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HypoxanthineOvO
HypoxanthineOvOCOMMUNITY
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hypo-workflow

Run serialized prompt execution pipelines locally within a .pipeline workspace.

24|3|Updated Apr 22, 2026
Search Tags:#automation#planning#pipeline#execution#hypo-workflow#hw:start
One-click install
npx skills add https://github.com/HypoxanthineOvO/Hypo-Workflow --skill hypo-workflow
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hypo-workflow
Source: https://github.com/HypoxanthineOvO/Hypo-Workflow/tree/main
Command: npx skills add https://github.com/HypoxanthineOvO/Hypo-Workflow --skill hypo-workflow

SYSTEM DOCUMENTATION & REQUIREMENTS

## What problem does it solve? This Skill provides a centralized, auditable way to run and manage a serialized prompt-execution pipeline locally within a .pipeline workspace, enabling safe transitions from planning to execution to review.

## Core Features & Use Cases

  • Orchestrates /hw:start, /hw:plan, /hw:start, /hw:resume, /hw:status, /hw:report, and related commands into a cohesive local workflow.
  • Supports lifecycle management, plan generation, compact reporting, knowledge extraction, and cycle/patch handling for long-running AI tasks.
  • Useful for teams needing reproducible, recoverable AI experiments within a single project workspace.

Quick Start

Initiate the pipeline from your repo with /hw:start to begin the first milestone.

Frequently Asked Questions about hypo-workflow

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

FAQPage Schema
How do I coordinate local AI workflows end-to-end?▼

To coordinate local AI workflows end-to-end, run a serialized prompt execution pipeline within a .pipeline workspace to safely transition from planning to execution and review.

How do I manage and recover long-running AI tasks locally?▼

You can manage and recover long-running AI tasks locally using lifecycle commands for plan generation, status tracking, and cycle handling, ensuring reproducible experiments within a single project workspace.

What is the best way to orchestrate a serialized prompt execution pipeline?▼

The best way to orchestrate a serialized prompt execution pipeline is by initiating the first milestone from your repository, then progressing through planning, execution, auditing, and reporting stages.

Can I use local AI workflow commands for auditing and reporting?▼

Yes, local AI workflow commands support compact reporting and knowledge extraction, providing a centralized and auditable way to review long AI tasks across the full workflow lifecycle.

What are the limitations of running AI experiments in a local project workspace?▼

Running AI experiments in a local project workspace is constrained to a serialized execution pipeline, meaning tasks rely on deterministic scripts and platform adapters without external distributed processing.

Do I need a specific workspace setup to automate AI task planning and execution?▼

Yes, automating AI task planning and execution requires a local .pipeline workspace to safely serialize prompts, apply platform adapters, and manage lifecycle transitions deterministically.

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