prose

Name: prose
Availability: InStock
Author: luisvelmarketer

Orchestrates AI agents via a declarative language with parallel execution and error handling.

1|1|Updated Mar 3, 2026

Search Tags:#automation #agent workflow #multi-agent systems #ai orchestration #llm programming #prose language

Ready to run

npx skills add https://github.com/LuisvelMarketer/genome --skill prose-luisvelmarketer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: prose
Source: https://github.com/LuisvelMarketer/genome/tree/main/extensions/open-prose/skills/prose
Command: npx skills add https://github.com/LuisvelMarketer/genome --skill prose-luisvelmarketer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates complex multi-agent workflows by allowing you to write structured programs that define agent behavior, data flow, and execution logic, eliminating manual orchestration and ad-hoc prompting.

Core Features & Use Cases

AI Agent Orchestration: Define and manage sequences, parallel execution, and conditional logic for multiple AI agents.
Workflow Automation: Build reusable, robust workflows for tasks like code review pipelines, content generation, and complex data analysis.
Use Case: Automate a code review process where one agent identifies potential bugs, another checks for style compliance, and a third synthesizes feedback into a single report, all managed by a central prose program.

Quick Start

Use the prose skill to run the example program 'examples/01-hello-world.prose'.

Frequently Asked Questions about prose

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

FAQPage Schema

How do I orchestrate multi-agent systems without manual prompting?▼

You can orchestrate multi-agent systems by writing structured programs in a declarative language that defines agent behavior, data flow, and execution logic. This eliminates manual orchestration by managing agent lifecycles, context passing, and parallel execution automatically.

What is the best way to automate conditional logic for AI agent workflows?▼

The best way to automate conditional logic for AI agent workflows is using a declarative programming language to define sequences, parallel execution, and error handling. This approach manages complex agent lifecycles and context passing without ad-hoc prompting.

Can I build reusable workflow templates for complex code review pipelines?▼

Yes, you can build reusable workflow templates for complex code review pipelines. The system supports workflow templating and agent specialization, allowing you to define programs where distinct agents handle bug identification, style checks, and feedback synthesis.

How does context passing work between parallel AI agents?▼

Context passing between parallel AI agents works through a declarative program structure that manages the agent lifecycle. The orchestration system automatically handles data flow and execution logic, ensuring context is passed correctly across concurrent agent tasks.

Do I need prior LLM programming experience to use declarative agent orchestration?▼

Prior LLM programming experience is beneficial but not strictly required. The declarative language approach abstracts manual orchestration and ad-hoc prompting, allowing you to define complex multi-agent execution logic through structured programs and reusable templates.

Why use a declarative language for AI orchestration instead of manual ad-hoc prompting?▼

A declarative language for AI orchestration eliminates manual ad-hoc prompting by allowing you to define structured programs for agent behavior and data flow. It provides robust error handling, self-improvement patterns, and workflow templating that manual prompting cannot achieve reliably.