prompt-engineering-output-format

This skill enables version control and management for AI prompts, allowing developers to track changes, compare iterations, and maintain prompt history. It provides tools to create versioned prompt templates with parameters like style and length constraints. Use this when you need reproducible, auditable prompt workflows across different model versions or team collaborations.

This skill provides best practices for versioning AI prompts using semantic versioning and structured metadata. It helps developers track prompt changes, maintain changelogs, and organize different prompt versions systematically. Use this when implementing version control for production prompts in AI applications.

Agenta is a self-hosted platform for managing and evaluating LLM prompts. It enables developers to version prompts, run A/B tests, and track experiments with evaluation metrics. Use it to systematically test and deploy prompt changes with confidence.

This DSPy skill implements Retrieval-Augmented Generation (RAG) by integrating document retrieval with language model generation. It allows developers to configure a retriever (like ChromaDB) and define a pipeline that fetches relevant context before generating answers. Use this when you need to ground AI responses in specific external knowledge sources or documentation.