skills.rest
yunmoxinghe
yunmoxingheCOMMUNITY
Explore all skills by this author →
GitHub

continual-learning

Capture and retain tool usage patterns and user corrections across coding sessions.

2|Updated Jan 23, 2026
Search Tags:#coding conventions#continual learning#memory retention#AI coding agents#knowledge enhancement
One-click install
npx skills add https://github.com/yunmoxinghe/Docked-AI --skill continual-learning-yunmoxinghe
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: continual-learning
Source: https://github.com/yunmoxinghe/Docked-AI/tree/main/.kiro/skills/continual-learning
Command: npx skills add https://github.com/yunmoxinghe/Docked-AI --skill continual-learning-yunmoxinghe

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the issue of AI coding agents forgetting learned patterns and information between sessions, providing a guide for implementing continual learning to retain knowledge over time.

Core Features & Use Cases

  • Memory Scoping: Utilizes two-tier memory for global and local learnings.
  • Learning Storage: Automatically captures and stores tool outcomes and user corrections.
  • Learning Decay & Compaction: Ensures that only relevant learnings persist, optimizing for performance and resource management.
  • Quick Start: Offers an easy installation process and immediate initialization on first use.
  • Use Case: Ideal for setting up learning infrastructure for agents working across various projects, enabling them to remember and apply past learnings.

Quick Start

Copy the hook to your GitHub repository: cp -r hooks/continual-learning .github/hooks/

Frequently Asked Questions about continual-learning

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

FAQPage Schema
How do I stop my AI coding agent from forgetting coding conventions between sessions?▼

To stop AI coding agents from forgetting coding conventions between sessions, you need a continual learning mechanism that captures and retains tool usage patterns and user corrections across multiple sessions.

What is the best way to implement long-term memory retention for AI coding agents?▼

The best way to implement long-term memory retention for AI coding agents is using a two-tier memory scope that stores global and local learnings, automatically capturing tool outcomes and user corrections.

Can I use GitHub hooks to set up continual learning for my coding agent?▼

Yes, you can set up continual learning for coding agents by copying the provided hook into your GitHub repository's hooks directory, which initializes the learning infrastructure on first use.

Does continual learning for AI agents support memory compaction to optimize performance?▼

Continual learning for AI agents supports memory decay and compaction, ensuring that only relevant learnings persist over time to optimize long-term memory performance and resource management.

Why does my AI coding agent fail to apply past user corrections in new projects?▼

AI coding agents fail to apply past user corrections in new projects without an infrastructure for learning storage that automatically captures, retains, and scopes user corrections across multiple sessions.

Related Skills

Explore More Software Engineering Skills→
davidrrowleydavidrrowley

continual-learning

Capture and persist project-specific conventions and patterns across coding sessions.

Community
Advanced
RemseyMailjardRemseyMailjard

continual-learning

Store patterns, conventions, and learnings across AI coding sessions.

Community
Intermediate
pskoettpskoett
273

self-improvement

Log errors, corrections, and knowledge gaps during AI agent execution.

Community
Advanced
involvexinvolvex

continual-learning

Store and manage AI agent learnings across global and local databases.

Community
Advanced