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agentsop-dspy

Convert prompt-based LLM pipelines into DSPy-compiled programs with signatures, modules, and metrics.

287|16|Updated May 20, 2026
Search Tags:#dspy#metric design#rag optimization#teleprompting#prompt compilation#machine learning evaluation#code-free prompt tuning
One-click install
npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-dspy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentsop-dspy
Source: https://github.com/agentsope/SkillAlchemy/tree/main/skills/agentsop-dspy
Command: npx skills add https://github.com/agentsope/SkillAlchemy --skill agentsop-dspy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It turns brittle, hand-written prompt pipelines into a reusable decision system that can be optimized with evaluation metrics instead of guesswork.

Core Features & Use Cases

  • When to activate: Detects DSPy-relevant intent (e.g., “use DSPy”, “compile prompts”, “MIPRO/GEPA”) and explicitly avoids one-shot or audit-forced cases.
  • SOP for DSPy compilation: Guides coder-agents through Signature → Module → Metric → Compile using DSPy’s Stage 1/2/3 gate with clear exit criteria.
  • Dilemma-based decision rules: Covers optimizer cost vs gain, LM swapping and recompilation, metric bias risks, and handling stuck compile trials.
  • Evidence-driven guidance: Uses documented DSPy/GEPA practices and warns against common anti-patterns (no metric, too few examples, wrong optimizer stage).

Quick Start

Ask an AI coder-agent to “apply the agentsop-dspy skill to turn my current prompt pipeline into a DSPy program by defining a Signature, selecting an appropriate Module, building a trustworthy metric, and running a Stage 3 compile with auto='light' first.”

Frequently Asked Questions about agentsop-dspy

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compile brittle LLM prompts into measurable DSPy programs?▼

DSPy compilation requires an evaluation metric, a dataset meeting recommended thresholds for few-shot examples, and correct module selection (Predict/ChainOfThought/ReAct). You need these prerequisites to transition from brittle prompts to measurable, optimized DSPy programs.

What is the best way to optimize RAG generators and rerankers using DSPy?▼

The best way to optimize RAG components is applying staged DSPy compilation with explicit metrics and optimizer gates. This allows you to tune rerankers and generators systematically rather than relying on manual prompt engineering.

When should I recompile my DSPy program after swapping language models?▼

You should recompile your DSPy program whenever swapping LM families because optimized prompts and few-shot examples are model-specific. Recompilation ensures the program adapts to the new model's characteristics and maintains evaluation performance.

What are common anti-patterns to avoid during DSPy prompt compilation?▼

Common DSPy compilation anti-patterns include lacking a metric for evaluation, using too few examples, and selecting the wrong optimizer stage. Avoiding these ensures your compile trials do not get stuck and prevents metric bias risks.

How do I choose between DSPy modules like Predict, ChainOfThought, and ReAct?▼

Choosing the right DSPy module depends on task complexity: Predict for direct mapping, ChainOfThought for reasoning steps, and ReAct for tool-using agents. Correct module selection is critical before running Stage 3 compilation with auto='light'.

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