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Use DSPy to build declarative, modular LM pipelines, optimize prompts automatically, and assemble reliable RAG/agent systems with structured signatures and opti
DSPy is a declarative framework for programming language models. It provides structured signatures, modular components (Predict, ChainOfThought, ReAct, ProgramOfThought), and optimizers (BootstrapFewShot, MIPRO, BootstrapFinetune) to build, evaluate, and automatically improve complex AI systems such as RAG pipelines, agents, and classifiers. The skill's SKILL.md contains usage examples, installation instructions, and code patterns for building multi-stage pipelines and applying optimization loops.
Use DSPy when you need to move beyond ad-hoc prompt engineering: for projects that require reproducible, testable LM modules, automatic prompt optimization from data, multi-stage retrieval+generation systems, or production-grade pipelines with evaluators and metrics. It's suitable for research prototypes and production systems where maintainability and optimization matter.
Best for agents or developers that can run Python packages and configure LM providers (OpenAI, Anthropic, local Ollama). Works well when integrated into systems that support package installs, code execution, and programmatic evaluation.
Comprehensive DSPy tutorial skill covering installation, quick start, core concepts (Signatures, Modules, Optimizers), advanced patterns (RAG, assertions, self-consistency), evaluation, and best practices. No scripts to execute — purely documentation. Well-structured with proper frontmatter, progressive disclosure, and references to deeper content. API key examples use env vars or placeholders, no security concerns.
Pure documentation/tutorial skill with no executable scripts. Exceptionally well-written SKILL.md covering the DSPy framework comprehensively. References section lists external files (modules.md, optimizers.md, examples.md) that weren't bundled. No security issues whatsoever.
