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.
