prose

Define and coordinate multi-agent AI workflows with a declarative language.

55|6|Updated Feb 13, 2026

Search Tags:#automation #workflow #multi-agent #AI #orchestration #programming #control flow

Ready to run

npx skills add https://github.com/NJX-njx/opensoul --skill prose-njx-njx

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yaml, json, task, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

It enables users to craft complex, integrated workflows for AI agents, streamlining multi-step and multi-agent tasks in a clear, maintainable manner.

Core Features & Use Cases

Structured Workflow Orchestration: Define sequences, parallel operations, and control flow with precision.
Multi-Agent Coordination: Instantiate, configure, and invoke reusable agent templates for specialized tasks.
Error Handling & Loops: Build resilient, iterative processes with robust retry, break, and conditional logic.
Use Case: Automate a content pipeline where an agent fetches data, processes it with specialized agents, and summarizes findings with error recovery.

Quick Start

Use prose run to execute your .prose script and automate multi-agent workflows with minimal setup.

Frequently Asked Questions about prose

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

FAQPage Schema

How do I coordinate multi-agent AI workflows with complex control flow?▼

Multi-agent AI workflows are coordinated using a declarative language that defines sequences, parallel operations, and control flow. This approach manages specialized agent templates, persistent state, and modular design within structured automated pipelines.

What is the best way to build automated AI pipelines with error handling and loops?▼

Automated AI pipelines with error handling and loops are built using a declarative programming language supporting robust retry, break, and conditional logic. This enables resilient iterative processes for orchestrating multi-step analysis and generation tasks.

Can I use YAML and JSON to configure reusable components for AI orchestration?▼

YAML and JSON are supported as dependencies for configuring reusable components and agent templates in AI orchestration. These formats provide the structured data necessary to define and instantiate specialized tasks within multi-agent workflows.

Does this multi-agent orchestration tool support parallel operations and persistent state?▼

Parallel operations and persistent state are fully supported by this multi-agent orchestration tool. It allows users to define sequences, manage state across workflow steps, and execute concurrent operations for streamlined multi-step task automation.

How do I execute a declarative script for multi-agent programming?▼

Declarative scripts for multi-agent programming are executed using the `prose run` command on `.prose` script files. This automates multi-agent workflows with minimal setup, handling the instantiation and coordination of configured agents.

When do I need a declarative language for AI workflow automation?▼

A declarative language for AI workflow automation is needed when building complex, integrated pipelines requiring coordinated multi-agent tasks. It provides maintainable structured programs for automating sequences, handling errors, and managing persistent state across specialized agents.