dspy
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
npx skills add Orchestra-Research/AI-Research-SKILLs --skill dspy --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# DSPy: Declarative Language Model Programming ## When to Use This Skill Use DSPy when you need to: - **Build complex AI systems** with multiple components and workflows - **Program LMs declaratively** instead of manual prompt engineering - **Optimize prompts automatically** using data-driven methods - **Create modular AI pipelines** that are maintainable and portable - **Improve model outputs systematically** with optimizers - **Build RAG systems, agents, or classifiers** with better reliability **GitHub Stars**: 22,000+ | **Created By**: Stanford NLP ## Installation ```bash # Stable release pip install dspy # Latest development version pip install git+https://github.com/stanfordnlp/dspy.git # With specific LM providers pip install dspy[openai] # OpenAI pip install dspy[anthropic] # Anthropic Claude pip install dspy[all] # All providers ``` ## Quick Start ### Basic Example: Question Answering ```python import dspy # Configure your language model lm = dspy.Claude(model="claude-sonnet-4-5-20250929") dspy.settings.configure(lm=lm) # Define a signature (input → output) class QA(dspy.Signature): """Answer questions with short factual answers.""" question = dspy.InputField() answer = ds
- When to Use This Skill
- Installation
- Quick Start
- Basic Example: Question Answering
- Chain of Thought Reasoning
- Core Concepts
- 1. Signatures
- 2. Modules
- 3. Optimizers
- 4. Building Complex Systems
- LM Provider Configuration
- Anthropic Claude
- OpenAI
- Local Models (Ollama)
Stable release pip install dspy Latest development version pip install git+https://github.com/stanfordnlp/dspy.git With specific LM providers pip install dspy[openai] # OpenAI pip install dspy[anthropic] # Anthropic Claude pip install dspy[all] # All providers
What does the dspy skill do?
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
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill dspy --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.