evaluating-machine-learning-models

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Interpretation is structured for decision-making; original keeps the upstream SKILL.md unchanged.

Decide Fit First

Core job: | Use when this capability is needed. This skill empowers Claude to perform thorough evaluations of machine learning models, pro…
Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

Structure: The skill is organized around “Overview”, “How It Works”, “When to Use This Skill”, “Examples”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
Invocation: Name evaluating-machine-learning-models directly; if the source includes slash commands, start with the command and then add task context.
Validation: Start small and check whether the result follows “Overview / How It Works / When to Use This Skill” before expanding.

Boundaries And Review

Dependencies: It usually needs no extra API key, so start with a small validation task.
Permissions: Declared permissions include read / write / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.

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: ComeOnOliver/skillshub — distributed by TomeVault.

Fluxly profile Author and license come from source; runtime, permissions, and network are Fluxly detections or estimates

Fluxly category: Other
Author-declared agents: No explicit declaration found; this is not inferred or tested compatibility
Static check: 88 / 100 · heuristic scan, not runtime safety proof
Author / version / license: @tomevault-io · no license declared
Fluxly token estimate: Lean
Fluxly setup estimate: Guided setup
External API key: No requirement detected
Detected OS requirements: Unspecified
Runtime requirements: Unspecified
Detected file/system behavior: Read-only
Write / modify
Shell exec
Detected network behavior: Local-only
Install commands: None (reference only)

Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.

Heads up: 未限定 allowed-tools，默认拥有全部工具权限。

Output preview evaluating-machine-learning-models.preview

The current SKILL.md does not define a fixed output example.

SKILL.md SKILL.md

## 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

1. **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 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:
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 Performance

User 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.

…

Evidence boundary and execution chain

Author text anchors workflow facts; Fluxly only indexes current sections, terms, files, and commands.

sections -> Overview → How It Works → When to Use This Skill → Examples → Example 1: Evaluating Model Accuracy → Example 2: Comparing Model Performance
terms    -> Analyzing Context · Executing Evaluation · Presenting Results · Specify Metrics · Data Validation · Interpret Results
files/cmd -> model-evaluation-suite · /eval-model · ComeOnOliver/skillshub · github.com/ComeOnOliver/skillshub
body sha256 -> a2f688fda568