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Agent skill

Explaining Machine Learning Models

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Build this skill enables AI assistant to provide interpretability and explainability for machine learning models.

MITAuto-check passedData & Analytics

Install Explaining Machine Learning Models

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill explaining-machine-learning-models -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace explaining-machine-learning-models --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/explaining-machine-learning-models .claude/skills/explaining-machine-learning-models && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
explaining-machine-learning-models
GitHub stars
2.8k
Token cost
~1k tokens
SKILL.md length
475 words
Files
7 (incl. scripts, references, assets)
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Build this skill enables AI assistant to provide interpretability and explainability for machine learning models.

  • Works in 4 steps: Analyze Context: Claude analyzes the… → Select Explanation Technique: Claude… → Generate Explanations: Claude uses the… → …
  • Requests explanations for model predictions
  • SKILL.md covers Overview, How It Works, When to Use This Skill and Examples, plus 7 more sections
  • Insights into feature importance

What it does

Explaining Machine Learning Models is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build this skill enables AI assistant to provide interpretability and explainability for machine learning models. it is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts, reference files and assets (for example `assets/README.md`, `assets/example_explanation.json` and `references/README.md`). Compatibility notes: Designed for Claude Code

It sits in Data & Analytics, covering Machine learning. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Requests explanations for model predictions
  • Insights into feature importance
  • Help understanding model behavior..
  • Appropriate context detected

Example prompts

  • “/explaining-machine-learning-models”

Requirements

  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(cmd:*)

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Analyze Context: Claude analyzes the user's request and the available model data.
  2. Select Explanation Technique: Claude chooses the most appropriate explanation technique (e.g., SHAP, LIME) based on the model type and the…
  3. Generate Explanations: Claude uses the selected technique to generate explanations for model predictions.
  4. Present Results: Claude presents the explanations in a clear and concise format, highlighting key insights and feature importances.

What it can do on your machine

Read from SKILL.md and the folder at commit 80f86df. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Grep
    • Glob
    • Bash(cmd:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/, which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Explaining Machine Learning Models loads about 1k tokens when it runs, and up to ~1.1k if it reads all its reference files. Until then it costs about 98 tokens; SKILL.md has 475 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~98
When it runs · the whole SKILL.md, loaded when a task matches
~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit 80f86df, republished under its MIT licence (© jeremylongshore). 475 words, ~1,044 tokens.

Download SKILL.mdSave it as .claude/skills/explaining-machine-learning-models/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
explaining-machine-learning-models
description
Build this skill enables AI assistant to provide interpretability and explainability for machine learning models. it is triggered when the user requests explanations for model predictions, insights into feature importance, or help understanding model behavior... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(cmd:*)
compatibility
Designed for Claude Code
version
1.21.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT
tags
ai, ml, explaining-machine

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

  1. Analyze Context: Claude analyzes the user's request and the available model data.
  2. Select Explanation Technique: Claude chooses the most appropriate explanation technique (e.g., SHAP, LIME) based on the model type and the user's needs.
  3. Generate Explanations: Claude uses the selected technique to generate explanations for model predictions.
  4. 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:

  1. Analyze the loan application data and the model's prediction.
  2. Calculate SHAP values to determine the contribution of each feature to the rejection decision.
  3. 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:

  1. Analyze the customer churn model and its predictions.
  2. Use LIME to generate local explanations for individual customer churn predictions.
  3. Aggregate the LIME explanations to identify the most important features driving churn, such as customer tenure or service usage.
Show full SKILL.md (167 more words)Show less

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

  1. Invoke this skill when the trigger conditions are met
  2. Provide necessary context and parameters
  3. Review the generated output
  4. 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

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references, assets) in skills/.curated/explaining-machine-learning-models of jeremylongshore/tons-of-skills-marketplace.

  • SKILL.md
  • assets/README.md
  • assets/example_explanation.json
  • assets/explanation_template.html
  • assets/visualization_styles.css
  • references/README.md
  • scripts/README.md

Open the folder on GitHubat commit 80f86df

Compare with similar skills

Explaining Machine Learning Models next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Explaining Machine Learning Models compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Explaining Machine Learning Models this skilljeremylongshore/tons-of-skills-marketplace2.8k—~1kAutomated safety check: PassMIT
Scikit LearnzLanqing/codex-claude-academic-skills4.7k16 repos~3.9kAutomated safety check: PassBSD-3-Clause
Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill188—~4kAutomated safety check: PassMIT
Senior Data ScientistRaidriar7170/hermes-skilleval1255 repos~1.4kAutomated safety check: PassMIT
Geomlitalo-goncalves/geoML109—~4.6kAutomated safety check: PassGPL-3.0
QuantMind Training Config Generatorqusong0627/QuantMind1.7k—~1.5kAutomated safety check: PassAGPL-3.0

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Categories

Questions about Explaining Machine Learning Models

What does Explaining Machine Learning Models do?

Build this skill enables AI assistant to provide interpretability and explainability for machine learning models. Explaining Machine Learning Models is an agent skill from jeremylongshore/tons-of-skills-marketplace. Build this skill enables AI assistant to provide interpretability and explainability for machine learning models.

When should I use Explaining Machine Learning Models?

Explaining Machine Learning Models fits situations like: requests explanations for model predictions; insights into feature importance; help understanding model behavior.; appropriate context detected.

How do I install Explaining Machine Learning Models in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill explaining-machine-learning-models -a claude-code`. Or copy the skill folder (skills/.curated/explaining-machine-learning-models in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/explaining-machine-learning-models in your project. Claude Code loads it when a task matches its description.

How do I install Explaining Machine Learning Models in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill explaining-machine-learning-models -a codex`. Or copy the skill folder (skills/.curated/explaining-machine-learning-models in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/explaining-machine-learning-models in your project. Codex loads it when a task matches its description.

Can I use Explaining Machine Learning Models in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill explaining-machine-learning-models -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/explaining-machine-learning-models, .gemini/skills/explaining-machine-learning-models, .github/skills/explaining-machine-learning-models and .opencode/skills/explaining-machine-learning-models in your project.

What does Explaining Machine Learning Models need to run?

SKILL.md names no scripts, command-line tools or credentials: Explaining Machine Learning Models is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Explaining Machine Learning Models access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Explaining Machine Learning Models safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Explaining Machine Learning Models use?

Explaining Machine Learning Models is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Explaining Machine Learning Models use?

About 1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 17 tokens, read only when the agent opens those files.

What are the alternatives to Explaining Machine Learning Models?

Skills that share tags, products or a category with Explaining Machine Learning Models: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars), Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars) and Geoml (italo-goncalves/geoML, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Explaining Machine Learning Models?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,825 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 9, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.