self-refine

Evaluate and iteratively refine AI-generated plans across five quality dimensions.

Updated Mar 6, 2026
One-click install
npx skills add https://github.com/53able/docs-site --skill self-refine
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: self-refine
Source: https://github.com/53able/docs-site/tree/main/.cursor/skills/self-refine
Command: npx skills add https://github.com/53able/docs-site --skill self-refine

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill ensures the quality and effectiveness of AI-generated plans by having the AI critically evaluate and refine its own proposals before execution.

Core Features & Use Cases

  • Multi-Aspect Evaluation: Assesses plans across five critical dimensions: Purpose Alignment, Impact Scope, Premise Validity, Executability, and Cognitive Load.
  • Iterative Refinement: Automatically revises plans based on evaluation feedback until quality standards are met or a stopping criterion is reached.
  • Use Case: Before executing a complex code generation plan, the AI uses self-refine to check if the code meets the user's requirements, if it has unintended side effects, and if it's the simplest possible solution.

Quick Start

Ask the AI to refine the current plan based on the multi-aspect evaluation criteria.

Frequently Asked Questions about self-refine

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

FAQPage Schema
How do I automate AI plan refinement before code execution?

AI plan refinement is automated by evaluating proposals across five dimensions: purpose alignment, impact scope, premise validity, executability, and cognitive load, then iteratively revising the plan based on feedback until quality standards are met.

What is self-correction in AI-generated plans and when do I need it?

Self-correction in AI-generated plans is an iterative process where the AI critically evaluates its own proposals before execution. You need it to ensure plan quality, verify requirements, check for unintended side effects, and simplify complex solutions.

How to evaluate AI plan quality for executability and cognitive load?

To evaluate AI plan quality, assess executability and cognitive load alongside purpose alignment, impact scope, and premise validity. The plan is automatically refined through a feedback loop until these criteria are satisfied or a stopping point is reached.

Does iterative plan improvement work for complex code generation?

Iterative plan improvement works for complex code generation by checking if the proposed code meets user requirements, evaluating unintended side effects, and ensuring it is the simplest possible solution before execution begins.

What are the limitations of automated AI self-refinement cycles?

Automated AI self-refinement cycles operate until quality is assured or a defined stopping point is met. While they evaluate multiple aspects like premise validity and executability, the process depends entirely on the AI's ability to critically assess its own output.