ce-optimize

Automating iterative optimization loops requires defining a measurable goal and running parallel experiments to converge toward the best code performance solution. This Skill handles that process using metric-driven evaluation and a validated optimization spec.

To optimize search relevance, you can test different ranking strategies against hard metrics like latency and judge metrics like coherence. This Skill runs parallel experiments to evaluate these metrics and converge on the optimal search algorithm configuration.

Yes, you need a validated YAML optimization spec containing metric definitions and a measurement harness to run parallel experiments. The Skill uses this YAML file to automate the iterative optimization loop and evaluate measurable outcomes.

Yes, you can use Python and Git to automate clustering quality optimization by defining judge metrics like cluster coherence. The Skill leverages these dependencies to execute iterative experiments and evaluate the measurable outcomes automatically.

Metric-driven optimization supports both hard metrics like build time and test pass rate, alongside judge metrics like cluster coherence and search relevance. This comprehensive evaluation enables effective convergence during the iterative optimization process.

You should not use automated iterative optimization if your engineering problem lacks measurable outcomes or a defined measurement harness. Without validated metric definitions in a YAML spec, the parallel experiments cannot converge on a quantifiable best solution.