What is model-workflows?

A guide for choosing supported workflows for cached AI models, single-GPU model serving, and fine-tuning preparation. Fine-tuning means adapting an existing model with additional training data; this guide does not provide a training procedure.

Use it to decide whether to manage model files, serve a fixed model snapshot on one GPU, or check prerequisites for fine-tuning in DeepOps.

It separates model preparation, serving, and training readiness so a readiness check is not mistaken for a completed training run, and unsupported multi-node work is stopped early.

model-workflows — Skill for Claude Code and Codex by NVIDIA/deepops

About the project

DeepOps is a collection of automation tools for deploying and managing compute clusters that use NVIDIA GPUs with Kubernetes or Slurm. It supports environments ranging from full data-center clusters to individual machines needing GPU drivers, Docker, and NVIDIA container support.

NVIDIA/deepops · 1,485 stars · on GitHub

Install

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.

Any agent

npx skills add NVIDIA/deepops --skill model-workflows

Clone the repo

git clone --depth 1 https://github.com/NVIDIA/deepops

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/nvidia/deepops/model-workflows/github.svg)](https://agentmods.dev/skills/nvidia/deepops/model-workflows)

Your own site

<a href="https://agentmods.dev/skills/nvidia/deepops/model-workflows"><img src="https://agentmods.dev/badge/skills/nvidia/deepops/model-workflows/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/nvidia/deepops/model-workflows"><img src="https://agentmods.dev/badge/skills/nvidia/deepops/model-workflows.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 729 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00038	$0.00729
Opus 5.5	$0.00015	$0.00292
Sonnet 5.5	$0.00008	$0.00146
Haiku 4.5	$0.00004	$0.00073

Measured yesterday against content hash 29e0e87c0ec7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

model-workflows 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 yesterday.

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.

skills/model-workflows/SKILL.md · 53 lines

How it starts

The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Choose a model workflow

This is the umbrella entry point, not a launcher. Start from the repository root. Identify the requested outcome before running anything: cached inputs, working inference, or training are different outcomes with different evidence.

Select the supported path

Requested outcome	Procedure	Evidence
Prepare or repair shared model inputs	Manage model cache	Cache validator exit `0` and `ok: true`, as the real consumer. Only named-file readability is proven.
Serve a pinned snapshot on one GPU	Serve model	Cache gate first, then serving validator exit `0` and `ok: true`. One bounded completion, not training or performance validation.
Check fine-tuning prerequisites	Fine-tuning readiness	Readiness validator exit `0`, `status: ready`, `training_validated: false`. Optional one-GPU container probe is off by default.
Actually fine-tune a model	Stop: no supported training recipe or training-result validator is provided here yet.	Readiness is not training success. Obtain a reviewed recipe and explicit success criteria before proceeding.
Multi-node serving or training	Stop: unsupported by these skills.	Do not infer distributed support from a successful single-node check.

Common preflight

Confirm the operator's goal, model license, repository ID, immutable full revision, complete required-file list, cache path and real reader UID/group. Do not accept a directory or config.json alone as complete model coverage.
Confirm the target and authorization for each action. Cache preparation, downloads, package installation, GPU allocation, service startup and inference requests are separate actions. A skill or validator grants none of them.
Reuse the existing cache interface: share only HF_HUB_CACHE, use a designated writer and pass the local snapshot path to consumers. Mount the whole cache read-only; keep authentication and framework caches private. Never put training outputs or checkpoints in the shared input cache.
Follow the selected skill's failure branches. Stop on absent authorization, incomplete files, space pressure, unknown endpoint ownership or required site authentication. Never remove controls, change infrastructure or delete other users' files to turn a failure into a pass.

Read the full file on GitHub · 53 lines

Changes

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.

yesterday First seen · 53 lines · 38 tokens per session scan A 29e0e87c0ec7

Subscribe to this mod's changes

model-workflows is a skill published in the GitHub repository NVIDIA/deepops (1,485 stars, last pushed yesterday), licensed BSD-3-Clause. It adds 38 tokens to every session and 729 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-11-06.

Data and AI

model-workflows next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
model-workflowsNVIDIA/deepops	Skill	1.5k	yesterday	BSD-3-Clause	A	38	Claude Code, Codex
agent-platform-model-registrygoogle/skills	Skill	22k	yesterday	Apache-2.0	A	60	Claude Code, Codex
foundry-config-setupmicrosoft/agent-framework	Skill	15k	yesterday	MIT	A	65	Claude Code, Codex
google-cloud-solution-agentic-analytics-spark-knowledge-cataloggoogle/skills	Skill	22k	yesterday	Apache-2.0	A	132	Claude Code, Codex
training-checkwanshuiyin/Auto-claude-code-research-in-sleep	Skill	19k	3d ago	MIT	A	35	Claude Code
nemo-automodel-launcher-configNVIDIA/skills	Skill	3.9k	yesterday	Apache-2.0	A	30	Claude Code
finetuningmicrosoft/skills	Skill	3.2k	yesterday	MIT	A	132	Claude Code