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Skills ml-pipeline-workflow
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ml-pipeline-workflow
Content revision r2 Safe ⚙️ External commands
Design Production ML Pipelines
Production ML workflows often fail because data, training, validation, deployment, and monitoring are designed separately. This skill provides a structured lifecycle and orchestration guidance.
Supports: Claude Codex Code(CC)
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Agent request
Review the Skillstore skill "ml-pipeline-workflow" from https://skillstore.io/skills/sickn33-ml-pipeline-workflow.md and its manifest at https://skillstore.io/api/skills/sickn33-ml-pipeline-workflow/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "ml-pipeline-workflow". Design a batch churn-model pipeline with daily training and weekly deployment.
Expected outcome:
- Daily: ingest snapshots, validate schema and quality, build versioned features, train candidates, and record experiments.
- Weekly: compare the best candidate with production, require approval, deploy a canary, monitor metrics, and preserve rollback.
Using "ml-pipeline-workflow". How should a team detect model regressions before deployment?
Expected outcome:
Compare candidates against a fixed baseline on versioned data. Gate deployment on predictive quality, fairness, latency, stability, and business thresholds.
Using "ml-pipeline-workflow". Review a pipeline that retrains automatically after drift alerts.
Expected outcome:
Validate alert quality and incoming data before training. Require candidate comparison, controlled rollout, monitoring, and rollback before replacing production.
Security Audit
Safev5 • 8/4/2026 Open versioned report
All ten static findings are false positives caused by Markdown inline code markers and fenced code blocks. The reviewed content contains guidance and declarative examples, not shell execution.
1
Files scanned
266
Lines analyzed
0
Review items
0
False positives ignored
Risk Factors
No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
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<iframe src="https://skillstore.io/embed/skills/sickn33-ml-pipeline-workflow.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
sickn33. (2026). ml-pipeline-workflow security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/sickn33-ml-pipeline-workflow/audits/5BibTeX citation
@techreport{sickn33-sickn33-ml-pipeline-workflow-2026,
author = {sickn33},
title = {ml-pipeline-workflow security audit report (audit version 5)},
institution = {Skillstore},
year = {2026},
number = {5},
url = {https://skillstore.io/skills/sickn33-ml-pipeline-workflow/audits/5},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "ml-pipeline-workflow security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
- name: "sickn33"
date-released: "2026-08-04"
url: "https://skillstore.io/skills/sickn33-ml-pipeline-workflow/audits/5"
identifiers:
- type: other
value: "skillstore:sickn33-ml-pipeline-workflow:audit:5"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
Highest Skillstore Score
Author / source Skillstore Score Evidence Confidence Skillstore usage Updated
wshobson Recommended
wshobson-ml-pipeline-workflow
Skillstore Score 79
Evidence Confidence High
Skillstore usage 12
Updated
2026-09-09
sickn33 Current
sickn33-ml-pipeline-workflow
Skillstore Score 78
Evidence Confidence High
Skillstore usage 13
Updated
2026-09-09
Skillstore Score
Why this score Evidence Confidence: High 55
Architecture
85
Maintainability
87
Content
69
Community
83
Spec Compliance
What You Can Build
Plan a New ML Platform
Map lifecycle stages, dependencies, validation gates, and deployment controls before implementation.
Operationalize a Research Model
Convert an experimental training process into a reproducible workflow with tracking, validation, and rollback.
Review Pipeline Reliability
Assess an existing workflow for observability, idempotency, lineage, retries, and production monitoring.
Try These Prompts
Outline a Basic Pipeline
Design a simple ML pipeline for [use case]. Include ingestion, validation, training, evaluation, deployment, and monitoring stages.
Choose an Orchestrator
Compare Airflow, Dagster, Kubeflow, and Prefect for [environment]. Recommend one based on scale, infrastructure, team skills, and operational needs.
Add Production Controls
Review this pipeline design: [design]. Add data checks, lineage, experiment tracking, approval gates, retries, observability, and rollback controls.
Design Continuous Training
Create a continuous training architecture for [model]. Define triggers, data validation, comparison gates, canary rollout, drift monitoring, and automated rollback.
Best Practices
- Version data, code, configuration, and model artifacts together for reproducibility.
- Make every stage idempotent, independently testable, observable, and retry-aware.
- Use explicit validation and approval gates before production deployment.
Avoid
- Do not deploy a newly trained model without baseline comparison and rollback planning.
- Do not hide data transformations inside training jobs without lineage or versioning.
- Do not automate retraining from drift alerts without validating data and alert quality.
Frequently Asked Questions
Does this skill execute an ML pipeline?
No. It provides architecture guidance, workflow patterns, checklists, and troubleshooting steps.
Which orchestrators does it cover?
It discusses Apache Airflow, Dagster, Kubeflow Pipelines, and Prefect.
Does it support cloud deployments?
It identifies patterns for SageMaker, Vertex AI, Azure ML, Kubernetes, and KServe, but requires environment-specific implementation.
Can it help with continuous training?
Yes. It covers scheduled retraining, drift triggers, validation gates, gradual rollout, monitoring, and rollback.
Does it include ready-to-run templates?
No. The document references template and resource directories that are not included in this package.
What information should I provide?
Provide the model purpose, data sources, scale, infrastructure, compliance needs, deployment target, service objectives, and team constraints.
Developer Details
Author
sickn33License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Repository
https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/ml-pipeline-workflowRef
81e05e636292629114b76cbb3922fbe57672fc02
Maintenance freshness
8/5/2026
Usage
10 downloads · 112 views
File structure
📄 SKILL.md
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