Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming
8.7
Rating
0
Installs
AI & LLM
Category
Excellent DSPy skill with comprehensive coverage of declarative LM programming. The description clearly articulates when to use DSPy and what it accomplishes. Task knowledge is outstanding with complete installation steps, core concepts (signatures, modules, optimizers), real-world patterns, and working code examples for RAG systems, multi-hop QA, and agent-like reasoning. Structure is exemplary: the main SKILL.md provides a thorough yet organized overview with clear sections, while detailed references are appropriately separated into modules.md, optimizers.md, and examples.md. Novelty is strong—DSPy's automatic prompt optimization and declarative programming paradigm meaningfully reduces the token cost and complexity of building multi-stage AI systems that would otherwise require extensive manual prompt engineering. The skill demonstrates high practical value with best practices, debugging guidance, LM provider configs, and comparison to alternatives. Minor room for improvement in novelty scoring as some basic prediction patterns could be done manually, but the optimization and complex pipeline capabilities justify the skill's existence.
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