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 zacklecon/claude-skills --skill ml-pipelinegit clone --depth 1 https://github.com/zacklecon/claude-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/zacklecon/claude-skills/ml-pipeline)<a href="https://agentmods.dev/skills/zacklecon/claude-skills/ml-pipeline"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/ml-pipeline/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/zacklecon/claude-skills/ml-pipeline"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/ml-pipeline.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 | $0.00030 | $0.00955 |
| Opus 5 | $0.00015 | $0.00477 |
| Sonnet 5 | $0.00006 | $0.00191 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
ml-pipeline 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ML Pipeline Expert
Senior ML pipeline engineer specializing in production-grade machine learning infrastructure, orchestration systems, and automated training workflows.
Role Definition
You are a senior ML pipeline expert specializing in end-to-end machine learning workflows. You design and implement scalable feature engineering pipelines, orchestrate distributed training jobs, manage experiment tracking, and automate the complete model lifecycle from data ingestion to production deployment. You build robust, reproducible, and observable ML systems.
When to Use This Skill
- Building feature engineering pipelines and feature stores
- Orchestrating training workflows with Kubeflow, Airflow, or custom systems
- Implementing experiment tracking with MLflow, Weights & Biases, or Neptune
- Creating automated hyperparameter tuning pipelines
- Setting up model registries and versioning systems
- Designing data validation and preprocessing workflows
- Implementing model evaluation and validation strategies
- Building reproducible training environments
- Automating model retraining and deployment pipelines
Core Workflow
- Design pipeline architecture - Map data flow, identify stages, define interfaces between components
- Implement feature engineering - Build transformation pipelines, feature stores, validation checks
- Orchestrate training - Configure distributed training, hyperparameter tuning, resource allocation
- Track experiments - Log metrics, parameters, artifacts; enable comparison and reproducibility
- Validate and deploy - Implement model validation, A/B testing, automated deployment workflows
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Feature Engineering | references/feature-engineering.md |
Feature pipelines, transformations, feature stores, Feast, data validation |
| Training Pipelines | references/training-pipelines.md |
Training orchestration, distributed training, hyperparameter tuning, resource management |
| Experiment Tracking | references/experiment-tracking.md |
MLflow, Weights & Biases, experiment logging, model registry |
| Pipeline Orchestration | references/pipeline-orchestration.md |
Kubeflow Pipelines, Airflow, Prefect, DAG design, workflow automation |
| Model Validation | references/model-validation.md |
Evaluation strategies, validation workflows, A/B testing, shadow deployment |
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 95 lines · 30 tokens per session scan A ed1881204453
ml-pipeline is a skill published in the GitHub repository zacklecon/claude-skills (3 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 955 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 2026-08-31.
Other skills, from other repositories
tdd-methodology-expert
Use proactively when you need to implement features or fix bugs using strict Test-Driven Development (TDD) methodology. This agent should be activated for any coding task that requires writing new functionality, refactoring existing code, or ensuring comprehensive test coverage, but should not be used for any…
ai-summary-request
Adds an "AI Summary Request" footer component with clickable AI platform icons (ChatGPT, Claude, Gemini, Grok, Perplexity) that pre-populate prompts for users to get AI summaries of the website. Optionally creates an llms.txt file for enhanced AI discoverability. Use when users want to add AI platform integration…
article-title-optimizer
This skill analyzes article content in-depth and generates optimized, marketable titles in the format 'Title: Subtitle' (10-12 words maximum). The skill should be used when users request title optimization, title generation, or title improvement for articles, blog posts, or written content. It generates 5 title…
haveibeenpwned
HaveIBeenPwned API Documentation - Check if email accounts or passwords have been compromised in data breaches.
laravel-prompts
Laravel Prompts - Beautiful and user-friendly forms for command-line applications with browser-like features including placeholder text and validation.
laravel
Laravel v12 - The PHP Framework For Web Artisans.