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Each README is written for a human audience, explaining: - What the skill does (not what format it follows) - What benefit the user gets from installing it - Quick setup and usage patterns - When to load/trigger the skill - What scripts, references, and templates it ships data-scientist already had a README — left unchanged. 48 READMEs added across all skill and bundle directories.
DSPy — Programming, Not Prompting Language Models (Stanford)
An expert-level skill for programmatic prompt optimization with Stanford's DSPy framework. You write Python programs with typed signatures; DSPy optimizes the prompts automatically. This is the framework for prompt engineering that doesn't feel like engineering.
Why Install This Skill
When your agent loads this skill, it becomes a DSPy expert who can:
- Define typed signatures — input/output schemas with descriptions
- Build program modules — Predict, ChainOfThought, ReAct, and custom Module subclasses
- Select optimizers — MIPROv2, BootstrapFewShot, BootstrapFinetune — matching optimizer to bottleneck
- Compile programs — transform a Python program into an optimized, prompt-efficient artifact
- Evaluate and iterate — metrics, datasets, and optimization loops
What You Get
| Directory | Purpose |
|---|---|
SKILL.md |
Core paradigm, optimizer cheat sheet, compilation pipeline |
references/ |
Signatures deep dive, module patterns, optimizer selection guide, evaluation methodology |
Framework Comparison
DSPy is not a chain or RAG framework. It operates at the compiler layer — you define the program structure, DSPy optimizes the prompts. Use this when you want prompt engineering to be deterministic and testable, not a manual tuning exercise.
Requirements
Python 3.8+ with dspy package.