deepwork_jobs

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

Updated Jan 27, 2026

Search Tags:#learning #orchestration #workflow-automation #define #ai-workflow #deepwork #job.yml

Ready to run

npx skills add https://github.com/ncrmro/deepwork-permanent-portfolio --skill deepwork-jobs-ncrmro

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deepwork_jobs
Source: https://github.com/ncrmro/deepwork-permanent-portfolio/tree/main/.claude/skills/deepwork_jobs
Command: npx skills add https://github.com/ncrmro/deepwork-permanent-portfolio --skill deepwork-jobs-ncrmro

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

DeepWork projects often require designing and coordinating multi-step AI workflows. This Skill provides a structured toolkit to define, validate, implement, and refine those workflows, ensuring repeatable, auditable results.

Core Features & Use Cases

Define: Creates a job.yml specification by gathering workflow requirements through structured questions. Use when starting a new multi-step workflow.
Review_job_spec: Reviews job.yml against quality criteria using a sub-agent for unbiased validation. Use after defining a job specification. (requires: define)
Implement: Generates step instruction files and syncs slash commands from the job.yml specification. Use after job spec review passes. (requires: review_job_spec)
Learn: Analyzes conversation history to improve job instructions and capture learnings. Use after running a job to refine it.

Quick Start

Start by running the define step to interactively create and tailor a new DeepWork job.yml for your workflow.

Frequently Asked Questions about deepwork_jobs

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

FAQPage Schema

How do I define multi-step AI workflows using a job.yml specification?▼

To define multi-step AI workflows, use an interactive process that gathers requirements to create a structured job.yml specification. This specification serves as the foundational blueprint for orchestrating automated AI tasks.

What is the best way to validate a job.yml specification for AI workflow automation?▼

The best way to validate a job.yml specification is to review it against quality criteria using a sub-agent. This ensures unbiased validation and confirms the workflow meets structural and operational standards before implementation.

How do I implement step instructions and sync slash commands from a job.yml file?▼

You implement step instructions by generating instruction files and syncing slash commands directly from the validated job.yml specification. This translates the workflow definition into executable actions for orchestration.

How does automated learning improve multi-step AI workflow instructions after a run?▼

Automated learning improves multi-step AI workflow instructions by analyzing conversation history after a job runs. It captures learnings to refine future runs and updates the AGENTS.md pipeline for better orchestration.

Do I need any external dependencies to orchestrate and refine multi-step AI workflows?▼

No external dependencies are required to orchestrate and refine multi-step AI workflows. The system operates independently using the job.yml definition and AGENTS.md to manage structured automated learning pipelines.

When should I use a structured job.yml definition for workflow automation instead of ad-hoc prompting?▼

You should use a structured job.yml definition for workflow automation when you need repeatable, auditable results. It provides a structured toolkit to coordinate multi-step AI tasks, ensuring consistent execution over manual prompting.