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dspy-development

Automate DSPy pipeline creation, optimization, and deployment workflows.

Updated Aug 23, 2026
Search Tags:#multimodal#pipeline#dspy#rlm#codeact#lm-programming#optimizers
One-click install
npx skills add https://github.com/bjornslib/cobuilder-harness --skill dspy-development
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dspy-development
Source: https://github.com/bjornslib/cobuilder-harness/tree/main/.claude/skills/dspy-development
Command: npx skills add https://github.com/bjornslib/cobuilder-harness --skill dspy-development

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

DSPy Development helps teams rapidly compose, test, and operationalize DSPy-based pipelines and agents by providing standardized building blocks, templates, and optimization workflows. It centralizes knowledge about modules, optimizers, adapters, and save/load practices to accelerate production-grade DSPy projects.

Core Features & Use Cases

  • Templates and examples for Predict, ChainOfThought, ReAct, RLM, CodeAct, Refine, BestofN, and Type systems
  • Integrated optimizer workflows (MIPROv2, GEPA, SIMBA, ArborGRPO) to improve prompts, instructions, and even weights
  • Guidance on saving/loading programs with 3.x compatibility and multi-model LM management

Quick Start

Describe how to start a DSPy project using the DSPy Development skill.

Frequently Asked Questions about dspy-development

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

FAQPage Schema
How do I build and optimize multi-stage LM pipelines using DSPy?▼

Build multi-stage LM pipelines using DSPy by composing modules like Predict, ChainOfThought, and CodeAct, then applying optimizers such as MIPROv2, GEPA, or SIMBA to automatically refine prompts and weights.

When should I use RLM and CodeAct instead of standard DSPy modules?▼

Use RLM for long-context processing and CodeAct for multi-modal tool integration when standard Predict or ChainOfThought modules lack the reasoning or execution capabilities required for complex workflows.

Do I need prior experience with the DSPy module system to use this workflow?▼

Yes, this workflow requires familiarity with the DSPy module system, including Predict, ChainOfThought, RLM, and CodeAct, as well as the optimizer family and the 3.x save/load protocol for portable programs.

What is the best way to save and load DSPy programs for production deployment?▼

The best way to save and load DSPy programs is using the 3.x save/load protocol, which ensures portable program state and supports multi-model LM management across different deployment environments.

Can I optimize both prompts and model weights with DSPy optimizers?▼

Yes, you can optimize both prompts and model weights using the integrated DSPy optimizer workflows, specifically leveraging MIPROv2, GEPA, SIMBA, and ArborGRPO to improve instructions and fine-tune weights.

How do I handle multi-modal tool integration in a DSPy pipeline?▼

Handle multi-modal tool integration in a DSPy pipeline by utilizing the CodeAct module type, which enables dynamic tool execution and orchestration within multi-stage LM workflows.

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