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create-meta-prompts

Generates structured, multi-stage Claude-to-Claude prompts with XML metadata and versioning.

Updated Aug 23, 2026
Search Tags:#automation#Claude#orchestration#metadata#execution#prompts#workflows
One-click install
npx skills add https://github.com/voronerd/claude-orchestration-template --skill create-meta-prompts-voronerd
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: create-meta-prompts
Source: https://github.com/voronerd/claude-orchestration-template/tree/main/template_root/.claude/skills/create-meta-prompts
Command: npx skills add https://github.com/voronerd/claude-orchestration-template --skill create-meta-prompts-voronerd

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Create-meta-prompts automates the generation of structured, multi-stage Claude-to-Claude prompts, enabling teams to plan, execute, and review prompts with clear provenance and XML metadata.

Core Features & Use Cases

  • Teeing middleware integration for anti-pattern checks, agent routing by complexity, cost estimation display, and logging.
  • Chain detection to reference existing research or plan artifacts and ensure proper prompt sequencing.
  • Automated prompt generation using purpose-specific patterns (Do/Plan/Research/Refine) with output to prompts/{number}-{topic}-{purpose}/ and SUMMARY.md creation.
  • Execution engine capable of single, sequential, parallel, or mixed mode runs with completion archiving and result presentation.

Quick Start

Describe your task to let the system generate a multi-stage Claude-to-Claude prompt chain and execute it end-to-end, producing a SUMMARY.md for quick review.

Frequently Asked Questions about create-meta-prompts

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

FAQPage Schema
How do I automate Claude-to-Claude prompt pipelines for multi-stage workflows?▼

Automating Claude-to-Claude prompt pipelines involves generating structured, multi-stage prompts that chain together for sequential or parallel execution. This system analyzes intent, detects existing prompt artifacts, and composes purpose-driven templates with metadata and versioning for every prompt.

What is the best way to orchestrate sequential and parallel prompt execution with Claude?▼

Orchestrating prompt execution with Claude is handled by an execution engine supporting single, sequential, parallel, or mixed mode runs. It manages completion archiving and result presentation, ensuring proper prompt sequencing by detecting existing research or plan artifacts.

How do I structure prompt templates for Do, Plan, Research, and Refine lifecycles?▼

Structuring prompt templates for Do, Plan, Research, and Refine lifecycles requires purpose-specific patterns that output to dedicated per-prompt folders. The system generates a SUMMARY.md for quick review and ensures metadata, versioning, and provenance are attached to every prompt artifact.

Can I integrate middleware for anti-pattern checks and cost estimation in Claude prompt workflows?▼

Integrating middleware for anti-pattern checks, agent routing by complexity, cost estimation display, and logging is supported through teeing middleware integration. This ensures prompt workflows maintain quality control and provide visibility into execution metrics.

Does this prompt orchestration approach support tracking provenance and versioning for generated prompts?▼

Tracking provenance and versioning for generated prompts is a core feature of this orchestration approach. It ensures metadata, versioning, and provenance are maintained for every prompt, allowing teams to review the history and origin of structured prompt artifacts.

What are the limitations of automating multi-stage prompt generation without existing artifacts?▼

Automating multi-stage prompt generation without existing artifacts limits chain detection capabilities, preventing the system from referencing prior research or plan outputs to ensure proper prompt sequencing. The system relies on detecting these artifacts to build effective multi-stage Claude-to-Claude prompt chains.

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