dspy-signature-designer

Design type-safe DSPy signatures with InputField, OutputField, and Python typing.

120|13|Updated Dec 21, 2025

Search Tags:#typing #InputField #signature #dspy #OutputField

Ready to run

npx skills add https://github.com/OmidZamani/dspy-skills --skill dspy-signature-designer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dspy-signature-designer
Source: https://github.com/OmidZamani/dspy-skills/tree/main/skills/dspy-signature-designer
Command: npx skills add https://github.com/OmidZamani/dspy-skills --skill dspy-signature-designer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you design clear, type-safe DSPy signatures using InputField, OutputField, and advanced type hints, ensuring predictable module interfaces.

Core Features & Use Cases

Inline Signatures (Simple) with dspy.Predict and dspy.ChainOfThought templates
Class-based Signatures (Complex) using Python typing (Literal, Optional) and Pydantic models
Type Hints Reference to guide consistent outputs

Quick Start

Create a simple Summarize signature with text input and list of output strings, or build a class-based signature for more structured outputs.

Frequently Asked Questions about dspy-signature-designer

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

FAQPage Schema

How do I define type-safe DSPy signatures for my modules?▼

Type-safe DSPy signatures use InputField and OutputField with Python typing (Literal, Optional, List) to enforce structured input/output validation. Define either inline signatures with dspy.Predict for simple tasks or class-based signatures for complex multi-field relationships, ensuring predictable module interfaces across development and deployment.

What's the difference between inline and class-based DSPy signatures?▼

Inline signatures work with dspy.Predict and dspy.ChainOfThought for straightforward single-task modules. Class-based signatures leverage Python typing and Pydantic models for complex outputs with multiple fields, default values, and metadata-enabled validation across intricate input/output workflows.

Can I use type hints like Optional and List in DSPy signatures?▼

Yes. DSPy signatures support advanced type hints including Literal, Optional, and List to define flexible field constraints. These typing aids ensure consistent outputs and work with both inline templates and class-based signature designs for structured, validated results.

How do I validate outputs in DSPy signatures?▼

DSPy signatures implement metadata-enabled validation through OutputField definitions with type hints and Pydantic models. This approach enforces field constraints, manages default values, and ensures outputs conform to the defined signature structure across development and integration scenarios.

Do I need prior knowledge of Python typing to use DSPy signatures?▼

Basic Python typing knowledge helps but is not required. DSPy signatures guide consistent output design through type hints and Pydantic patterns, supporting both simple inline signatures for beginners and advanced class-based signatures for complex multi-field workflows.

What problems does structured DSPy signature design solve?▼

Type-safe DSPy signatures eliminate ambiguous module interfaces by enforcing InputField and OutputField validation, reducing integration errors, enabling predictable deployments, and supporting complex multi-field relationships across development stages without manual output parsing or validation.