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domino-jobs

Automate Domino batch job orchestration, scheduling, and monitoring via API or CLI.

Updated Apr 11, 2026
Search Tags:#automation#mlops#scheduling#batch#jobs#domino
One-click install
npx skills add https://github.com/ToXMon/tolu --skill domino-jobs-toxmon
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: domino-jobs
Source: https://github.com/ToXMon/tolu/tree/main/agent-zero-backup/workdir/memory-palace/skills/domino/domino-jobs
Command: npx skills add https://github.com/ToXMon/tolu --skill domino-jobs-toxmon

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Domino-driven workflows require reliable batching, scheduling, and monitoring to scale data science and automation. This skill enables teams to create, run, and track Domino batch jobs end-to-end, reducing manual setup and drift across pipelines.

Core Features & Use Cases

  • Job creation and configuration (hardware tier, compute environment, inputs/outputs)
  • Scheduling and automated runs (cron-like schedules, recurring workflows)
  • Monitoring and logging through Domino UI or API, including status, artifacts, and logs
  • API/CLI/SDK access to start, query, or stop jobs for integration with pipelines
  • Use cases include batch data processing, scheduled model training, and reproducible experiments across teams

Quick Start

Configure and start a batch Domino job to run a script, set environment and schedule as needed, and monitor results.

Frequently Asked Questions about domino-jobs

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I schedule and run recurring Domino batch jobs?▼

To configure Domino batch jobs, specify the hardware tier, compute environment, and input/output management settings. These configurations ensure your automated workflows have the necessary computational resources and dependencies.

Can I start and stop Domino batch jobs using the API or CLI?▼

Monitoring Domino batch jobs provides access to status updates, logs, and artifacts via the UI or API. This tracking mechanism ensures visibility into pipeline execution and helps identify failures across automated workflows.

Does this approach support batch processing for scheduled model training?▼

Batch processing supports scheduled model training by applying automated job orchestration across projects. You can configure compute environments and schedule recurring training tasks to produce reproducible experiments for your team.

What is the best way to manage hardware tier selection for Domino workflows?▼

Managing hardware tier selection requires specifying the appropriate compute environment during job creation. This ensures your batch data processing and scheduled tasks have sufficient resources allocated for execution.

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