prompt-dsl

What problem does it solve?

Prompt engineering often yields brittle, ad-hoc prompt chains that are hard to reuse and maintain. This skill provides a domain-specific language (DSL) for categorical prompt composition to enable predictable, composable, and testable prompt pipelines.

Core Features & Use Cases

• Sequences, parallels, repeats, and conditional compositions for constructing complex prompts.
• Template objects and natural transformations to preserve composition guarantees.
• A Monad-like PromptM and functor utilities that support deterministic prompt generation and modular design.
• Use cases include building multi-step prompt pipelines, parameterized prompts, and robust algebraic prompt systems for AI applications.

Quick Start

Import the DSL primitives (system, user, assistant, context) and construct a simple prompt chain using the composition operators. Then render the chain into a flat list of prompts suitable for API calls.

Example: from prompt_dsl import system, user, assistant, Literal prompt = system("You are an expert prompt engineer.") >> user("Explain the concept of the DSL.") >> assistant("Here is the result.") prompts = prompt.render({})

prompts is a list of Prompt objects ready for sending to an API.