Ml Pipeline Workflow
CheckedHelps with machine learning, pipeline automation as part of automating DevOps pipelines and CI/CD workflows workflows.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
- 1
Run this in your project
terminal$ npx skills add wshobson/agents --skill ml-pipeline-workflow -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
wshobson
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.
Automated safety checks
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 wshobson/agents at 46891e7e60.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- 46891e7e60
- Scanned
- 1 file, 241 lines
About this skill
What it does
The ml-pipeline-workflow skill automates and streamlines machine learning pipeline processes, including data preprocessing, model training, evaluation, and deployment. It supports integration with popular ML frameworks like TensorFlow and PyTorch and provides tools for version control and monitoring of pipeline stages.
When to use it
- Automating repetitive tasks in a machine learning workflow such as data cleaning or feature engineering.
- Setting up continuous integration/continuous deployment (CI/CD) pipelines for model training and deployment.
- Monitoring the performance of ML models during inference and retraining phases.
- Collaborating with teams by ensuring consistent and reproducible pipeline execution across environments.
Key capabilities
- Integration with TensorFlow, PyTorch, and Scikit-learn
- Automated data preprocessing and feature engineering
- Model training, evaluation, and deployment automation
- Pipeline versioning and monitoring
Example prompts
- "Automate the preprocessing of my dataset using standard ML pipeline steps."
- "Set up a CI/CD workflow for model retraining every time new data is added to the repository."
- "Monitor the accuracy of deployed models in production and trigger retraining if it drops below 90%."
Tips & gotchas
Ensure that all dependencies, such as ML frameworks and libraries, are installed before using this skill. It may require configuration for specific cloud environments or data storage solutions.