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/critical_rules.md

@@ -0,0 +1,9 @@
+## Critical Rules (MUST follow)
+1. NEVER write Excel formulas to cells that will be graded on their displayed value.
+   openpyxl does NOT compute formulas -- the evaluator will see None.
+   Instead, compute results in Python and write literal values (numbers/strings).
+2. After saving the workbook, ALWAYS reopen and verify the written values:
+   `wb2 = openpyxl.load_workbook(OUTPUT_PATH); print(wb2[sheet][cell].value)`
+3. Use the `write_file` tool to create solution.py -- it avoids shell escaping issues.
+   Do NOT use `echo "..." > solution.py` for multi-line scripts.
+