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prompt-caller

Manage and invoke AI prompts with YAML frontmatter and Jinja templating.

2|Updated Sep 9, 2024
Search Tags:#prompt engineering#langchain#agent development#openai#structured output#ai prompts
One-click install
npx skills add https://github.com/ThiNepo/prompt-caller --skill prompt-caller
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-caller
Source: https://github.com/ThiNepo/prompt-caller/tree/main/prompt_caller/skills/prompt-caller
Command: npx skills add https://github.com/ThiNepo/prompt-caller --skill prompt-caller

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the creation, management, and invocation of AI prompts, ensuring consistency and enabling agents to interact with AI models effectively.

Core Features & Use Cases

  • Prompt Definition: Define prompts using a structured format with YAML frontmatter and Jinja templating.
  • Structured Output: Generate structured outputs based on defined schemas, facilitating data processing.
  • Agent Integration: Supports tool-enabled agent workflows for complex task execution.
  • Use Case: Develop a consistent customer support chatbot by defining all interaction prompts with PromptCaller, ensuring standardized responses and structured data capture.

Quick Start

Use the prompt-caller skill to create a new prompt file named 'customer-greeting'.

Frequently Asked Questions about prompt-caller

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

FAQPage Schema
How do I manage and invoke AI prompts with structured output?▼

You manage and invoke AI prompts using a structured format with YAML frontmatter, JSX-like blocks, and Jinja templating. This approach supports defining output schemas with a DSL to generate structured data for consistent processing.

What's the best way to define structured output schemas for AI prompts?▼

Defining structured output schemas for AI prompts is best handled using a dedicated DSL within your prompt files. This allows you to specify exact data structures alongside Jinja templating for reliable, structured data generation from the model.

Can I use prompt templating with tool-enabled agents for task execution?▼

Yes, prompt templating works with tool-enabled agents to support complex task workflows. By defining prompts with structured formats and schemas, agents can interact with AI models effectively and execute multi-step tasks reliably.

How do I create a consistent customer support chatbot using prompt engineering?▼

To create a consistent customer support chatbot, define all interaction prompts using structured YAML frontmatter and Jinja templating. This ensures standardized responses and enables structured data capture during customer interactions.

Do I need an OpenAI API key to generate structured data from AI prompts?▼

Yes, an OpenAI API key is required to invoke the AI models for generating structured data. The prompt management system relies on this key to process your structured templates and return schema-compliant outputs.

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