Agent skill

Shap Explainer

by majiayu000 in majiayu000/claude-skill-registry

SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.

Install Shap Explainer

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill shap-explainer -a claude-code

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

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry shap-explainer --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/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-ml/shap-explainer .claude/skills/shap-explainer && 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/

Paste into Claude Code

Install the "shap-explainer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/shap-explainer into .claude/skills/shap-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shap-explainer", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/shap-explainer

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill shap-explainer -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry shap-explainer --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/ai-ml/shap-explainer .agents/skills/shap-explainer && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "shap-explainer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/shap-explainer into .agents/skills/shap-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shap-explainer", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill shap-explainer -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry shap-explainer --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/ai-ml/shap-explainer .cursor/skills/shap-explainer && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "shap-explainer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/shap-explainer into .cursor/skills/shap-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shap-explainer", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/majiayu000/claude-skill-registry.git --path skills/ai-ml/shap-explainer

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill shap-explainer -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry shap-explainer --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/ai-ml/shap-explainer .gemini/skills/shap-explainer && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "shap-explainer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/shap-explainer into .gemini/skills/shap-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shap-explainer", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry shap-explainer

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill shap-explainer -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/ai-ml/shap-explainer .github/skills/shap-explainer && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "shap-explainer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/shap-explainer into .github/skills/shap-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shap-explainer", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add majiayu000/claude-skill-registry --skill shap-explainer -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install majiayu000/claude-skill-registry shap-explainer --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/ai-ml/shap-explainer .opencode/skills/shap-explainer && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "shap-explainer" agent skill from https://github.com/majiayu000/claude-skill-registry/tree/main/skills/ai-ml/shap-explainer into .opencode/skills/shap-explainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "shap-explainer", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: shap-explainer
GitHub stars: 666
Used in: 1 other repo
Token cost: ~872 tokens
SKILL.md length: 80 words
Files: 2
Skills in repo: 1,273
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.

Tasks that involve Machine learning
SKILL.md covers Overview, Capabilities, Target Processes and Tools and Libraries, plus 3 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Shap Explainer is an agent skill from majiayu000/claude-skill-registry. SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.

Its SKILL.md is about 870 tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).

It sits in Data & Analytics, covering Machine learning. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.

When your agent uses it

Tasks that involve Machine learning

Example prompts

“/shap-explainer”

Requirements

Pre-approved tools (allowed-tools): Read, Write, Bash, Glob, Grep

What it can do on your machine

Read from SKILL.md and the folder at commit 2d14a69. 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
- Bash
- Glob
- Grep
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are json and javascript).
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

Shap Explainer loads about 872 tokens when it runs. Until then it costs about 30 tokens; SKILL.md has 80 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~30
When it runs · the whole SKILL.md, loaded when a task matches: ~872

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: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
```
allowed-tools: Read, Write, Bash, Glob, Grep
```

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); files beside SKILL.md are not scanned.

SKILL.md

The full file from majiayu000/claude-skill-registry at commit 2d14a69, republished under its MIT licence (© majiayu000). 80 words, ~872 tokens.

Download SKILL.mdSave it as .claude/skills/shap-explainer/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.

name: shap-explainer
description: SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.
allowed-tools: Read, Write, Bash, Glob, Grep

shap-explainer

Overview

SHAP-based model explainability skill for feature attribution, summary plots, interaction analysis, and model interpretation.

Capabilities

TreeExplainer for tree-based models (XGBoost, LightGBM, Random Forest)
DeepExplainer for neural networks
KernelExplainer for model-agnostic explanations
Summary, dependence, and force plots
Interaction value computation
Cohort-based analysis
Waterfall and bar plots
Expected value analysis

Target Processes

Model Interpretability and Explainability Analysis
Model Evaluation and Validation Framework
A/B Testing Framework for ML Models

Tools and Libraries

SHAP
matplotlib
numpy

Input Schema

json

{
  "type": "object",
  "required": ["modelPath", "dataPath", "explainerType"],
  "properties": {
    "modelPath": {
      "type": "string",
      "description": "Path to the trained model"
    },
    "dataPath": {
      "type": "string",
      "description": "Path to data for explanation"
    },
    "explainerType": {
      "type": "string",
      "enum": ["tree", "deep", "kernel", "linear", "gradient"],
      "description": "Type of SHAP explainer to use"
    },
    "analysisConfig": {
      "type": "object",
      "properties": {
        "numSamples": { "type": "integer" },
        "backgroundSamples": { "type": "integer" },
        "featureNames": { "type": "array", "items": { "type": "string" } },
        "outputIndex": { "type": "integer" }
      }
    },
    "plotConfig": {
      "type": "object",
      "properties": {
        "plotTypes": {
          "type": "array",
          "items": { "type": "string", "enum": ["summary", "bar", "waterfall", "force", "dependence", "interaction"] }
        },
        "maxFeatures": { "type": "integer" },
        "outputDir": { "type": "string" }
      }
    }
  }
}

Output Schema

json

{
  "type": "object",
  "required": ["status", "shapValues"],
  "properties": {
    "status": {
      "type": "string",
      "enum": ["success", "error"]
    },
    "shapValues": {
      "type": "string",
      "description": "Path to SHAP values file"
    },
    "expectedValue": {
      "type": "number"
    },
    "featureImportance": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "feature": { "type": "string" },
          "importance": { "type": "number" },
          "rank": { "type": "integer" }
        }
      }
    },
    "plots": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "type": { "type": "string" },
          "path": { "type": "string" }
        }
      }
    },
    "interactions": {
      "type": "object",
      "description": "Top feature interactions"
    }
  }
}

Usage Example

javascript

{
  kind: 'skill',
  title: 'Generate SHAP explanations',
  skill: {
    name: 'shap-explainer',
    context: {
      modelPath: 'models/xgboost_model.pkl',
      dataPath: 'data/test.csv',
      explainerType: 'tree',
      analysisConfig: {
        numSamples: 1000,
        backgroundSamples: 100
      },
      plotConfig: {
        plotTypes: ['summary', 'bar', 'dependence'],
        maxFeatures: 20,
        outputDir: 'explanations/'
      }
    }
  }
}

Files

SKILL.md and 1 other file in skills/ai-ml/shap-explainer of majiayu000/claude-skill-registry.

SKILL.md
metadata.json

Open the folder on GitHubat commit 2d14a69

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.

a5c-ai/babysitterlibrary/specializations/data-science-ml/skills/shap-explainer

Compare with similar skills

Shap Explainer 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.

Shap Explainer compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Shap Explainer this skillmajiayu000/claude-skill-registry	666	1 repos	~872	Automated safety check: Notes	MIT	today
Scikit LearnzLanqing/codex-claude-academic-skills	4.6k	17 repos	~3.9k	Automated safety check: Pass	BSD-3-Clause	4 mo ago
Senior Data ScientistRaidriar7170/hermes-skilleval	125	6 repos	~1.4k	Automated safety check: Pass	MIT	12 days ago
Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill	188	—	~4k	Automated safety check: Pass	MIT	3 mo ago
Retention Analysisliangdabiao/claude-data-analysis-ultra-main	290	1 repos	~1.3k	Automated safety check: Notes	None	5 mo ago
Geomlitalo-goncalves/geoML	109	—	~4.2k	Automated safety check: Pass	GPL-3.0	5 days ago

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Questions about Shap Explainer

What does Shap Explainer do?

SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis. Shap Explainer is an agent skill from majiayu000/claude-skill-registry. SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.

When should I use Shap Explainer?

Shap Explainer fits situations like: tasks that involve Machine learning.

How do I install Shap Explainer in Claude Code?

Run `npx skills add majiayu000/claude-skill-registry --skill shap-explainer -a claude-code`. Or copy the skill folder (skills/ai-ml/shap-explainer in majiayu000/claude-skill-registry) into .claude/skills/shap-explainer in your project. Claude Code loads it when a task matches its description.

How do I install Shap Explainer in Codex?

Run `npx skills add majiayu000/claude-skill-registry --skill shap-explainer -a codex`. Or copy the skill folder (skills/ai-ml/shap-explainer in majiayu000/claude-skill-registry) into .agents/skills/shap-explainer in your project. Codex loads it when a task matches its description.

Can I use Shap Explainer 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 majiayu000/claude-skill-registry --skill shap-explainer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/shap-explainer, .gemini/skills/shap-explainer, .github/skills/shap-explainer and .opencode/skills/shap-explainer in your project.

What does Shap Explainer need to run?

SKILL.md names no scripts, command-line tools or credentials: Shap Explainer is instructions for the agent only. Its frontmatter pre-approves these tools: Read, Write, Bash, Glob, Grep.

Does Shap Explainer 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 Shap Explainer safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Shap Explainer use?

Shap Explainer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Shap Explainer use?

About 872 tokens (SKILL.md is roughly 3.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Shap Explainer?

Skills that share tags, products or a category with Shap Explainer: 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 Shap Explainer?

majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 1,273 skills in this directory. The repository was last updated on October 7, 2026.

Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Shap Explainer

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

shap-explainer

Overview

Capabilities

Target Processes

Tools and Libraries

Input Schema

Output Schema

Usage Example

Files

Used in 1 other repository

Compare with similar skills

Similar skills

More from majiayu000/claude-skill-registry

Related

Questions about Shap Explainer