dynamic-prompt-registry

Dynamically compose AI prompts by using a categorical registry to retrieve and combine sub-prompts based on criteria like domain, quality, or tags. This enables flexible meta-prompting by treating prompts as first-class, composable entities.

Build a prompt library with deferred resolution by leveraging a Reader monad for runtime resolution and tensor product composition. This approach facilitates managing complex AI workflows through deferred evaluation and unified syntax.

Combine multiple skills into a single meta-prompt using categorical operators like tensor products, sequential composition, and Kleisli operators. This allows dynamic discovery and composition of relevant sub-prompts for comprehensive task execution.

Discover relevant API testing prompts by querying the dynamic prompt registry using a unified syntax to filter for skills with a relevance score greater than a specified threshold, dynamically retrieving matching sub-prompts.

Dynamic prompt composition works completely without external dependencies, relying solely on internal scripts and references to manage categorical structures and runtime resolution for complex AI agent workflows.

Use tensor product composition for prompt engineering when building complex AI agents that require dynamically discovering and combining multiple relevant sub-prompts, such as generating a comprehensive test suite from API testing and validation skills.