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AlexanderRadevich
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self-correction-loop

Detects user corrections, generalizes them into rules, and stores them in MEMORY.md.

Updated Aug 23, 2026
Search Tags:#prompt engineering#memory management#knowledge capture#ai learning#self-correction#rule generalization
One-click install
npx skills add https://github.com/AlexanderRadevich/SportowyHub_clientApp --skill self-correction-loop-alexanderradevich
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: self-correction-loop
Source: https://github.com/AlexanderRadevich/SportowyHub_clientApp/tree/main/.claude/skills/self-correction-loop
Command: npx skills add https://github.com/AlexanderRadevich/SportowyHub_clientApp --skill self-correction-loop-alexanderradevich

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the issue of AI models repeatedly making the same errors by establishing a robust system for capturing, generalizing, and storing user corrections as reusable knowledge.

Core Features & Use Cases

  • Correction Capture: Automatically detects and acknowledges user corrections.
  • Generalization: Transforms specific feedback into broad, class-level rules.
  • Memory Management: Stores generalized rules in MEMORY.md, categorized for retrieval and deduplicated.
  • Proactive Application: Reviews stored memory at the start of each session to prevent recurring mistakes.
  • Use Case: If a user corrects the AI for using DateTime.Now instead of TimeProvider in a specific instance, the AI will generalize this to a project-wide rule: "Always use TimeProvider instead of DateTime.Now/UtcNow across all modules."

Quick Start

When a user corrects the AI's output, acknowledge the correction, generalize the lesson, and store it as a reusable rule in MEMORY.md.

Frequently Asked Questions about self-correction-loop

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

FAQPage Schema
How do I stop AI from making the same mistakes repeatedly in my projects?▼

A self-improving correction capture system stops AI from making repeated mistakes by generalizing user feedback into broad rules and storing them in MEMORY.md for future retrieval.

How does AI memory management generalize specific corrections into reusable rules?▼

AI memory management generalizes corrections by transforming specific user feedback into class-level rules, categorizing them for retrieval, and aggressively deduplicating entries before storing them in MEMORY.md.

What is the best way to capture prompt engineering feedback for AI learning?▼

The best way to capture prompt engineering feedback is to acknowledge user corrections during a session, generalize the specific feedback into broad rules, and save them in MEMORY.md to reduce future mistake rates.

Can I use MEMORY.md to store and deduplicate generalized rules across sessions?▼

Yes, you can use MEMORY.md to store generalized rules. The system categorizes rules for retrieval and deduplicates aggressively, loading existing memory at the start of each session to apply knowledge proactively.

Does the self-correction loop require specific dependencies to manage AI knowledge capture?▼

No dependencies are required to implement this AI knowledge capture and self-correction system. It operates independently by detecting corrections, generalizing lessons, and managing memory within the session environment.

When should I not use a self-improving correction capture system for AI memory management?▼

You should not use this self-improving correction capture system if your workflow lacks a persistent MEMORY.md file structure, as it relies on loading and storing generalized rules at the start and during active sessions.

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