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continual-learning

Implement continual learning with hooks, memory scoping, and reflection patterns.

Updated Aug 23, 2026
Search Tags:#hooks#ai agents#memory#persistence#reflection#continual learning
One-click install
npx skills add https://github.com/MoonAxis/azure-stack --skill continual-learning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: continual-learning
Source: https://github.com/MoonAxis/azure-stack/tree/main/skills/continual-learning
Command: npx skills add https://github.com/MoonAxis/azure-stack --skill continual-learning

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the issue of AI agents forgetting information between sessions, enabling them to retain and apply learned knowledge over time.

Core Features & Use Cases

  • Persistent Memory: Implements a two-tier memory system (global and local) to store learnings.
  • Automated Learning Capture: Hooks automatically capture tool usage patterns and failures to create new learnings.
  • Agent-Native Storage: Allows agents to directly store learnings via SQL commands.
  • Manual Memory Input: Supports human-readable, version-controlled memory files for explicit knowledge.
  • Compaction Mechanism: Automatically prunes old or low-value learnings to manage memory size.
  • Use Case: An AI agent repeatedly makes the same mistake when generating Terraform code. Continual learning captures this pattern, and the agent is prompted with the correct approach in future sessions, preventing recurrence.

Quick Start

Copy the continual-learning hook to your project's hooks directory and initialize the project memory database.

Frequently Asked Questions about continual-learning

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

FAQPage Schema
How do I enable persistent memory for AI agents across different sessions?▼

Persistent memory for AI agents is enabled through a two-tier system storing global and local learnings. This approach retains knowledge across sessions using automated capture hooks, agent-native SQL storage, and manual input files.

What is continual learning in AI coding agents and how does it work?▼

Continual learning in AI coding agents captures tool usage patterns and failures via hooks to prevent recurring mistakes. It implements reflection patterns and a compaction mechanism to prune low-value learnings for efficient knowledge management.

How do I automatically capture AI agent mistakes and prevent them in future sessions?▼

You automatically capture AI agent mistakes by implementing hooks that monitor tool usage patterns and failures. These hooks create new learnings stored in a persistent memory database, prompting the correct approach in future sessions.

Can I use SQL commands to store memory and learnings directly from an AI agent?▼

Yes, you can use SQL commands for agent-native storage of memory and learnings. The system supports direct SQL storage alongside human-readable, version-controlled memory files for explicit manual knowledge input.

Does the AI agent memory system automatically manage storage size over time?▼

Yes, the AI agent memory system automatically manages storage size through a compaction mechanism. This feature prunes old or low-value learnings to maintain efficient knowledge management and prevent database bloat.

What is the best way to scope AI agent memory for global versus local project contexts?▼

The best way to scope AI agent memory is using a two-tier system separating global and local storage. This ensures broad learnings apply universally while local memories target specific project contexts without interference.

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