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 dominodatalab/domino-claude-plugin --skill datasetsgit clone --depth 1 https://github.com/dominodatalab/domino-claude-pluginWrote 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/dominodatalab/domino-claude-plugin/datasets)<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.
<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>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 | $0.00063 | $0.02706 |
| Opus 5 | $0.00032 | $0.01353 |
| Sonnet 5 | $0.00013 | $0.00541 |
| Haiku 4.5 | $0.00006 | $0.00271 |
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.
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
- Navigate to your project
- Go to Data > Domino Datasets
- Click Create New Dataset
- Enter:
- Name: Dataset name (e.g.,
training-data) - Description: What the dataset contains
- Name: Dataset name (e.g.,
- 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
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.
- 10d ago First seen · 393 lines · 63 tokens per session scan A ef1d7b80c230
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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…