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Agent skill

Explaining Machine Learning Models

by foryourhealth111-pixel in foryourhealth111-pixel/Vibe-Skills

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries.

MITAuto-check passedData & Analytics

Install Explaining Machine Learning Models

skills CLI
$ npx skills add foryourhealth111-pixel/Vibe-Skills --skill explaining-machine-learning-models -a claude-code

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

GitHub CLI
$ gh skill install foryourhealth111-pixel/Vibe-Skills 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/foryourhealth111-pixel/Vibe-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/bundled/skills/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
3.6k
Token cost
~383 tokens
SKILL.md length
123 words
Files
10 (incl. scripts, references, assets)
Skills in repo
65
Repo updated
First seen
Licence
MIT

At a glance

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries.

  • Tasks that involve Machine learning
  • SKILL.md covers Positioning, When to Use, Not For / Boundaries and Typical Outputs, plus 1 more section
  • Runs Python scripts from its folder

What it does

Explaining Machine Learning Models is an agent skill from foryourhealth111-pixel/Vibe-Skills. Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.

Its SKILL.md is about 380 tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts, reference files and assets (for example `assets/README.md`, `assets/example_explanation.json` and `references/README.md`).

It sits in Data & Analytics, covering Machine learning. The repository describes itself as: Intelligent Skill routing and workflow orchestration for AI agents — +21.12 pp reward, −29.6% tokens on SkillsBench with DeepSeekV4Flash-VE. The licence is MIT.

When your agent uses it

  • Tasks that involve Machine learning

Example prompts

  • “/explaining-machine-learning-models”

Requirements

  • Python 3
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(cmd:*)

What it can do on your machine

Read from SKILL.md and the folder at commit ddcaa2a. 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 4 files in scripts/ (Python), 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.

Context cost

Explaining Machine Learning Models loads about 383 tokens when it runs, and up to ~521 if it reads all its reference files. Until then it costs about 72 tokens; SKILL.md has 123 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~383
With references · SKILL.md plus every file in references/, read only if the agent opens them
~521

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 foryourhealth111-pixel/Vibe-Skills at commit ddcaa2a, republished under its MIT licence (© foryourhealth111-pixel). 123 words, ~383 tokens.

Download SKILL.mdSave it as .claude/skills/explaining-machine-learning-models/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
explaining-machine-learning-models
description
Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Use as an explicit/manual helper once a model already exists, not for training ownership, leakage auditing, or general ML strategy selection.
allowed-tools
Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version
1.0.0
author
Jeremy Longshore <jeremy@intentsolutions.io>
license
MIT

Model Explainability Tool

Positioning

Treat this skill as an explicit/manual helper for interpretability work.

When to Use

Use this skill when:

  • 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.

Not For / Boundaries

  • Model training and hyperparameter search: use scikit-learn
  • Benchmark comparison and threshold selection: use evaluating-machine-learning-models
  • Leakage or prediction-time audits: use ml-data-leakage-guard

Typical Outputs

  • Feature importance or attribution summaries
  • Local explanation workflow for a concrete prediction
  • Notes on caveats, instability, or misleading explanations
  • shap for SHAP-specific workflows
  • evaluating-machine-learning-models when the question is whether the model is good enough

© foryourhealth111-pixel, 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 9 other files (scripts, references, assets) in bundled/skills/explaining-machine-learning-models of foryourhealth111-pixel/Vibe-Skills.

  • SKILL.md
  • assets/README.md
  • assets/example_explanation.json
  • assets/explanation_template.html
  • assets/visualization_styles.css
  • references/README.md
  • scripts/README.md
  • scripts/data_preprocessing.py
  • scripts/explain_model.py
  • scripts/feature_importance.py

Open the folder on GitHubat commit ddcaa2a

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 skillforyourhealth111-pixel/Vibe-Skills3.6k—~383Automated safety check: PassMIT
Scikit LearnzLanqing/codex-claude-academic-skills4.6k17 repos~3.9kAutomated safety check: PassBSD-3-Clause
Senior Data ScientistRaidriar7170/hermes-skilleval1256 repos~1.4kAutomated safety check: PassMIT
Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill188—~4kAutomated safety check: PassMIT
Retention Analysisliangdabiao/claude-data-analysis-ultra-main2901 repos~1.3kAutomated safety check: NotesNone
Geomlitalo-goncalves/geoML109—~4.2kAutomated safety check: PassGPL-3.0

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Categories

Questions about Explaining Machine Learning Models

What does Explaining Machine Learning Models do?

Explain trained machine learning models through feature attribution, local explanations, and behavior summaries. Explaining Machine Learning Models is an agent skill from foryourhealth111-pixel/Vibe-Skills. Explain trained machine learning models through feature attribution, local explanations, and behavior summaries.

When should I use Explaining Machine Learning Models?

Explaining Machine Learning Models fits situations like: tasks that involve Machine learning.

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

Run `npx skills add foryourhealth111-pixel/Vibe-Skills --skill explaining-machine-learning-models -a claude-code`. Or copy the skill folder (bundled/skills/explaining-machine-learning-models in foryourhealth111-pixel/Vibe-Skills) 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 foryourhealth111-pixel/Vibe-Skills --skill explaining-machine-learning-models -a codex`. Or copy the skill folder (bundled/skills/explaining-machine-learning-models in foryourhealth111-pixel/Vibe-Skills) 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 foryourhealth111-pixel/Vibe-Skills --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?

Going by SKILL.md and its folder, Explaining Machine Learning Models needs Python for the scripts in its folder. Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*).

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 383 tokens (SKILL.md is roughly 1.5k 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 138 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, 4.6k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars) and Retention Analysis (liangdabiao/claude-data-analysis-ultra-main, 290 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?

foryourhealth111-pixel (a GitHub user) maintains it in foryourhealth111-pixel/Vibe-Skills, which has 3,604 GitHub stars. The repository holds 65 skills in this directory. The repository was last updated on August 31, 2026.

Source: foryourhealth111-pixel/Vibe-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.