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 HermeticOrmus/LibreUIUX-Claude-Code --skill ml-pipeline-workflowgit clone --depth 1 https://github.com/HermeticOrmus/LibreUIUX-Claude-CodeWrote 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/hermeticormus/libreuiux-claude-code/ml-pipeline-workflow)<a href="https://agentmods.dev/skills/hermeticormus/libreuiux-claude-code/ml-pipeline-workflow"><img src="https://agentmods.dev/badge/skills/hermeticormus/libreuiux-claude-code/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/hermeticormus/libreuiux-claude-code/ml-pipeline-workflow"><img src="https://agentmods.dev/badge/skills/hermeticormus/libreuiux-claude-code/ml-pipeline-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 5.5 | $0.00019 | $0.00592 |
| Sonnet 5.5 | $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 29d 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.
This is a copy
100% identical to ml-pipeline-workflow — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 29d ago First seen · 246 lines · 48 tokens per session scan A 79d76e607424
ml-pipeline-workflow is a skill published in the GitHub repository HermeticOrmus/LibreUIUX-Claude-Code (115 stars, last pushed 14d ago), licensed MIT. It adds 48 tokens to every session and 1,479 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to ml-pipeline-workflow, differing in 0 lines, and is treated as a copy.
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