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Bayesian optimizer

Skill FridrichMethod/awesome-skills/skills/bayesian-optimizer

Bayesian OptimizeFrom its SKILL.md

Install
npx -y skills add FridrichMethod/awesome-skills --skill bayesian-optimizer

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SKILL.md

1.9 KB, 442 tokens by cl100k_base, as published. Nobody here has run it

<!-- # COPYRIGHT NOTICE # This file is part of the "Universal Biomedical Skills" project. # Copyright (c) 2026 MD BABU MIA, PhD <[email protected]> # All Rights Reserved. # # This code is proprietary and confidential. # Unauthorized copying of this file, via any medium is strictly prohibited. # # Provenance: Authenticated by MD BABU MIA -->

Bayesian Optimization (Self-Driving Lab)

The Bayesian Optimizer allows agents to efficiently explore a parameter space to maximize a target metric (yield, purity, binding affinity) with minimal experiments. It uses Gaussian Processes to model uncertainty and the Upper Confidence Bound (UCB) acquisition function.

When to Use This Skill

  • When experiments are expensive or time-consuming.
  • To autonomously tune hyperparameters for a machine learning model.
  • To optimize reaction conditions (temperature, pH, concentration).

Core Capabilities

  1. Next Step Proposal: Suggests the next best experiment parameters.
  2. Surrogate Modeling: Predicts outcomes for untested parameters.
  3. Exploration/Exploitation: Balances trying new things vs. refining known good results.

Workflow

  1. Input: History of past experiments (params -> results) and bounds.
  2. Process: Fits a Gaussian Process to the data.
  3. Output: Returns the parameters for the next experiment.

Example Usage

User: "Given these past results, what temperature and pH should I try next?"

Agent Action:

python3 Skills/Mathematics/Probability_Statistics/bayesian_optimization.py \
    --history "[[20, 7.0, 0.5], [25, 6.5, 0.6]]" \
    --bounds "[[10, 40], [5, 9]]" \
    --output next_experiment.json
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->

What ships with it: 4 files

12.8 KB alongside SKILL.md, 2 of them executable

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