Compose declarative LM tasks into multi-step AI workflows with DSPy.
npx skills add https://github.com/quiznat/Hermes_Sapho --skill dspy-quiznat
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/quiznat/Hermes_Sapho/tree/main/.hermes/skills/mlops/research/dspy Command: npx skills add https://github.com/quiznat/Hermes_Sapho --skill dspy-quiznat
DSPy enables building end-to-end AI workflows by declaratively composing language-model tasks, turning ad-hoc prompt engineering into maintainable pipelines.
Create a minimal DSPy module signature and run it to generate a simple QA pipeline.
High-intent search queries and answers about installing and using this skill.
You can build declarative pipelines for RAG systems and multi-step reasoning by defining task signatures and modular components, turning ad-hoc prompt engineering into maintainable end-to-end AI workflows.
Automate LM pipeline optimization by applying built-in teleprompter-based optimizers like BootstrapFewShot and MIPRO to systematically improve your prompts and weights across production workflows.
Declarative modules improve multi-stage reasoning workflows by allowing you to compose language-model tasks modularly, enabling tool-using agents and self-improving systems to operate predictably.
No specific external dependencies are required to construct tool-using agents; the framework provides built-in patterns and tooling to implement multi-stage retrieval and reasoning natively.
Yes, you can declaratively compose classifiers and tool-using agents without manual prompt engineering by utilizing task signatures that systematically structure language-model interactions into automated pipelines.
Devsoul2026Build modular AI pipelines with DSPy signatures and optimizers.
ChangZhou-xjBuild self-improving AI pipelines with declarative DSPy modules.
xurenluDevelop RAG systems integrating vector databases, embedding models, and retrieval strategies.
x-TheFoxBuild and optimize LM workflows with a declarative DSL.
