deep-research-prompt-architect

Guide interactive research workflows to structure delegation payloads for prompt-architect.

Updated Feb 25, 2024

Search Tags:#workflow #design #research #prompt-design #prompt-architect #agentic-llm

Ready to run

npx skills add https://github.com/GyroZepelix/.dotfiles --skill deep-research-prompt-architect

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deep-research-prompt-architect
Source: https://github.com/GyroZepelix/.dotfiles/tree/main/02-agentic-llms/.claude/skills/deep-research-prompt-architect
Command: npx skills add https://github.com/GyroZepelix/.dotfiles --skill deep-research-prompt-architect

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This guidance provides an interactive workflow to design or refine skills and commands for agentic coding LLMs. It orchestrates thorough research, structured questioning, and frontmatter preparation, ensuring a deterministic handoff to the prompt-architect for final prompt generation.

Core Features & Use Cases

Interactive analysis that clarifies requirements, surfaces edge cases, and eliminates ambiguity before drafting prompts.
Two-workflow option design that presents distinct implementation paths, with a recommended option identified upfront.
Structured frontmatter planning and generation of a programmatic delegation payload to feed prompt-architect.
End-to-end delegation to prompt-architect in Programmatic mode to produce the final prompt, ready for integration.
Research-driven synthesis that can incorporate web-domain knowledge, codebase examination, and domain APIs as needed.

Quick Start

Describe your desired agentic-LMM skill in detail and I will guide you through the research workflow to draft a final prompt-ready specification.

Frequently Asked Questions about deep-research-prompt-architect

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

FAQPage Schema

How do I design prompts for agentic LLM workflows?▼

To design prompts for agentic LLM workflows, use an interactive, research-driven process that surfaces ambiguities and gathers requirements before structuring a programmatic delegation payload for final generation.

What is the best way to structure requirements before drafting LLM prompts?▼

The best way to structure requirements before drafting LLM prompts is through interactive analysis that clarifies needs, surfaces edge cases, and eliminates ambiguity. This captures thorough frontmatter and workflow decisions for reproducible outputs.

Can I use this approach to improve existing prompt specifications?▼

Yes, you can use this interactive workflow to improve existing prompt specifications. It applies to refining existing prompts by orchestrating research, structured questioning, and frontmatter preparation before final drafting.

Does this prompt design workflow support codebase examination and domain APIs?▼

Yes, this prompt design workflow supports codebase examination and domain APIs. Its research-driven synthesis incorporates web-domain knowledge, codebase examination, and domain APIs as needed to structure the delegation payload.

How do I choose between distinct implementation paths for an LLM skill?▼

To choose between implementation paths for an LLM skill, the workflow presents a two-workflow option design with a recommended option identified upfront, ensuring you select an optimal path before final prompt generation.

When should I not use an interactive research workflow for prompt generation?▼

You should not use an interactive research workflow for prompt generation when your requirements are fully defined with zero ambiguity. This process specifically targets eliminating ambiguity and surfacing edge cases through structured questioning before drafting.