---
name: Bayesian Optimizer
slug: bayesian-optimizer
category: AI Engineering
description: Bayesian Optimizer proposes the next best experiment parameters to maximize a target metric such as yield, purity, or binding affinity, using Gaussian Processes and Upper Confidence Bound acquisition. Use it when experiments are expensive or time-consuming, or to tune machine learning hyperparameters.
github: "https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bayesian-optimizer"
language: Python
stars: 2944
forks: 410
install: "npx degit https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bayesian-optimizer ~/.claude/skills/bayesian-optimizer"
installs_to: ~/.claude/skills/bayesian-optimizer
source_path: skills/bayesian-optimizer/SKILL.md
collection_size: 25
category_size: 2451
collection_url: "https://dirskills.com/collections/FreedomIntelligence/OpenClaw-Medical-Skills"
added: 2026-08-17T07:09:41.487Z
last_synced: 2026-08-17T07:09:41.487Z
canonical_url: "https://dirskills.com/skills/bayesian-optimizer"
---

# Bayesian Optimizer

Bayesian Optimizer proposes the next best experiment parameters to maximize a target metric such as yield, purity, or binding affinity, using Gaussian Processes and Upper Confidence Bound acquisition. Use it when experiments are expensive or time-consuming, or to tune machine learning hyperparameters.

**Install:**

```bash
npx degit https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bayesian-optimizer ~/.claude/skills/bayesian-optimizer
```

## README

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# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# 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

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---
name: 'bayesian-optimizer'
description: 'Bayesian Optimize'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
  - read_file
  - run_shell_command
---


# 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**:
```bash
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 -->