explaining-machine-learning-models
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- Claude Code
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- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @tomevault-io · no license declared
- Token usage
- Lean
- Setup complexity
- Plug-and-play
- External API key
- Not required
- Operating systems
- Unspecified (assume cross-platform)
- Runtime requirements
- No special requirements
- Permissions
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- Read-only
- Write / modify
- 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: explaining-machine-learning-models
description: | Use when this capability is needed. Interpret machine learning model predictions using SHAP, L…
category: other
runtime: no special runtime
---
# explaining-machine-learning-models output preview
## PART A: Task fit
- Use case: | Use when this capability is needed. Interpret machine learning model predictions using SHAP, LIME, and feature importance analysis to explain model behavior. This skill empowers Claude to analyze and explain machine learning models. It helps users understand why a model makes certain predictions, identify the most influential features, and gain insights….
- 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. Interpret machine learning model predictions using SHAP, LIME, and feature importance analysis to explain model behavior. This skill empowers Claude to analyze and explain machine learning models. It helps users understand why a model makes certain predictions, identify the most influential features, and gain insights…”.
- **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; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files; 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 does not require a stable slash command. After installation, invoke the skill by name and describe the task.
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.
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: explaining-machine-learning-models
description: | Use when this capability is needed. Interpret machine learning model predictions using SHAP, L…
category: other
source: tomevault-io/skills-registry
---
# explaining-machine-learning-models
## When to use
- | Use when this capability is needed. Interpret machine learning model predictions using SHAP, LIME, and feature impor…
- 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; 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 "explaining-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 | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
}Model Explainability Tool
Interpret machine learning model predictions using SHAP, LIME, and feature importance analysis to explain model behavior.
Overview
This skill empowers Claude to analyze and explain machine learning models. It helps users understand why a model makes certain predictions, identify the most influential features, and gain insights into the model's overall behavior.
How It Works
- Analyze Context: Claude analyzes the user's request and the available model data.
- Select Explanation Technique: Claude chooses the most appropriate explanation technique (e.g., SHAP, LIME) based on the model type and the user's needs.
- Generate Explanations: Claude uses the selected technique to generate explanations for model predictions.
- Present Results: Claude presents the explanations in a clear and concise format, highlighting key insights and feature importances.
When to Use This Skill
This skill activates when you need to:
- Understand why a machine learning model made a specific prediction.
- Identify the most important features influencing a model's output.
- Debug model performance issues by identifying unexpected feature interactions.
- Communicate model insights to non-technical stakeholders.
- Ensure fairness and transparency in model predictions.
Examples
Example 1: Understanding Loan Application Decisions
User request: "Explain why this loan application was rejected."
The skill will:
- Analyze the loan application data and the model's prediction.
- Calculate SHAP values to determine the contribution of each feature to the rejection decision.
- Present the results, highlighting the features that most strongly influenced the outcome, such as credit score or debt-to-income ratio.
Example 2: Identifying Key Factors in Customer Churn
User request: "Interpret the customer churn model and identify the most important factors."
The skill will:
- Analyze the customer churn model and its predictions.
- Use LIME to generate local explanations for individual customer churn predictions.
- Aggregate the LIME explanations to identify the most important features driving churn, such as customer tenure or service usage.
Best Practices
- Model Type: Choose the explanation technique that is most appropriate for the model type (e.g., tree-based models, neural networks).
- Data Preprocessing: Ensure that the data used for explanation is properly preprocessed and aligned with the model's input format.
- Visualization: Use visualizations to effectively communicate model insights and feature importances.
Integration
This skill integrates with other data analysis and visualization plugins to provide a comprehensive model understanding workflow. It can be used in conjunction with data cleaning and preprocessing plugins to ensure data quality and with visualization tools to present the explanation results in an informative way.
Prerequisites
- Appropriate file access permissions
- Required dependencies installed
Instructions
- Invoke this skill when the trigger conditions are met
- Provide necessary context and parameters
- Review the generated output
- Apply modifications as needed
Output
The skill produces structured output relevant to the task.
Error Handling
- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps
Resources
- Project documentation
- Related skills and commands
Source: ComeOnOliver/skillshub — distributed by TomeVault.
Decide Fit First
Design Intent
How To Use It
Boundaries And Review