domino-datasets

domino-datasets is a skill for Claude Code from dominodatalab/domino-claude-plugin. It costs 63 tokens per session (2,706 once invoked), scanned A, original, MIT.

A Domino storage skill for keeping data on persistent, shared filesystems with snapshots. Snapshots are saved versions of a dataset that can be mounted by projects and executions.

In plain words
What is it for?
Use it to create datasets, save and access snapshots, share data across projects, mount dataset paths, and improve access to large files.
Why use it?
It keeps large data available between runs and helps you reproduce work using a known data version. Sharing avoids copying the same data between projects.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the domino-claude-plugin plugin — 23 skills, 4 commands, 3 agents, 1 MCP server shipped together

Good fit Use it to create datasets, save and access snapshots, share data across projects, mount dataset paths, and improve access to large files.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dominodatalab/domino-claude-plugin/datasets
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 dominodatalab/domino-claude-plugin --skill datasets
Clone the repo
git clone --depth 1 https://github.com/dominodatalab/domino-claude-plugin

Made for: Claude Code.

Or install domino-claude-plugin, the plugin that ships this one along with the rest of its 23 skills, 4 commands, 3 agents, 1 MCP server.

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.

agentmods badge for domino-datasets

README.md
[![agentmods](https://agentmods.dev/badge/skills/dominodatalab/domino-claude-plugin/datasets/github.svg)](https://agentmods.dev/skills/dominodatalab/domino-claude-plugin/datasets)
Your own site
<a href="https://agentmods.dev/skills/dominodatalab/domino-claude-plugin/datasets"><img src="https://agentmods.dev/badge/skills/dominodatalab/domino-claude-plugin/datasets/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.

agentmods 80×15 button for domino-datasets

Your own site · 80×15
<a href="https://agentmods.dev/skills/dominodatalab/domino-claude-plugin/datasets"><img src="https://agentmods.dev/badge/skills/dominodatalab/domino-claude-plugin/datasets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,706 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00063 $0.02706
Opus 5 $0.00032 $0.01353
Sonnet 5 $0.00013 $0.00541
Haiku 4.5 $0.00006 $0.00271

Measured 10d ago against content hash ef1d7b80c230, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

domino-datasets 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 10d 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.

skills/datasets/SKILL.md · 393 lines

How it starts

The opening of the file, as written. The whole thing — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Domino Datasets Skill

Description

This skill helps users work with Domino Datasets - high-performance, versioned filesystem storage for data science projects.

Activation

Activate this skill when users want to:

  • Create or manage Domino Datasets
  • Work with dataset snapshots and versioning
  • Share data between projects
  • Access large datasets efficiently
  • Understand dataset paths and mounting

What is a Domino Dataset?

A Domino Dataset is:

  • High-performance storage: Network filesystem optimized for data science
  • Versioned: Create snapshots for reproducibility
  • Shareable: Access across projects
  • Scalable: No file size or count limits
  • Persistent: Data persists across executions

Creating a Dataset

Via Domino UI

  1. Navigate to your project
  2. Go to Data > Domino Datasets
  3. Click Create New Dataset
  4. Enter:
    • Name: Dataset name (e.g., training-data)
    • Description: What the dataset contains
  5. Click Create

Via Python SDK

from domino import Domino

domino = Domino("project-owner/project-name")

# Create a new dataset
dataset = domino.datasets_create(
    name="training-data",
    description="Training data for classification model"
)

Dataset Paths

Dataset paths differ based on your project type. Domino has two project types with different mount structures.

DFS (Domino File System) Projects

DFS projects use /domino as the root:

/domino
   |--/datasets
      |--/local               <== Local datasets and snapshots
         |--/clapton          <== Read-write dataset for owner and editor, read-only for reader
         |--/mingus           <== Read-write dataset for owner and editor, read-only for reader
         |--/snapshots        <== Snapshot folder organized by dataset
            |--/clapton       <== Read-write for owner and editor, read-only for reader
               |--/tag1          <== Mounted under latest tag
               |--/1             <== Always mounted under the snapshot number
               |--/2
            |--/mingus
               |--/tag2
               |--/1
               |--/2
      |--/ella                <== Read-write shared dataset for owner and editor, Read-only for reader
      |--/davis               <== Read-write shared dataset for owner and editor, Read-only for reader
      |--/snapshots           <== Shared datasets snapshots organized by dataset
         |--/ella             <== Read-write for owner and editor, read-only for reader
            |--/tag3          <== Mounted under latest tag
            |--/1             <== Always mounted under the snapshot number
            |--/2
         |--/davis
            |--/tag4
            |--/1
            |--/2

Read the full file on GitHub · 393 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.

  1. 10d ago First seen · 393 lines · 63 tokens per session scan A ef1d7b80c230

Subscribe to this mod's changes

domino-datasets is a skill published in the GitHub repository dominodatalab/domino-claude-plugin (6 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 2,706 once invoked, about $0.0003 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 2026-08-30.

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