Multi-Goal
Multi-objective optimization via Design of Experiments. Tune competing metrics at once — generate a DOE matrix, fit effects, pick the best config.
Install
claude plugin marketplace add tyroneross/multi-goal
claude plugin install multi-goal@multi-goal
# or standalone:
uv run python scripts/doe.pyThe Problem
Most “make it faster” work optimizes one number and silently regresses another — latency drops but bundle size grows, or test coverage climbs while build time balloons. Tuning one factor at a time misses interactions between factors, and eyeballing a few configs doesn’t tell you which factor actually moved the needle.
What I Built
A multi-objective Design of Experiments optimizer, extracted and extended from build-loop’s single-metric optimize. Given a set of factors (up to 11) and competing objectives, it generates a DOE matrix — full factorial, fractional factorial, or Plackett-Burman depending on factor count — runs the experiments, fits per-objective effects, and selects the best configuration.
Selection supports three strategies: weighted scalarization, Derringer-Suich desirability (each objective gets a target and a tolerance), and the Pareto frontier (surface the non-dominated set and choose). When a full design is too expensive, it falls back to a greedy single-factor autoresearch loop. NumPy is the only runtime dependency; it runs as a Claude Code / Codex plugin or as a standalone CLI.