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39 lines
1.7 KiB
Markdown
39 lines
1.7 KiB
Markdown
# DSPy — Programming, Not Prompting Language Models (Stanford)
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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.
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## Why Install This Skill
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When your agent loads this skill, it becomes a DSPy expert who can:
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- **Define typed signatures** — input/output schemas with descriptions
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- **Build program modules** — Predict, ChainOfThought, ReAct, and custom Module subclasses
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- **Select optimizers** — MIPROv2, BootstrapFewShot, BootstrapFinetune — matching optimizer to bottleneck
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- **Compile programs** — transform a Python program into an optimized, prompt-efficient artifact
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- **Evaluate and iterate** — metrics, datasets, and optimization loops
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## What You Get
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| Directory | Purpose |
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|-----------|---------|
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| `SKILL.md` | Core paradigm, optimizer cheat sheet, compilation pipeline |
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| `references/` | Signatures deep dive, module patterns, optimizer selection guide, evaluation methodology |
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## Framework Comparison
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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.
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## Requirements
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Python 3.8+ with `dspy` package.
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## Quick Start
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Start with the setup and first workflow in SKILL.md, then use the linked resources for the specific task you need to complete.
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## Triggers
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Use this skill for the task types and keywords described in its SKILL.md description.
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