Compile declarative language model signatures into self-improving modules.
npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill dspy-cxnaive
Please help me install this Agent Skill. Skill: dspy Source: https://github.com/cxnaive/hermes-agent-llbot/tree/main/optional-skills/mlops/research/dspy Command: npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill dspy-cxnaive
This Skill solves the fragility and manual labor of traditional prompt engineering by providing a framework to program language models declaratively and optimize them automatically using data-driven methods.
Use the dspy skill to configure your language model and compile a question-answering module using your provided training dataset.
High-intent search queries and answers about installing and using this skill.
You can optimize LLM pipelines by compiling declarative task signatures into self-improving modules that automatically refine prompts and few-shot examples based on your validation metrics.
Yes, you can build modular RAG and multi-stage reasoning pipelines that are portable across different LLM providers by defining declarative signatures rather than hardcoding provider-specific logic.
Yes, automatic prompt optimization requires systematic validation metrics from your training dataset to iteratively evaluate and refine prompts and few-shot demonstrations.
The best way to maintain agentic workflows is programming language models declaratively, which compiles task inputs and outputs into maintainable, optimized pipelines.
Yes, this approach supports multi-hop reasoning and agentic tool use across various LLM providers, explicitly including dependencies for both OpenAI and Anthropic models.
Manual prompt engineering fails due to fragility and labor intensity, whereas declarative programming compiles task signatures into optimized, data-driven pipelines that self-improve.
MigoXLabVerify factual claims in articles using OpenAI GPT models and web search.
JKhyroBuild modular AI systems with DSPy and optimize prompts automatically.
shalevaminDesigns and implements RAG systems with chunking, embedding generation, vector stores, hybrid search, reranking, and evaluation.
cndoit18Design and optimize LLM prompts with few-shot learning and chain-of-thought.
