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/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>"
+}