create-meta-prompts

Generate structured prompts for Claude-to-Claude pipelines with dependency detection.

Updated Aug 6, 2025

Search Tags:#workflow automation #prompt engineering #code generation #ai development #meta-prompting #llm orchestration

Ready to run

npx skills add https://github.com/flosrn/.claude --skill create-meta-prompts-flosrn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: create-meta-prompts
Source: https://github.com/flosrn/.claude/tree/main/skills/create-meta-prompts
Command: npx skills add https://github.com/flosrn/.claude --skill create-meta-prompts-flosrn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the creation of prompts designed for multi-stage AI workflows, ensuring structured outputs and clear dependency management between prompts.

Core Features & Use Cases

Prompt Generation: Creates prompts optimized for research, planning, or execution stages.
Dependency Detection: Automatically identifies and references existing research or plan files.
Structured Outputs: Generates prompts that produce outputs with XML metadata for easy parsing by subsequent AI agents.
Use Case: When building a complex feature, use this Skill to first generate a research prompt, then a planning prompt that references the research, and finally an implementation prompt that references the plan, creating a seamless AI-driven development pipeline.

Quick Start

Use the create-meta-prompts skill to generate a prompt for researching authentication options for a new web application.

Frequently Asked Questions about create-meta-prompts

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

FAQPage Schema

How do I build multi-stage AI workflows with structured prompts?▼

Structured prompt workflows for Claude-to-Claude pipelines are built by generating purpose-specific prompts for research, planning, and execution stages, which automatically detect dependencies and produce XML metadata for parsing by subsequent LLM agents.

What is meta-prompting for LLM orchestration?▼

Meta-prompting for LLM orchestration is the process of generating optimized prompts that produce structured outputs and clear dependency management, ensuring seamless context transfer between research, planning, and execution stages of AI pipelines.

How do I generate prompts for a Claude-to-Claude pipeline?▼

To generate prompts for a Claude-to-Claude pipeline, use a meta-prompting skill to create purpose-specific structures for research, planning, and execution, saving them to a dedicated .prompts directory for clear provenance and chain detection.

Does AI workflow automation support dependency detection between prompts?▼

AI workflow automation supports dependency detection by automatically identifying and referencing existing research or plan files when generating new prompts, creating a seamless chain for AI-driven development pipelines.

Can I use structured outputs and XML metadata for AI code generation?▼

You can use structured outputs and XML metadata for AI code generation by generating execution prompts that reference prior planning files, allowing subsequent AI agents to easily parse dependencies and structured outputs.

When do I need a dedicated .prompts directory for workflow automation?▼

You need a dedicated .prompts directory for workflow automation when managing multi-stage AI pipelines, as it saves generated prompts with clear provenance and facilitates chain detection across research, planning, and execution stages.