Ml Pipeline

Name: Ml Pipeline
Author: jeffallan

Checked

jeffallan·vlatest·devops·MIT

Helps with machine learning, pipeline automation as part of automating DevOps pipelines and CI/CD workflows workflows.

Repository

Install on your platform

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

Run this in your project

terminal

$ npx skills add jeffallan/claude-skills --skill ml-pipeline -a claude-code

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

jeffallan

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 12 Oct 2026

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 jeffallan/claude-skills at 1be15d8064.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: 1be15d8064
Manifest: skills/ml-pipeline/SKILL.md
Scanned: 6 files, 4,206 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 automates and orchestrates machine learning pipelines. It allows users to define a series of steps, such as data preprocessing, model training, evaluation, and deployment, which are then executed in a predefined order. The goal is to streamline the ML development process and reduce manual effort.

When to use it

Automating repetitive tasks in your machine learning workflow.
Deploying new models or updating existing ones with minimal intervention.
Ensuring consistent execution of ML experiments across different environments.
Managing complex ML projects involving multiple data sources and model versions.

Key capabilities

Pipeline definition via configuration files.
Automated task scheduling and dependency management.
Model training and evaluation.
Deployment to various platforms.

Example prompts

"Run the 'model_training' pipeline."
"Deploy the latest model version to production."
"Show me the status of the data preprocessing step in the 'feature_engineering' pipeline."

Tips & gotchas

The skill requires familiarity with machine learning concepts and pipeline architecture. Ensure that all dependencies are correctly configured before running a pipeline.