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reflection-loop

Apply a self-critique loop to identify and fix mistakes in AI task outputs.

Updated Apr 26, 2026
Search Tags:#reflection#quality-assurance#code-review#loop#self-critique#error-detection#ai-improvement
One-click install
npx skills add https://github.com/kinnerkarmanish/mak --skill reflection-loop-kinnerkarmanish
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: reflection-loop
Source: https://github.com/kinnerkarmanish/mak/tree/main/library/skills/ai-patterns/reflection
Command: npx skills add https://github.com/kinnerkarmanish/mak --skill reflection-loop-kinnerkarmanish

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates structured self-critique and revision to improve AI task outputs and reduce errors.

Core Features & Use Cases

  • Self-critique: generate a targeted critique of the initial solution to surface flaws.
  • Issue tracking & revision: categorize issues and apply fixes iteratively.
  • Final verification: re-check criteria and guard against regressions before completion.

Quick Start

Trigger the reflection loop after generating an initial result to automatically critique, revise, and verify quality.

Frequently Asked Questions about reflection-loop

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

FAQPage Schema
How does a self-critique loop improve AI task outputs?▼

A self-critique loop improves AI task outputs by applying explicit critique steps to identify flaws, tracking issues categorically, and applying iterative revisions to fix mistakes before final verification.

How do I automate code review and error detection for iterative reasoning tasks?▼

Automate code review and error detection by triggering a reflection loop after generating an initial result, which automatically critiques, revises, and verifies quality against requirement checks.

Can I use self-critique workflows for quality-sensitive software deployments?▼

Self-critique workflows are applicable to quality-sensitive software deployments, using issue tracking and final verification to guard against regressions and ensure complex analyses meet criteria.

What is the best way to apply iterative revisions to complex data analyses?▼

The best way to apply iterative revisions to complex data analyses is using an automated reflection loop that surfaces flaws, categorizes issues, and applies targeted fixes to reduce errors.

Do I need any external dependencies to run an AI self-critique loop?▼

No external dependencies are required to run an AI self-critique loop, as the implementation operates independently to generate critiques, apply revisions, and execute final verification.

When should I not use an automated reflection loop for AI outputs?▼

You should avoid using an automated reflection loop when initial results require immediate deployment without the latency of iterative issue tracking, revision application, and regression verification.

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