Why use OpenProse instead of plain English prompts?

OpenProse provides necessary structure for logic that English lacks, such as variable scoping, precise iteration, sequential vs. parallel distinction, and error handling.

Does OpenProse support parallel execution?

Yes, OpenProse features a 'parallel:' block that allows you to spawn and manage multiple subagent sessions concurrently.

Where are the OpenProse configuration files stored?

The skill uses a .prose/ directory in your workspace for state management and telemetry, while execution logic is defined in .prose files.

OpenProse is a programming language specifically for AI sessions that structures English instructions into unambiguous control flow for complex tasks.

How do I execute an OpenProse program?

You can run .prose files by activating the skill in Claude Code and using commands like 'prose run [filename]' or 'prose boot'.

OpenProse AI Session Language

Name: OpenProse AI Session Language
Author: openprose

byopenprose

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580

Productivity & Workflow

Executes structured multi-agent workflows and complex AI orchestrations using a specialized programming language for LLM sessions.

About

OpenProse is a specialized programming language designed to transform AI sessions into Turing-complete virtual machines. It allows developers to structure English into unambiguous control flow, providing a reliable pattern for orchestrating multiple subagents, parallel tasks, and complex loops without the overhead of external frameworks. By leveraging 'Intelligent IoC' and the OpenProse VM specification, it enables high-fidelity simulation of agentic workflows directly within Claude Code, making it ideal for sophisticated automation, multi-step research, and automated code maintenance.

Key Features

Multi-agent orchestration with native subagent session management
Zero-dependency implementation that turns LLMs into execution environments
Intelligent IoC for context-aware agent wiring and dependency management
Structured control flow including parallel execution, loops, and try/catch blocks
Comprehensive library of 30+ implementation patterns and examples
580 GitHub stars

Use Cases

Orchestrating complex multi-agent research and software engineering pipelines
Automating repetitive coding tasks with intelligent error handling and retries
Creating reusable, documentable AI workflows for team-wide consistency

About

Key Features

Multi-agent orchestration with native subagent session management
Zero-dependency implementation that turns LLMs into execution environments
Intelligent IoC for context-aware agent wiring and dependency management
Structured control flow including parallel execution, loops, and try/catch blocks
Comprehensive library of 30+ implementation patterns and examples
580 GitHub stars

Use Cases

Orchestrating complex multi-agent research and software engineering pipelines
Automating repetitive coding tasks with intelligent error handling and retries
Creating reusable, documentable AI workflows for team-wide consistency