Agent skill · software engineering · aiskillstore
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 and deploying ML pipelines that aren't commonly found in public documentation.
What it needs
Requires airflow, dagster, kubeflow installed locally. About 4k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
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. If detailed examples are required, open resources/implementation-playbook.md. 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. Pipeline Architecture End-to-end workflow design DAG orchestration patterns (Airflow, Dagster, Kubeflow) Component dependencies and data flow Error handling and retry strategies 2. Data Preparation Data validation and quality checks Feature engineering pipelines Data versioning and lineage Train/validation/test splitting strategies 3. Model Training Training job orchestration Hyperparameter management Experiment tracking integration Distributed training patterns 4. Model Validation Validation frameworks and metrics A/B testing infrastructure Performance regression detection Model comparison workflows 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/ml-pipeline-workflow