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Ml Model Explanation

Checked
aj-geddes·vlatest·ai-ml·MIT

Helps with machine learning, data modeling, explanation as part of agent workflows workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it ML Model Explanation, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add aj-geddes/useful-ai-prompts --skill "ML Model Explanation" -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

aj-geddes

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned aj-geddes/useful-ai-prompts at 3f5182cfd7.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
3f5182cfd7
Manifest
skills/ml-model-explanation/SKILL.md
Scanned
3 files, 382 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to analyze machine learning model outputs and generate clear, human-readable explanations of how predictions were made. It translates complex algorithmic logic into accessible language for non-technical stakeholders.

When to use it

  • Explain credit denial decisions in automated loan approval systems to applicants.
  • Clarify why a medical diagnostic AI flagged specific symptoms as high-risk.
  • Justify content moderation rejections to users who do not understand the underlying policy logic.
  • Simplify technical model behavior for executive summaries or board presentations.

Key capabilities

  • Interprets raw model outputs and internal decision pathways.
  • Generates natural language summaries of prediction drivers.
  • Bridges the gap between complex algorithms and human understanding.

Example prompts

  • "Explain why this specific customer was denied a loan based on the model's recent output."
  • "Summarize the key factors influencing the AI's diagnosis for this patient case."
  • "Translate these technical model confidence scores into plain English for a client report."

Tips & gotchas

Ensure the input data provided to the agent includes sufficient context about the model's training or specific prediction logic, as the explanation quality depends heavily on available metadata. This skill is not a substitute for auditing the model itself but rather for communicating its results.