Agent skill · software engineering · sickn33
ml-pipeline-workflow
Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.
Why this skill is useful
Adds executable templates and detailed workflows for building robust MLOps pipelines that are not commonly found in public documentation.
What it needs
Requires airflow, dagster, kubeflow installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
Supporting material availability This catalog copy contains the inline guidance below, but does not include the following supporting paths. They are external prerequisites, not bundled files: <support-root>/assets/pipeline-dag.yaml.template <support-root>/references/data-preparation.md <support-root>/references/model-training.md Ask for the original supporting material or an existing installation before using a step that depends on these paths; this catalog does not identify a verified download source for them. Resolve each path relative to that installation, inspect its contents, and verify any applicable license and execution prerequisites. Do not run a command or claim a referenced procedure was followed until its required file exists. Do not fabricate the missing scripts or treat an unrelated file with the same name as equivalent. Independent steps fully documented here can proceed within the user’s authorized scope; report any dependent step as unavailable. ML Pipeline Workflow Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. Do not use this skill when The task is unrelated to ml pipeline workflow You need a different domain or tool outside this scope Instructions Clarify goals, constraints, and required inputs. Apply relevant best practices and validate outcomes. Provide actionable steps and verification. Overview This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring. Use this skill when Building new ML pipelines from scratch Designing workflow orchestration for ML systems Implementing data → model → deployment automation Setting up reproducible training workflows Creating DAG-based ML orchestration Integrating ML components into production systems What This Skill Provides Core Capabilities 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/ml-pipeline-workflow