Automate LM workflows with declarative modules and optimizers.
npx skills add https://github.com/moltis-org/moltis --skill dspy-moltis-org
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/moltis-org/moltis/tree/main/crates/skills/src/assets/mlops/research/dspy Command: npx skills add https://github.com/moltis-org/moltis --skill dspy-moltis-org
DSPy reduces the complexity of building reliable, scalable AI systems by providing a declarative, modular programming model for composing pipelines, reasoning, and optimization across RAGs and agents.
Create a simple ChainOfThought module and run a basic question-answer workflow.
High-intent search queries and answers about installing and using this skill.
You can build declarative AI pipelines by defining typed Signatures for inputs and outputs, then composing modular reasoning steps like Predict, ChainOfThought, and ReAct to structure LM workflows safely.
Automatic prompt optimization is handled by built-in optimizers like BootstrapFewShot, MIPRO, and COPRO, which improve prompt performance and model outputs dynamically without manual tuning.
Multi-stage reasoning workflows for RAG and agents are created by chaining modular components such as ChainOfThought, ProgramOfThought, and ReAct to handle tool-use flows and real-time information retrieval.
Yes, this declarative computing model applies to both research and production environments, providing modular pipelines and optimization to reduce the complexity of building scalable AI systems.
Manual prompt engineering lacks scalability and reliability for complex AI systems, whereas declarative programming with built-in optimizers standardizes prompt generation and improves model performance systematically.
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