How do I install the Jackreis Arbiter Evaluating Machine Learning Models skill?

Run npx get-skill add tomevault-io/jackreis-arbiter-evaluating-machine-learning-models in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Which AI agents work with Jackreis Arbiter Evaluating Machine Learning Models?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Who published Jackreis Arbiter Evaluating Machine Learning Models?

tomevault-io (@tomevault-io) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Jackreis Arbiter Evaluating Machine Learning Models by tomevault-io

Name: Jackreis Arbiter Evaluating Machine Learning Models
Author: tomevault-io

File contents

Overview

This skill empowers Claude to perform thorough evaluations of machine learning models, providing detailed performance insights. It leverages the model-evaluation-suite plugin to generate a range of metrics, enabling informed decisions about model selection and optimization.

How It Works

Analyzing Context: Claude analyzes the user's request to identify the model to be evaluated and any specific metrics of interest.
Executing Evaluation: Claude uses the /eval-model command to initiate the model evaluation process within the model-evaluation-suite plugin.
Presenting Results: Claude presents the generated metrics and insights to the user, highlighting key performance indicators and potential areas for improvement.

When to Use This Skill

This skill activates when you need to:

Assess the performance of a machine learning model.
Compare the performance of multiple models.
Identify areas where a model can be improved.
Validate a model's performance before deployment.

Examples

Example 1: Evaluating Model Accuracy

User request: "Evaluate the accuracy of my image classification model."

The skill will:

Invoke the /eval-model command.
Analyze the model's performance on a held-out dataset.
Report the accuracy score and other relevant metrics.

Example 2: Comparing Model Performance

User request: "Compare the F1-score of model A and model B."

The skill will:

Invoke the /eval-model command for both models.
Extract the F1-score from the evaluation results.
Present a comparison of the F1-scores for model A and model B.

Best Practices

Specify Metrics: Clearly define the specific metrics of interest for the evaluation.
Data Validation: Ensure the data used for evaluation is representative of the real-world data the model will encounter.
Interpret Results: Provide context and interpretation of the evaluation results to facilitate informed decision-making.

Integration

This skill integrates seamlessly with the model-evaluation-suite plugin, providing a comprehensive solution for model evaluation within the Claude Code environment. It can be combined with other skills to build automated machine learning workflows.

Source: JackReis/arbiter — distributed by TomeVault.

---name: jackreis-arbiter-evaluating-machine-learning-modelsdescription: Overview---## OverviewThis skill empowers Claude to perform thorough evaluations of machine learning models, providing detailed performance insights. It leverages the `model-evaluation-suite` plugin to generate a range of metrics, enabling informed decisions about model selection and optimization.## How It Works1. **Analyzing Context**: Claude analyzes the user's request to identify the model to be evaluated and any specific metrics of interest.2. **Executing Evaluation**: Claude uses the `/eval-model` command to initiate the model evaluation process within the `model-evaluation-suite` plugin.3. **Presenting Results**: Claude presents the generated metrics and insights to the user, highlighting key performance indicators and potential areas for improvement.## When to Use This SkillThis skill activates when you need to:- Assess the performance of a machine learning model.- Compare the performance of multiple models.- Identify areas where a model can be improved.- Validate a model's performance before deployment.## Examples### Example 1: Evaluating Model AccuracyUser request: "Evaluate the accuracy of my image classification model."The skill will:1. Invoke the `/eval-model` command.2. Analyze the model's performance on a held-out dataset.3. Report the accuracy score and other relevant metrics.### Example 2: Comparing Model PerformanceUser request: "Compare the F1-score of model A and model B."The skill will:1. Invoke the `/eval-model` command for both models.2. Extract the F1-score from the evaluation results.3. Present a comparison of the F1-scores for model A and model B.## Best Practices- **Specify Metrics**: Clearly define the specific metrics of interest for the evaluation.- **Data Validation**: Ensure the data used for evaluation is representative of the real-world data the model will encounter.- **Interpret Results**: Provide context and interpretation of the evaluation results to facilitate informed decision-making.## IntegrationThis skill integrates seamlessly with the `model-evaluation-suite` plugin, providing a comprehensive solution for model evaluation within the Claude Code environment. It can be combined with other skills to build automated machine learning workflows.---> Source: [JackReis/arbiter](https://github.com/JackReis/arbiter) — distributed by [TomeVault](https://tomevault.io).

Frequently asked questions

Jackreis Arbiter Evaluating Machine Learning Models