ML Pipeline Creation

ML Pipeline Creation is an AI Engineering skill for Claude Code, published by seb1n in awesome-ai-agent-skills.

174 stars32 forkson seb1n/awesome-ai-agent-skillsAdded 2026/09/07+2% in starsRepository updated 2026/08/09

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Install in seconds

Install ML Pipeline Creation

Copy ML Pipeline Creation into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/seb1n/awesome-ai-agent-skills/tree/main/ai-ml-operations/ml-pipeline-creation ~/.claude/skills/ml-pipeline-creation

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/seb1n/awesome-ai-agent-skills.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: ai-ml-operations/ml-pipeline-creation/SKILL.md in seb1n/awesome-ai-agent-skills
Installs to: ~/.claude/skills/ml-pipeline-creation
Collection: One of 25 skills cataloged from this repository
Category: AI Engineering — 3475 skills

What ML Pipeline Creation does

ML Pipeline Creation designs reproducible machine-learning workflows with explicit data, training, evaluation, and deployment gates. Use it when turning model scripts into an orchestrated pipeline or connecting existing pipeline components safely.

ML Pipeline Creation is cataloged under AI Engineering on DirSkills. ML Pipeline Creation comes from a repository tagged agent-skills, ai-agent-skills, ai-agents, awesome-list and claude-code.

Documentation

README

Excerpt

ML Pipeline Creation

Build reproducible ML workflows whose inputs, outputs, lineage, and promotion criteria are explicit. Prefer the project's existing orchestrator and conventions; do not introduce a platform merely to demonstrate one.

Required Inputs

Business objective and measurable model acceptance criteria
Data sources, ownership, sensitivity, and expected refresh cadence
Existing preprocessing, training, evaluation, and serving code
Target environments and available orchestration or CI system
Compute, cost, latency, reproducibility, and compliance constraints

If critical details are missing, state assumptions and design a platform-neutral pipeline before selecting an implementation.

This is the opening of the README. Read the full README on GitHub.

Frequently asked about ML Pipeline Creation

What else does seb1n publish alongside ML Pipeline Creation?
ML Pipeline Creation is one of 25 skills that DirSkills catalogs from seb1n/awesome-ai-agent-skills, the repository it ships in. Its siblings there include API Design, API Integration and Agent Evaluation. Each one is a separate skill with its own page in this directory, installs the same way ML Pipeline Creation does, and is maintained by seb1n in that same repository. The rest of the collection is listed on the seb1n/awesome-ai-agent-skills page.
How does ML Pipeline Creation compare to other AI Engineering skills?
ML Pipeline Creation ranks #3226 by stars among the 3475 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of ML Pipeline Creation against them. Open each page to compare what they document and how they install.