Compose DSPy modules and automate prompt optimization in Python.
npx skills add https://github.com/AissenLiu/EasyHermes --skill dspy-aissenliu
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/AissenLiu/EasyHermes/tree/main/hermes-agent/skills/mlops/research/dspy Command: npx skills add https://github.com/AissenLiu/EasyHermes --skill dspy-aissenliu
Declarative DSPy enables building complex AI systems by composing modular components and automating prompt improvements.
Provide a DSPy module that answers a complex question with step-by-step reasoning and demonstrate bootstrapping few-shot optimization.
High-intent search queries and answers about installing and using this skill.
Automate RAG prompt optimization by defining declarative modules and applying DSPy teleprompters like BootstrapFewShot or MIPRO. These optimizers compile data-driven demonstrations into your pipeline, automatically improving prompt quality without manual tuning.
Build multi-stage AI pipelines with tool-enabled agents by declaratively composing DSPy modules such as ReAct and ProgramOfThought. This modular approach chains reasoning steps and tool interactions into structured outputs without hard-coded prompts.
Yes, you need Python and the DSPy package installed to build declarative AI pipelines. The framework provides modular components and optimizers that require a Python environment to compose and execute complex AI workflows.
Structure step-by-step reasoning using the DSPy ChainOfThought module within declarative AI pipelines. This approach programmatically defines reasoning stages and supports automated prompt improvement through built-in optimizers like KNNFewShot.
BootstrapFewShot generates few-shot examples by simulating successful pipeline executions, while MIPRO optimizes prompts by proposing instructions and demonstrations across multiple stages. Both are DSPy teleprompters used for data-driven prompt improvement.
DSPy optimizers like BootstrapFewShot require some input examples to bootstrap few-shot demonstrations. Without structured training data, optimizers cannot effectively evaluate and improve the declarative pipeline's prompt quality or reasoning steps.
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