optimize

# Optimize Metric-driven optimization with explicit guards, rollback criteria, experiment log. ## Input Schema ```json { "goal": "required measurable improvement objective", "mode": "single|campaign", "metric_cmd": "required command that emits or validates the target metric", "metric_direction": "higher|lower", "guard_cmd": "required command that must continue to pass", "max_iterations": "optional integer, default 1", "min_delta": "optional practical significance threshold", "scope_files": [ "paths the optimization may edit" ], "done_when": "metric improves without guard regression" } ``` ## Workflow ### 01: Create run directory Run `python PLUGIN_ROOT/shared/create_run.py --skill optimize` once. Retain its single printed path as `<run-directory>` and substitute that literal path into every later artifact path and helper argument. Never store or reuse the path through a shell variable; shell variables do not persist across tool calls. ### 02: Validate metric and guard commands Require: - Repeatable `metric_cmd` producing comparable value or pass/fail. - Known `metric_direction`. - `guard_cmd` fails on unacceptable regressions. - Bounded `scope_files`. - Explicit, bounded `max_iterations` for `campaign`. - Protect files/scripts used by `metric_cmd`/`guard_cmd` unless user explicitly scopes them and accepts measurement-integrity risk. Dry-run both before edit: Execute the configured `metric_cmd` and `guard_cmd` separately with the host-native ...