rarv-cycle

Execute a Reason-Act-Reflect-Verify loop for autonomous task execution.

Updated Feb 25, 2026
One-click install
npx skills add https://github.com/LayerDynamics/Lore --skill rarv-cycle
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: rarv-cycle
Source: https://github.com/LayerDynamics/Lore/tree/main/lore/skills/rarv-cycle
Command: npx skills add https://github.com/LayerDynamics/Lore --skill rarv-cycle

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides a structured, four-step execution loop (Reason, Act, Reflect, Verify) to ensure that autonomous or semi-autonomous tasks are completed reliably, with built-in learning and error handling.

Core Features & Use Cases

  • Structured Workflow: Enforces a strict Reason-Act-Reflect-Verify cycle for every action.
  • Error Handling & Learning: Captures errors, learns from failures, and retries or escalates appropriately.
  • Use Case: When an AI agent is tasked with refactoring a complex module, the RARV cycle ensures each refactoring step is reasoned about, acted upon, reflected upon for correctness, and verified with tests before proceeding.

Quick Start

Use the rarv-cycle skill to execute the task of refactoring the authentication module.

Frequently Asked Questions about rarv-cycle

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

FAQPage Schema
What is a Reason Act Reflect Verify autonomous execution loop?▼

A Reason Act Reflect Verify autonomous execution loop is a deterministic workflow that ensures every AI agent action is preceded by planning and followed by evaluation and validation, enabling robust self-correction.

How do I add error handling and learning to an autonomous agent workflow?▼

You can add error handling to an autonomous agent workflow by enforcing a strict execution cycle that captures errors, learns from failures, and automatically retries or escalates tasks appropriately.

What's the best way to ensure an AI agent reliably refactors complex modules?▼

The best way to reliably refactor complex modules is applying a structured execution loop that verifies each refactoring step with tests before proceeding, ensuring correctness through reflection and validation.

Can I use this structured workflow for semi-autonomous task execution?▼

Yes, you can use this structured workflow for semi-autonomous task execution because it enforces a strict Reason Act Reflect Verify cycle that manages tasks with built-in learning and error handling.

Why does my autonomous AI agent fail to self-correct during complex problem solving?▼

Autonomous AI agents fail to self-correct during complex problem solving when they lack a deterministic execution loop to reflect on actions and verify results before proceeding to the next step.