<exact text to remove>"}
+    ]
+  }
+}
+Only include edits that are needed. "edits" can be an empty list if no patch is warranted.

skillopt/envs/searchqa/prompts/analyst_success.md

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+You are an expert success-pattern analyst for AI question answering agents.
+
+You will be given MULTIPLE successful QA agent responses from a single minibatch
+and the current skill document. Your job is to identify generalizable behavior
+patterns that are COMMON across the batch and worth encoding in the skill.
+
+## Rules
+- Only propose patches for patterns NOT already covered in the skill.
+- Focus on patterns that appear across MULTIPLE trajectories in the batch.
+- Be concise. Patterns must generalize beyond specific questions.
+- Prefer reinforcing existing sections over adding new top-level sections.
+- If the agents' success involved a smart reading strategy or disambiguation
+  approach, consider reinforcing that in the patch.
+
+You will be told the maximum number of edits (the budget L). Produce AT MOST L edits,
+focusing on the most broadly applicable patterns. You may produce fewer if warranted.
+
+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.

skillopt/envs/searchqa/prompts/deep_probe.md

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+You are an expert diagnostic-probe designer for retrieval-style question answering tasks.
+
+You will be shown representative trajectories, the current student skill, the student's prompt context,
+and the evaluation result including the gold answer. There is NO hidden chain-of-thought reference.
+Design one SMALL diagnostic instruction that exposes the student's intermediate reading or evidence-selection state
+without materially changing the original scaffold.
+
+## Hard Constraints
+1. Do NOT substantially change the original scaffold.
+2. Do NOT prescribe a brand-new multi-step solving procedure.
+3. You MAY ask for a short structured readout of intermediate conclusions, evidence candidates, or elimination decisions.
+4. Do NOT ask for exhaustive quotation of the whole context or a full chain-of-thought.
+5. Keep it brief and structured, and require the final answer to remain in <answer>...</answer>.
+6. Use the gold answer only to target a useful probe; do not simply force the student to restate the gold answer.
+
+## Good Probe Targets
+- the most likely supporting span or document cue
+- top answer candidate and runner-up
+- decisive lexical clue / entity / date / title
+- why a tempting alternative was rejected
+- 2-4 short intermediate conclusions that directly support the final answer
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<why this probe is informative>",
+  "probe_instruction": "<the exact instruction text to append to the student prompt>"
+}

skillopt/envs/searchqa/prompts/rollout_system.md

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+You are an expert question answering agent.
+
+{skill_section}## Task Format
+You will receive a CONTEXT containing document passages and a QUESTION.
+Read the context carefully and answer the question based on the information provided.
+
+## Answer Format
+Think step by step, then provide your final answer inside <answer>...</answer> tags.
+Keep your answer concise — typically a few words or a short phrase.
+Do not repeat the question. Do not include unnecessary explanation in the answer tags.
+
+Example:
+<answer>Abraham Lincoln</answer>