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Optuna bayesian optimization starter

Skill ma-compbio-lab/SkillFoundry/skills/statistical-and-machine-learning-foundations-for-science/optuna-bayesian-optimization-starter

A framework for discovering, compiling, and validating reusable skills for scientific agents.

Install
npx -y skills add ma-compbio-lab/SkillFoundry --skill optuna-bayesian-optimization-starter

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

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Optuna Bayesian Optimization Starter

Use this skill to run a deterministic Optuna optimization loop on a small synthetic objective and inspect the best trial summary.

What This Skill Does

  • defines a two-parameter toy objective
  • optimizes it with Optuna's TPE sampler
  • records the best trial, best parameters, and a short ranked trial table

When To Use It

  • when you need a runnable bayesian-optimization starter
  • when you want a local Optuna example before wiring in an expensive scientific objective
  • when you need deterministic optimization outputs for repository tests

Run

./slurm/envs/statistics/bin/python skills/statistical-and-machine-learning-foundations-for-science/optuna-bayesian-optimization-starter/scripts/run_optuna_bayesian_optimization.py --out scratch/optuna/bayesian_optimization_summary.json

Notes

  • The objective is synthetic and smooth on purpose; it exists to verify the optimization loop, not to benchmark samplers.
  • Increase the trial budget only after replacing the toy objective with a real scientific function.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.