Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add foryourhealth111-pixel/Vibe-Skills --skill ml-pipeline-workflowgit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/ml-pipeline-workflow)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/ml-pipeline-workflow"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/ml-pipeline-workflow/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/ml-pipeline-workflow"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/ml-pipeline-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00048 | $0.01479 |
| Opus 5 | $0.00024 | $0.00740 |
| Sonnet 5 | $0.00010 | $0.00296 |
| Haiku 4.5 | $0.00005 | $0.00148 |
Grade A, and why
ml-pipeline-workflow scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
5 near-identical copies found in the catalogue:
- ml-pipeline-workflow — 100% identical, 2 lines differ
- ml-pipeline-workflow — 100% identical, 0 lines differ
- ml-pipeline-workflow — 97% identical, 3 lines differ
- wshobson-ml-pipeline-workflow — 95% identical, 4 lines differ
- ml-pipeline-workflow — 91% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
-
Pipeline Architecture
- End-to-end workflow design
- DAG orchestration patterns (Airflow, Dagster, Kubeflow)
- Component dependencies and data flow
- Error handling and retry strategies
-
Data Preparation
- Data validation and quality checks
- Feature engineering pipelines
- Data versioning and lineage
- Train/validation/test splitting strategies
-
Model Training
- Training job orchestration
- Hyperparameter management
- Experiment tracking integration
- Distributed training patterns
-
Model Validation
- Validation frameworks and metrics
- A/B testing infrastructure
- Performance regression detection
- Model comparison workflows
-
Deployment Automation
- Model serving patterns
- Canary deployments
- Blue-green deployment strategies
- Rollback mechanisms
Reference Documentation
See the references/ directory for detailed guides:
- data-preparation.md - Data cleaning, validation, and feature engineering
- model-training.md - Training workflows and best practices
- model-validation.md - Validation strategies and metrics
- model-deployment.md - Deployment patterns and serving architectures
Assets and Templates
The assets/ directory contains:
- pipeline-dag.yaml.template - DAG template for workflow orchestration
- training-config.yaml - Training configuration template
- validation-checklist.md - Pre-deployment validation checklist
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 246 lines · 48 tokens per session scan A 79d76e607424
ml-pipeline-workflow is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 12d ago), licensed Apache-2.0. It adds 48 tokens to every session and 1,479 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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