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deepwork_jobs

Create and orchestrate multi-step DeepWork jobs as executable AI pipelines.

1|Updated Jun 30, 2025
Search Tags:#learning#multi-step#AI workflows#define#deepwork#job.yml
One-click install
npx skills add https://github.com/EONMUN/EONMUN --skill deepwork-jobs
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: deepwork_jobs
Source: https://github.com/EONMUN/EONMUN/tree/main/.claude/skills/deepwork_jobs
Command: npx skills add https://github.com/EONMUN/EONMUN --skill deepwork-jobs

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

DeepWork job orchestration is complex and repetitive. This Skill provides a structured approach to define, implement, and improve multi-step AI workflows, reducing errors and speeding development.

Core Features & Use Cases

  • Define: Create a complete job.yml by asking structured questions about the workflow, inputs, and outputs so teams can capture precise requirements.
  • Review: Validate the job specification with a sub-agent to ensure quality and compliance before implementation.
  • Implement: Generate step instruction files and synchronize commands from the job.yml to deploy and execute the workflow.
  • Learn: Analyze past runs to identify improvements and store bespoke learnings in AGENTS.md for future runs.
  • Use Case: When building a complex AI pipeline (e.g., data collection -> preprocessing -> model training -> evaluation), use this Skill to define, verify, implement, and refine the workflow.

Quick Start

Use the Skill tool to start by defining a new DeepWork job:

  • Skill tool: deepwork_jobs.define

Frequently Asked Questions about deepwork_jobs

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

FAQPage Schema
How do I orchestrate multi-step AI workflows end-to-end?▼

You can orchestrate multi-step AI workflows by using a structured job.yml specification to define, review, implement, and learn from complex AI pipelines. This process converts sequential tasks into executable, repeatable automation.

How do I define a structured AI pipeline from complex requirements?▼

To define an AI pipeline, you answer structured questions about workflow inputs, outputs, and task sequences. This interactive specification process captures precise requirements and generates a complete job.yml configuration file.

How do I validate an AI workflow specification before implementation?▼

You validate an AI workflow specification by using a sub-agent to review the job.yml file. This review step ensures quality and compliance of the multi-step task sequence before actual implementation begins.

How do I improve multi-step AI workflows after execution?▼

To improve multi-step AI workflows, you analyze past runs to identify areas for refinement. The system records these bespoke learnings in an AGENTS.md file to enhance future executions and continuous improvement.

What is the best way to automate complex AI task sequences for operations?▼

The best way to automate complex AI task sequences is through structured job orchestration. This approach applies across product development, analytics, and operations by guiding users through defining, reviewing, implementing, and learning from defined runs.

Do I need prior pipeline configurations to build a multi-step AI workflow?▼

You do not need prior pipeline configurations to start. The system guides you through an interactive job specification process, asking structured questions to create a complete job.yml from scratch based on your requirements.

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Defines and manages multi-step DeepWork AI workflows via structured job.yml files.

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