evaluating-machine-learning-models
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- Author updated Live
- Author repo skills-registry
- Domain
- Other
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
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。
---
name: evaluating-machine-learning-models
description: | Use when this capability is needed. This skill empowers Claude to perform thorough evaluations…
category: other
runtime: no special runtime
---
# evaluating-machine-learning-models output preview
## PART A: Task fit
- Use case: | Use when this capability is needed. 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. runs entirely locally. Works with Claude Cod….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Overview / How It Works / When to Use This Skill” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “| Use when this capability is needed. 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. runs entirely locally. Works with Claude Cod…”.
- **02** When the source has headings, the agent prioritizes “Overview / How It Works / When to Use This Skill” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options.The source mentions slash commands such as `/eval-model`; use them first when your agent supports command triggers.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Overview / How It Works / When to Use This Skill”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: evaluating-machine-learning-models
description: | Use when this capability is needed. This skill empowers Claude to perform thorough evaluations…
category: other
source: tomevault-io/skills-registry
---
# evaluating-machine-learning-models
## When to use
- | Use when this capability is needed. This skill empowers Claude to perform thorough evaluations of machine learning m…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Overview / How It Works / When to Use This Skill” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies.The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "evaluating-machine-learning-models" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Overview / How It Works / When to Use This Skill
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}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-modelcommand to initiate the model evaluation process within themodel-evaluation-suiteplugin. - 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-modelcommand. - 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-modelcommand 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.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review