About
This skill introduces DSPy Signatures for defining structured input/output templates for LLM tasks. It enables both inline and class-based signatures to create reusable, self-documenting prompts with typed fields and descriptions. Use this to standardize and validate LLM interactions within Claude Code workflows.
Quick Install
Claude Code
Recommendednpx skills add vamseeachanta/workspace-hub -a claude-code/plugin add https://github.com/vamseeachanta/workspace-hubgit clone https://github.com/vamseeachanta/workspace-hub.git ~/.claude/skills/dspy-1-signaturesCopy and paste this command in Claude Code to install this skill
GitHub Repository
Frequently asked questions
What is the dspy-1-signatures skill?
dspy-1-signatures is a Claude Skill by vamseeachanta. Skills package instructions and resources that Claude loads on demand, so Claude can perform dspy-1-signatures-related tasks without extra prompting.
How do I install dspy-1-signatures?
Use the install commands on this page: add dspy-1-signatures to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does dspy-1-signatures belong to?
dspy-1-signatures is in the ai-prompting category, tagged general.
Is dspy-1-signatures free to use?
Yes. dspy-1-signatures is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
Related Skills
This skill enables version control and management for AI prompts, allowing developers to track changes, compare iterations, and maintain prompt history. It provides tools to create versioned prompt templates with parameters like style and length constraints. Use this when you need reproducible, auditable prompt workflows across different model versions or team collaborations.
This skill provides best practices for versioning AI prompts using semantic versioning and structured metadata. It helps developers track prompt changes, maintain changelogs, and organize different prompt versions systematically. Use this when implementing version control for production prompts in AI applications.
Agenta is a self-hosted platform for managing and evaluating LLM prompts. It enables developers to version prompts, run A/B tests, and track experiments with evaluation metrics. Use it to systematically test and deploy prompt changes with confidence.
pandasai enables conversational data analysis by letting developers query pandas DataFrames using natural language. It supports chart generation, transformation explanations, and multi-table analysis, powered by various LLM backends. Use this skill to quickly build exploratory data interfaces or ask plain-English questions about your datasets.