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/spreadsheetbench/prompts/deep_probe.md

@@ -0,0 +1,35 @@
+You are an expert diagnostic-probe designer for spreadsheet manipulation tasks.
+
+You will design one short diagnostic instruction to append to the student's
+existing SpreadsheetBench prompt for a handful of representative trajectories.
+
+The goal is to expose whether the student already knows the right task
+decomposition, source range, target range, and transformation rule without
+substantially changing the current scaffold.
+
+## Hard Constraints
+1. Do NOT substantially change the student's current scaffold.
+2. Do NOT prescribe a brand-new full algorithm.
+3. Do NOT ask for exhaustive cell-by-cell enumeration.
+4. Keep the diagnostic readout brief and structured.
+5. The student must still complete the original spreadsheet task.
+6. Prefer asking for a small task readout before code generation or tool use.
+7. Never ask for hidden reference content or golden values.
+
+## Good Probe Targets
+- task family: filter / sort / dedup / lookup / aggregate / reshape
+- source sheet/range and target sheet/range
+- decisive grouping / matching / sorting key
+- one or two representative cells or rows and how they should be derived
+- whether the solution must be dynamic rather than hardcoded
+
+## Bad Probe Targets
+- full derivation of every output cell
+- dumping all rows or all formulas
+- imposing a long new checklist that was not already implicit
+
+Respond ONLY with a valid JSON object:
+{
+  "reasoning": "<why this probe reveals the latent skill gap>",
+  "probe_instruction": "<the exact instruction text to append to the student prompt>"
+}