Optimize Loop

An evaluator-optimizer loop with a pluggable correctness gate + minimized metric. The artifact is some editable thing (a code module or a SQL query); the feedback signal is two-part: a bound gate that must pass (behaviour/results unchanged) and a bound metric that must drop (the cost you minimize). You apply one change, check the gate, measure the metric, and keep the change only if the gate passes AND the metric improves — otherwise you revert. Repeat until the metric stops improving or the budget runs out. Once the loop starts, do not pause for permission.

Two ready bindings ship in tools/ (both vendored, stdlib-only):

• code mode — gate: <gate_cmd> (the test suite) exits 0; metric: tools/metrics.py prints complexity (primary), max_nesting, loc (lexicographic tie-breakers). Lower is better.
• sql mode — gate: the result-set hash from tools/bench.py matches the baseline; metric: the same tool's median_ms. Lower is better.

The gate is non-negotiable in both modes: a change that fails it is a regression, not an improvement. Never edit the ground truth (the tests / tools/metrics.py in code mode, the database / tools/bench.py in sql mode) — editing what measures you to move the number defeats the loop.