ml-pipeline-workflow SKILL.md skillA
ml-pipeline-workflow from wshobson/agents is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add wshobson/agents/ml-pipeline-workflow@git:20260316.24df162Pins git:20260316.24df162 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- wshobson/agents
- Path
plugins/machine-learning-ops/skills/ml-pipeline-workflow/SKILL.md- Versions recorded
- 3
- Latest version
- git:20260316.24df162, committed Mar 16, 2026
- First version recorded
- Oct 17, 2025
- Size
- 7,095 bytes
- Repository stars
- 42,320
- License
- MIT
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# ML Pipeline Workflow Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment. ## Overview This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring. ## When to Use This Skill - 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 …
Read the whole file at its exact version.
How to install
mdr add wshobson/agents/ml-pipeline-workflow@sha256:3049880901f70761The line at the top of this page pins the label git:20260316.24df162, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_dx6zkp72uomyuj3r)
5 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 3 versions of this file, between Oct 17, 2025 and Mar 16, 2026, about one every 75 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260316.24df162 latest | 2026-03-16 | 24df162 | 7,095 B | A | view · diff |
| git:20260119.5684887 | 2026-01-19 | 5684887 | 7,032 B | A | view · diff |
| git:20251017.65e5cb0 | 2025-10-17 | 65e5cb0 | 7,030 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (7095 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
wshobson/agents · 42,320 stars · license MIT · pushed 2026-11-08 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_dx6zkp72uomyuj3r GET https://markdownregistry.com/api/v1/resolve?ref=wshobson/agents/ml-pipeline-workflow GET https://markdownregistry.com/api/v1/blob/3049880901f70761137ef8818c6e0e5c357e5d108b82be48003e98c882a31c8b
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.