Machine Learning Ops Ml Pipeline

Name: Machine Learning Ops Ml Pipeline
Author: sickn33

Listed

sickn33·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 sickn33/agentic-awesome-skills --skill machine-learning-ops-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

sickn33

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 28 Sept 2026

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

Declares what it does: failed
No SKILL.md was found in the skill's directory.
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 sickn33/antigravity-awesome-skills at 5a4cdd9df8.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit: 5a4cdd9df8
Manifest: skills/machine-learning-ops-ml-pipeline/SKILL.md
Scanned: 0 files
Copies in repo: 3 — all of them scanned

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What each check looks for →

About this skill

What it does

This skill automates the creation and management of machine learning pipelines. It allows users to define, build, test, deploy, and monitor ML models in a repeatable and scalable manner. The focus is on streamlining the entire ML lifecycle from data ingestion to model serving.

When to use it

Automating repetitive tasks involved in training and deploying new versions of an existing machine learning model.
Creating standardized pipelines for different types of machine learning projects within an organization.
Setting up a continuous integration/continuous delivery (CI/CD) system specifically tailored for ML models.
Managing the lifecycle of multiple machine learning models, including versioning and rollback capabilities.

Key capabilities

Pipeline definition using code
Automated model training
Model deployment
Continuous monitoring
Version control

Example prompts

"Create a pipeline to train a sentiment analysis model on the latest dataset."
"Deploy the current version of the fraud detection model to production."
"Monitor the performance of the recommendation engine and alert me if accuracy drops below 90%."

Tips & gotchas

This skill requires familiarity with machine learning concepts and DevOps practices. Successfully using this skill depends on having a well-defined data schema and understanding of your ML model's dependencies.