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prose

Orchestrate multi-agent workflows with a declarative programming language.

Updated Mar 10, 2026
Search Tags:#workflow automation#prose#multi-agent systems#ai orchestration#agent programming#llmops
One-click install
npx skills add https://github.com/lemonlqf/openclaw-rtsp --skill prose-lemonlqf
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prose
Source: https://github.com/lemonlqf/openclaw-rtsp/tree/main/extensions/open-prose/skills/prose
Command: npx skills add https://github.com/lemonlqf/openclaw-rtsp --skill prose-lemonlqf

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, control flow, and data passing.

Core Features & Use Cases

  • AI Workflow Orchestration: Define sequences, parallel tasks, loops, and error handling for AI agents.
  • Agent Management: Define, configure, and manage specialized AI agents with specific models and permissions.
  • Use Case: Automate a code review process where one agent identifies issues, another suggests fixes, and a third synthesizes the feedback into a report.

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 complex multi-agent workflows with structured control flow?▼

Multi-agent workflow orchestration is managed using a declarative programming language that defines agent behavior, control flow, and data passing. This enables automation of sequential, parallel, and conditional agent interactions for complex AI tasks.

Can I define specialized AI agents with specific models and permissions in my workflow?▼

Yes, specialized AI agents can be defined, configured, and managed with specific models and permissions. This allows distinct agents to handle separate tasks like issue identification, fix suggestions, and feedback synthesis within a single automated process.

What is the best way to automate a multi-step AI code review process?▼

Automating a multi-step AI code review process is best handled by orchestrating specialized agents in a sequence. One agent identifies issues, another suggests fixes, and a third synthesizes the feedback into a final report using structured declarative programs.

Does this multi-agent orchestration approach support error handling and state persistence?▼

Yes, multi-agent orchestration supports error handling and state persistence. Workflow state is managed via file-based or database backends, ensuring complex AI task execution remains robust and recoverable across sequential, parallel, and conditional operations.

How do I get started with writing programs for AI agent orchestration?▼

To get started with AI agent orchestration, run the example program 'examples/01-hello-world.prose'. This demonstrates the basic structure of the declarative programming language used to define agent behavior and control flow.

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