End-to-end MLOps pipeline orchestration from data ingestion through model deployment and monitoring.
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add wshobson/agents --skill ml-pipeline-workflowCompatible with
Supported agents
Covers five core pipeline stages: data preparation, model training, validation, deployment, and monitoring with DAG orchestration patterns (Airflow, Dagster, Kubeflow)
Includes data validation, feature engineering, experiment tracking integration, and model versioning strategies across the full ML lifecycle
Provides deployment automation patterns including canary releases, blue-green
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionml-pipeline-workflowExecute the skills CLI command in your project's root directory to begin installation:
epx add wshobson/agents --skill ml-pipeline-workflowFetches ml-pipeline-workflow from wshobson/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/ml-pipeline-workflow
Restart Cursor to activate ml-pipeline-workflow. Access via /ml-pipeline-workflowin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
python-code-style
wshobson/agents · Backend · same repo
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react-native-architecture
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monorepo-management
wshobson/agents · Productivity · same repo
ml-paper-writing
davila7/claude-code-templates · AI/ML · same category
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ml-pipeline-workflow is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend ml-pipeline-workflow for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: ml-pipeline-workflow is focused, and the summary matches what you get after install.
ml-pipeline-workflow has been reliable in day-to-day use. Documentation quality is above average for community skills.
ml-pipeline-workflow has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: ml-pipeline-workflow is focused, and the summary matches what you get after install.
Keeps context tight: ml-pipeline-workflow is the kind of skill you can hand to a new teammate without a long onboarding doc.
ml-pipeline-workflow has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: ml-pipeline-workflow is focused, and the summary matches what you get after install.
Useful defaults in ml-pipeline-workflow — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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