ml-engineering SKILL.md skillA

ml-engineering from stas00/ml-engineering is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned
mdr add stas00/ml-engineering/ml-engineering@git:20261008.fb75494

Pins git:20261008.fb75494 in mdr.lock. Pin the fingerprint instead.

Audit grade
A, 17 of 17 checks passed
Kind
SKILL.md skill
Repository
stas00/ml-engineering
Path
skills/ml-engineering/SKILL.md
Versions recorded
2
Latest version
git:20261008.fb75494, committed Oct 8, 2026
First version recorded
Oct 1, 2026
Size
22,669 bytes
Repository stars
19,571
License
CC-BY-SA-4.0

Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training instabilities, NaN/Inf, and hardware/job failures; checkpointing and fault tolerance; inference performance and memory; debugging multi-node/ multi-GPU hangs; and writing/running tests.…

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# Machine Learning Engineering

Distilled from **Machine Learning Engineering Open Book** by Stas Bekman - source: https://github.com/stas00/ml-engineering (CC BY-SA 4.0). Know-how from training BLOOM-176B, IDEFICS-80B and production RAG and RL training and inference systems. This skill is a condensed index; each section links back to the full chapter for depth, scripts, and benchmarks.

A field-tested, end-to-end guide to **training and serving large models** (LLMs, VLMs, multi-modal, RAG) on real hardware at scale - distilled from actually training BLOOM-176B and IDEFICS-80B and building production inference/RAG systems. It is a practitioner's brain dump: opinionated guidance backed by copy-paste scripts, benchmark tools, and comparison tables, written for the engineers and operators who have to make expensive clusters actually deliver a finished model.
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint
mdr add stas00/ml-engineering/ml-engineering@sha256:e829186940e6d9d1

The line at the top of this page pins the label git:20261008.fb75494, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_35pb7vvvxf4gte34.svg)](https://markdownregistry.com/a/art_35pb7vvvxf4gte34)

2 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 2 versions of this file, between Oct 1, 2026 and Oct 8, 2026, about one every 6 days.

versioncommittedcommitsizeaudit
git:20261008.fb75494 latest2026-10-08 fb75494 22,669 BA view · diff
git:20261001.8d16fa22026-10-01 8d16fa2 22,669 BA view

Audit of the latest version

A  17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (22669 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

stas00/ml-engineering · 19,571 stars · license CC-BY-SA-4.0 · pushed 2026-10-21 · branch master

API

GET https://markdownregistry.com/api/v1/artifacts/art_35pb7vvvxf4gte34
GET https://markdownregistry.com/api/v1/resolve?ref=stas00/ml-engineering/ml-engineering
GET https://markdownregistry.com/api/v1/blob/e829186940e6d9d1c27c0e8b689d17d3b831b9b3c37c1d2738a4799e875eca21

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

More from stas00/ml-engineering

SKILL.md SKILL.md skill
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git:20261001.8d16fa2 · audit C · 19,571 stars
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stas00/ml-engineering · skills/evaluate-cluster/SKILL.md · Evaluates an ML GPU cluster for a cloud trial or acceptance test: environment dump, isolated newest PyTorch, matmul…
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Every file in stas00/ml-engineering

Other files named ml-engineering

ml-engineering SKILL.md skill
magnus919/agent-skills · ml-engineering/SKILL.md · Plan and execute production ML engineering work — classifier versus generative model selection, zero-shot baselines…
git:20261013.b3b4565 · audit A · 140 stars

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