claudegoodies
Skill

dspy

From NousResearch

DSPy: declarative LM programs, auto-optimize prompts, RAG.

Facts

Repository
NousResearch/hermes-agent
Status
Actively maintained
Last commit
Source file
optional-skills/mlops/research/dspy/SKILL.md

Source preview

The instructions Claude Code reads when this skill runs.

# 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 = dspy.OutputField(desc="often between 1 and 5 words")

# Create a module
qa = dspy.Predict(QA)

# Use it
response = qa(question="What is the capital of France
View full source on GitHub →

Other skills