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resonant-coding

Structure iterative AI prompt workflows with role-based Investigator, Strategist, and Executor steps.

Updated Aug 23, 2026
Search Tags:#workflow#planning#collaboration#llm#quality#execution#prompt-engineering
One-click install
npx skills add https://github.com/pqem/agent-automatizado --skill resonant-coding
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: resonant-coding
Source: https://github.com/pqem/agent-automatizado/tree/main/skills/resonant-coding
Command: npx skills add https://github.com/pqem/agent-automatizado --skill resonant-coding

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This methodology helps teams structure iterative prompts and role-based workflows to prevent chaotic, unproductive AI interactions.

Core Features & Use Cases

  • Provides a disciplined 5-filter process (Draft, Correction, Clarity, Edge cases, Excellence) to guide prompt refinement.
  • Splits work into three expert roles: Investigator, Strategist, and Executor, enabling focused collaboration.
  • Supports modular execution with clear, separate conversations for research, planning, and implementation.
  • Applies to complex AI tasks like research synthesis, plan generation, and guided code/documentation generation.

Quick Start

Begin a focused research conversation and follow the Resonant Coding workflow to plan, execute, and refine your prompts for reliable AI outcomes.

Frequently Asked Questions about resonant-coding

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

FAQPage Schema
How do I structure chaotic LLM prompts for complex coding tasks?▼

Structure chaotic LLM prompts by applying a disciplined five-filter process—Draft, Correction, Clarity, Edge cases, and Excellence—to iteratively refine inputs and enforce quality across coding and research workflows.

What is role-based prompt engineering and how does it improve AI collaboration?▼

Role-based prompt engineering splits AI work into three expert roles: Investigator, Strategist, and Executor. This division enables focused collaboration by separating research, planning, and implementation into distinct conversations for reliable outcomes.

How do I plan and execute iterative AI workflows without losing context?▼

Plan and execute iterative AI workflows by maintaining modular, separate conversations for research, planning, and implementation. This structured approach prevents unproductive interactions and ensures context remains clear throughout complex tasks.

Can I use this prompt refinement methodology for research synthesis and documentation?▼

Yes, this methodology applies to complex AI tasks including research synthesis, plan generation, and guided code or documentation generation, ensuring structured execution and refined prompts across diverse scenarios.

When should I use a multi-step prompt workflow instead of direct LLM prompting?▼

Use a multi-step prompt workflow when facing complex AI-driven work that risks chaotic interactions. The enforced process with distinct roles and frontmatter guidance ensures quality and safety that direct prompting cannot guarantee.

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