Execute .prose programs with a VM-like runtime for multi-agent workflows.
npx skills add https://github.com/MaxSaiets/myOpenCL --skill prose-maxsaiets
Please help me install this Agent Skill. Skill: prose Source: https://github.com/MaxSaiets/myOpenCL/tree/main/openclaw/extensions/open-prose/skills/prose Command: npx skills add https://github.com/MaxSaiets/myOpenCL --skill prose-maxsaiets
OpenProse provides a compact, scriptable runtime for OpenProse AI orchestration tasks, enabling teams to design, run, and manage multi-agent workflows as structured programs. It reduces ad-hoc prompt chaining, simplifies debugging, and makes large automations reproducible by codifying agent templates, flows, and state management.
Write a simple .prose program and run it with prose run to see the OpenProse VM orchestration in action.
High-intent search queries and answers about installing and using this skill.
Multi-agent orchestration is handled by codifying agent templates, flows, and state management into structured .prose programs. This approach replaces ad-hoc prompt chaining, simplifies debugging, and makes large automations reproducible by executing commands through a VM-like runtime.
Yes, AI agent workflows support multiple state-backends including SQLite and PostgreSQL. You can configure these databases to manage state, enabling workflow resumption, audit trails, and cross-project reuse alongside filesystem and in-context memory options.
You can run concurrent and sequential agent tasks by writing a .prose program that utilizes sessions, parallel blocks, and pipelines. Execute the structured program using the 'prose run' command to see the OpenProse VM orchestration in action.
The best way to route inputs and outputs is via a modular skill registry that integrates with external program imports. This allows you to compose nested workflows and manage inputs/outputs effectively by routing commands through modular skill docs.
No specific external dependencies are required to define reusable agent templates. You can define templates with models, prompts, skills, and permissions directly within your .prose programs, composing tasks with inputs and outputs natively.
A VM-like runtime provides a compact, scriptable environment for AI automation that standard scripts lack. It reduces ad-hoc prompt chaining, simplifies debugging, and makes large automations reproducible by codifying agent templates and managing state across backends.
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